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  1. ARM - Field Campaign - Arctic Cloud Infrared Imaging

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    govCampaignsArctic Cloud Infrared Imaging Campaign Links Field Campaign Report ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Campaign : Arctic Cloud Infrared Imaging 2012.07.16 - 2014.07.31 Lead Scientist : Joseph Shaw For data sets, see below. Abstract The 3rd-generation Infrared Cloud Imager (ICI) instrument was deployed close to the Great White facility at the North Slope of Alaska site and operated as

  2. Liquid Water the Key to Arctic Cloud Radiative Closure

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Water the Key to Arctic Cloud Radiative Closure For original submission and image(s), see ARM Research Highlights http:www.arm.govsciencehighlights Research Highlight...

  3. ARM - Field Campaign - FIRE-Arctic Cloud Experiment/SHEBA

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    govCampaignsFIRE-Arctic Cloud Experiment/SHEBA ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Campaign : FIRE-Arctic Cloud Experiment/SHEBA 1998.05.19 - 1998.06.24 Lead Scientist : Peter Hobbs Data Availability Data from the UW Convair-580 measurements in FIRE-ACE/SHEBA have been archived at the Langley DAAC. For data sets, see below. Abstract Based in Barrow, Alaska, from May 15 through June 24, 1998, the Univ. of

  4. Characterizing Arctic Mixed-phase Cloud Structure

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    have two distinguished cloud base heights (CBHs) that can be defined by both ceilometer (black dots) and micropulse lidar (MPL; pink dots) measurements (Figure 1). For a...

  5. Sensitivity of CAM5-Simulated Arctic Clouds and Radiation to Ice Nucleation Parameterization

    SciTech Connect (OSTI)

    Xie, Shaocheng; Liu, Xiaohong; Zhao, Chuanfeng; Zhang, Yuying

    2013-08-01

    Sensitivity of Arctic clouds and radiation in the Community Atmospheric Model version 5 to the ice nucleation process is examined by testing a new physically based ice nucleation scheme that links the variation of ice nuclei (IN) number concentration to aerosol properties. The default scheme parameterizes the IN concentration simply as a function of ice supersaturation. The new scheme leads to a significant reduction in simulated IN number concentrations at all latitudes while changes in cloud amount and cloud properties are mainly seen in high latitudes and middle latitude storm tracks. In the Arctic, there is a considerable increase in mid-level clouds and a decrease in low clouds, which result from the complex interaction among the cloud macrophysics, microphysics, and the large-scale environment. The smaller IN concentrations result in an increase in liquid water path and a decrease in ice water path due to the slow-down of the Bergeron-Findeisen process in mixed-phase clouds. Overall, there is an increase in the optical depth of Arctic clouds, which leads to a stronger cloud radiative forcing (net cooling) at the top of the atmosphere. The comparison with satellite data shows that the new scheme slightly improves low cloud simulations over most of the Arctic, but produces too many mid-level clouds. Considerable improvements are seen in the simulated low clouds and their properties when compared to Arctic ground-based measurements. Issues with the observations and the model-observation comparison in the Arctic region are discussed.

  6. Downloaded 09 Feb 2007 to 128.104.165.60. Redistribution subject to AIP license

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Downloaded

  7. Session Papers North Slope of Alaska and Adjacent Arctic Ocean Cloud

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Session Papers North Slope of Alaska and Adjacent Arctic Ocean Cloud and Radiation Testbed: Science and Siting Strategies B. D. Zak Sandia National Laboratories Albuquerque, New Mexico K. Stamnes University of Alaska Fairbanks, Alaska Introduction This paper serves as a summary of the current thinking regarding the development of the Atmospheric Radiation Measurement (ARM) Program's North Slope of Alaska and adjacent Arctic Ocean (NSA/AAO) Cloud and Radiation Testbed (CART) site. Ellingson et

  8. Microphysical Properties of Single and Mixed-Phase Arctic Clouds Derived from AERI Observations

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Microphysical Properties of Single and Mixed-Phase Arctic Clouds Derived from AERI Observations D. D. Turner University of Wisconsin-Madison Madison, Wisconsin and Pacific Northwest National Laboratory Richland, Washington Abstract A novel new approach to retrieve cloud microphysical properties from mixed-phase clouds is presented. This algorithm retrieves cloud optical depth, ice fraction, and the effective size of the water and ice particles from ground-based, high-resolution infrared radiance

  9. Downloads Feed

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Power http:www.anl.govdownloadstiny-particles-big-magnetic-power Magnetic nanofibers have wide-ranging applications October 6, 2015 Downloads Feed Harnessing Light in...

  10. Towards a Characterization of Arctic Mixed-Phase Clouds

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    manual classification of cloud phase. Using collocated cloud radar and depolarization lidar observations, it is shown that mixed-phase conditions have a high correlation with a...

  11. Aircraft-measured indirect cloud effects from biomass burning smoke in the Arctic and subarctic

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Zamora, L. M.; Kahn, R. A.; Cubison, M. J.; Diskin, G. S.; Jimenez, J. L.; Kondo, Y.; McFarquhar, G. M.; Nenes, A.; Thornhill, K. L.; Wisthaler, A.; et al

    2016-01-21

    The incidence of wildfires in the Arctic and subarctic is increasing; in boreal North America, for example, the burned area is expected to increase by 200–300% over the next 50–100 years, which previous studies suggest could have a large effect on cloud microphysics, lifetime, albedo, and precipitation. However, the interactions between smoke particles and clouds remain poorly quantified due to confounding meteorological influences and remote sensing limitations. Here, we use data from several aircraft campaigns in the Arctic and subarctic to explore cloud microphysics in liquid-phase clouds influenced by biomass burning. Median cloud droplet radii in smoky clouds were ~40–60% smallermore » than in background clouds. Based on the relationship between cloud droplet number (Nliq) and various biomass burning tracers (BBt) across the multi-campaign data set, we calculated the magnitude of subarctic and Arctic smoke aerosol–cloud interactions (ACIs, where ACI = (1/3) × dln(Nliq)/dln(BBt)) to be ~0.16 out of a maximum possible value of 0.33 that would be obtained if all aerosols were to nucleate cloud droplets. Interestingly, in a separate subarctic case study with low liquid water content (~0.02gm–3) and very high aerosol concentrations (2000–3000 cm–3) in the most polluted clouds, the estimated ACI value was only 0.05. In this case, competition for water vapor by the high concentration of cloud condensation nuclei (CCN) strongly limited the formation of droplets and reduced the cloud albedo effect, which highlights the importance of cloud feedbacks across scales. Using our calculated ACI values, we estimate that the smoke-driven cloud albedo effect may decrease local summertime short-wave radiative flux by between 2 and 4 Wm–2 or more under some low and homogeneous cloud cover conditions in the subarctic, although the changes should be smaller in high surface albedo regions of the Arctic. Furthermore, we lastly explore evidence suggesting that

  12. Arctic Mixed-Phase Cloud Properties from AERI Lidar Observations: Algorithm and Results from SHEBA

    SciTech Connect (OSTI)

    Turner, David D.

    2005-04-01

    A new approach to retrieve microphysical properties from mixed-phase Arctic clouds is presented. This mixed-phase cloud property retrieval algorithm (MIXCRA) retrieves cloud optical depth, ice fraction, and the effective radius of the water and ice particles from ground-based, high-resolution infrared radiance and lidar cloud boundary observations. The theoretical basis for this technique is that the absorption coefficient of ice is greater than that of liquid water from 10 to 13 ?m, whereas liquid water is more absorbing than ice from 16 to 25 ?m. MIXCRA retrievals are only valid for optically thin (?visible < 6) single-layer clouds when the precipitable water vapor is less than 1 cm. MIXCRA was applied to the Atmospheric Emitted Radiance Interferometer (AERI) data that were collected during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment from November 1997 to May 1998, where 63% of all of the cloudy scenes above the SHEBA site met this specification. The retrieval determined that approximately 48% of these clouds were mixed phase and that a significant number of clouds (during all 7 months) contained liquid water, even for cloud temperatures as low as 240 K. The retrieved distributions of effective radii for water and ice particles in single-phase clouds are shown to be different than the effective radii in mixed-phase clouds.

  13. Humidity trends imply increased sensitivity to clouds in a warming Arctic

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Cox, Christopher J.; Walden, Von P.; Rowe, Penny M.; Shupe, Matthew D.

    2015-12-10

    Infrared radiative processes are implicated in Arctic warming and sea-ice decline. The infrared cloud radiative effect (CRE) at the surface is modulated by cloud properties; however, CRE also depends on humidity because clouds emit at wavelengths that are semi-transparent to greenhouse gases, most notably water vapour. Here we show how temperature and humidity control CRE through competing influences between the mid- and far-infrared. At constant relative humidity, CRE does not decrease with increasing temperature/absolute humidity as expected, but rather is found to be approximately constant for temperatures characteristic of the Arctic. This stability is disrupted if relative humidity varies. Ourmore » findings explain observed seasonal and regional variability in Arctic CRE of order 10Wm 2. With the physical properties of Arctic clouds held constant, we calculate recent increases in CRE of 1–5Wm 2 in autumn and winter, which are projected to reach 5–15Wm 2 by 2050, implying increased sensitivity of the surface to clouds.« less

  14. Humidity trends imply increased sensitivity to clouds in a warming Arctic

    SciTech Connect (OSTI)

    Cox, Christopher J.; Walden, Von P.; Rowe, Penny M.; Shupe, Matthew D.

    2015-12-10

    Infrared radiative processes are implicated in Arctic warming and sea-ice decline. The infrared cloud radiative effect (CRE) at the surface is modulated by cloud properties; however, CRE also depends on humidity because clouds emit at wavelengths that are semi-transparent to greenhouse gases, most notably water vapour. Here we show how temperature and humidity control CRE through competing influences between the mid- and far-infrared. At constant relative humidity, CRE does not decrease with increasing temperature/absolute humidity as expected, but rather is found to be approximately constant for temperatures characteristic of the Arctic. This stability is disrupted if relative humidity varies. Our findings explain observed seasonal and regional variability in Arctic CRE of order 10Wm 2. With the physical properties of Arctic clouds held constant, we calculate recent increases in CRE of 1–5Wm 2 in autumn and winter, which are projected to reach 5–15Wm 2 by 2050, implying increased sensitivity of the surface to clouds.

  15. Microphysical Properties of Single and Mixed-Phase Arctic Clouds Derived from AERI Observations

    SciTech Connect (OSTI)

    Turner, David D.

    2003-06-01

    A novel new approach to retrieve cloud microphysical properties from mixed-phase clouds is presented. This algorithm retrieves cloud optical depth, ice fraction, and the effective size of the water and ice particles from ground-based, high-resolution infrared radiance observations. The theoretical basis is that the absorption coefficient of ice is stronger than that of liquid water from 10-13 mm, whereas liquid water is more absorbing than ice from 16-25 um. However, due to strong absorption in the rotational water vapor absorption band, the 16-25 um spectral region becomes opaque for significant water vapor burdens (i.e., for precipitable water vapor amounts over approximately 1 cm). The Arctic is characterized by its dry and cold atmosphere, as well as a preponderance of mixed-phase clouds, and thus this approach is applicable to Arctic clouds. Since this approach uses infrared observations, cloud properties are retrieved at night and during the long polar wintertime period. The analysis of the cloud properties retrieved during a 7 month period during the Surface Heat Budget of the Arctic (SHEBA) experiment demonstrates many interesting features. These results show a dependence of the optical depth on cloud phase, differences in the mode radius of the water droplets in liquid-only and mid-phase clouds, a lack of temperature dependence in the ice fraction for temperatures above 240 K, seasonal trends in the optical depth with the clouds being thinner in winter and becoming more optically thick in the late spring, and a seasonal trend in the effective size of the water droplets in liquid-only and mixed-phase clouds that is most likely related to aerosol concentration.

  16. Process-model Simulations of Cloud Albedo Enhancement by Aerosols in the Arctic

    SciTech Connect (OSTI)

    Kravitz, Benjamin S.; Wang, Hailong; Rasch, Philip J.; Morrison, H.; Solomon, Amy

    2014-11-17

    A cloud-resolving model is used to simulate the effectiveness of Arctic marine cloud brightening via injection of cloud condensation nuclei (CCN). An updated cloud microphysical scheme is employed, with prognostic CCN and cloud particle numbers in both liquid and mixed-phase marine low clouds. Injection of CCN into the marine boundary layer can delay the collapse of the boundary layer and increase low-cloud albedo. Because nearly all of the albedo effects are in the liquid phase due to the removal of ice water by snowfall when ice processes are involved, albedo increases are stronger for pure liquid clouds than mixed-phase clouds. Liquid precipitation can be suppressed by CCN injection, whereas ice precipitation (snow) is affected less; thus the effectiveness of brightening mixed-phase clouds is lower than for liquid-only clouds. CCN injection into a clean regime results in a greater albedo increase than injection into a polluted regime, consistent with current knowledge about aerosol-cloud interactions. Unlike previous studies investigating warm clouds, dynamical changes in circulation due to precipitation changes are small.

  17. Distribution and Validation of Cloud Cover Derived from AVHRR Data Over the Arctic Ocean During the SHEBA Year

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    and Validation of Cloud Cover Derived from AVHRR Data Over the Arctic Ocean During the SHEBA Year P. Minnis National Aeronautics and Space Administration Langley Research Center Hampton, Virginia D. A. Spangenberg and V. Chakrapani Analytical Services and Materials, Inc. Hampton, Virginia Introduction Determination of cloud radiation interactions over large areas of the Arctic is possible only with the use of data from polar orbiting satellites. Cloud detection using satellite data is difficult

  18. Nighttime Cloud Detection Over the Arctic Using AVHRR Data

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... Table 3. SHEBA domain cloud statistics from the polar cloud mask for January-March 1998. ... Earth Radiation Budget Experiment (ERBE) and NOAA-9 AVHRR data from 1986 were matched to ...

  19. Indirect and Semi-Direct Aerosol Campaign: The Impact of Arctic Aerosols on Clouds

    SciTech Connect (OSTI)

    McFarquhar, Greg; Ghan, Steven J.; Verlinde, J.; Korolev, Alexei; Strapp, J. Walter; Schmid, Beat; Tomlinson, Jason M.; Wolde, Mengistu; Brooks, Sarah D.; Cziczo, Daniel J.; Dubey, Manvendra K.; Fan, Jiwen; Flynn, Connor J.; Gultepe, Ismail; Hubbe, John M.; Gilles, Mary K.; Laskin, Alexander; Lawson, Paul; Leaitch, W. R.; Liu, Peter S.; Liu, Xiaohong; Lubin, Dan; Mazzoleni, Claudio; Macdonald, A. M.; Moffet, Ryan C.; Morrison, H.; Ovchinnikov, Mikhail; Shupe, Matthew D.; Turner, David D.; Xie, Shaocheng; Zelenyuk, Alla; Bae, Kenny; Freer, Matthew; Glen, Andrew

    2011-02-01

    A comprehensive dataset of microphysical and radiative properties of aerosols and clouds in the arctic boundary layer in the vicinity of Barrow, Alaska was collected in April 2008 during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) sponsored by the Department of Energy Atmospheric Radiation Measurement (ARM) and Atmospheric Science Programs. The primary aim of ISDAC was to examine indirect effects of aerosols on clouds that contain both liquid and ice water. The experiment utilized the ARM permanent observational facilities at the North Slope of Alaska (NSA) in Barrow. These include a cloud radar, a polarized micropulse lidar, and an atmospheric emitted radiance interferometer as well as instruments specially deployed for ISDAC measuring aerosol, ice fog, precipitation and spectral shortwave radiation. The National Research Council of Canada Convair-580 flew 27 sorties during ISDAC, collecting data using an unprecedented 42 cloud and aerosol instruments for more than 100 hours on 12 different days. Data were obtained above, below and within single-layer stratus on 8 April and 26 April 2008. These data enable a process-oriented understanding of how aerosols affect the microphysical and radiative properties of arctic clouds influenced by different surface conditions. Observations acquired on a heavily polluted day, 19 April 2008, are enhancing this understanding. Data acquired in cirrus on transit flights between Fairbanks and Barrow are improving our understanding of the performance of cloud probes in ice. Ultimately the ISDAC data will be used to improve the representation of cloud and aerosol processes in models covering a variety of spatial and temporal scales, and to determine the extent to which long-term surface-based measurements can provide retrievals of aerosols, clouds, precipitation and radiative heating in the Arctic.

  20. Deriving Arctic Cloud Microphysics at Barrow, Alaska. Algorithms, Results, and Radiative Closure

    SciTech Connect (OSTI)

    Shupe, Matthew D.; Turner, David D.; Zwink, Alexander; Thieman, Mandana M.; Mlawer, Eli J.; Shippert, Timothy

    2015-07-01

    Cloud phase and microphysical properties control the radiative effects of clouds in the climate system and are therefore crucial to characterize in a variety of conditions and locations. An Arctic-specific, ground-based, multi-sensor cloud retrieval system is described here and applied to two years of observations from Barrow, Alaska. Over these two years, clouds occurred 75% of the time, with cloud ice and liquid each occurring nearly 60% of the time. Liquid water occurred at least 25% of the time even in the winter, and existed up to heights of 8 km. The vertically integrated mass of liquid was typically larger than that of ice. While it is generally difficult to evaluate the overall uncertainty of a comprehensive cloud retrieval system of this type, radiative flux closure analyses were performed where flux calculations using the derived microphysical properties were compared to measurements at the surface and top-of-atmosphere. Radiative closure biases were generally smaller for cloudy scenes relative to clear skies, while the variability of flux closure results was only moderately larger than under clear skies. The best closure at the surface was obtained for liquid-containing clouds. Radiative closure results were compared to those based on a similar, yet simpler, cloud retrieval system. These comparisons demonstrated the importance of accurate cloud phase classification, and specifically the identification of liquid water, for determining radiative fluxes. Enhanced retrievals of liquid water path for thin clouds were also shown to improve radiative flux calculations.

  1. Fine-scale Horizontal Structure of Arctic Mixed-Phase Clouds.

    SciTech Connect (OSTI)

    Rambukkange,M.; Verlinde, J.; Elorante, E.; Luke, E.; Kollias, P.; Shupe, M.

    2006-07-10

    Recent in situ observations in stratiform clouds suggest that mixed phase regimes, here defined as limited cloud volumes containing both liquid and solid water, are constrained to narrow layers (order 100 m) separating all-liquid and fully glaciated volumes (Hallett and Viddaurre, 2005). The Department of Energy Atmospheric Radiation Measurement Program's (DOE-ARM, Ackerman and Stokes, 2003) North Slope of Alaska (NSA) ARM Climate Research Facility (ACRF) recently started collecting routine measurement of radar Doppler velocity power spectra from the Millimeter Cloud Radar (MMCR). Shupe et al. (2004) showed that Doppler spectra has potential to separate the contributions to the total reflectivity of the liquid and solid water in the radar volume, and thus to investigate further Hallett and Viddaurre's findings. The Mixed-Phase Arctic Cloud Experiment (MPACE) was conducted along the NSA to investigate the properties of Arctic mixed phase clouds (Verlinde et al., 2006). We present surface based remote sensing data from MPACE to discuss the fine-scale structure of the mixed-phase clouds observed during this experiment.

  2. ARM - Field Campaign - Mixed-Phase Arctic Cloud Experiment

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    The University of North Dakota Citation was the in situ platform, while the DOE-ARM UAV ... counter and the CSU IN counter, while the UAV had downward looking cloud radar, lidar and ...

  3. Influence of Arctic cloud thermodynamic phase on surface shortwave flux

    SciTech Connect (OSTI)

    Lubin, D.; Vogelmann, A.

    2010-03-15

    As part of the Indirect and Semi-Direct Aerosol Campaign (ISDAC) an Analytical Spectral Devices (ASD, Inc.) spectroradiometer was deployed at the Barrow NSA site during April and May of 2008, and in April-October of 2009. This instrument recorded one-minute averages of surface downwelling spectral flux in the wavelength interval 350-2200 nm, thus sampling the two major near infrared windows (1.6 and 2.2 microns) in which the flux is influenced by cloud microphysical properties including thermodynamic phase and effective particle size. Aircraft in situ measurements of cloud properties show mostly mixed-phase clouds over Barrow during the campaign, but with wide variability in relative liquid versus ice water content. At fixed total optical depth, this variability in phase composition can yield of order 5-10 Watts per square meter in surface flux variability, with greater cloud attenuation of the surface flux usually occurring under higher ice water content. Thus our data show that changes in cloud phase properties, even within the 'mixed-phase' category, can affect the surface energy balance at the same order of magnitude as greenhouse gas increases. Analysis of this spectral radiometric data provides suggestions for testing new mixed-phase parameterizations in climate models.

  4. Improved Arctic Cloud and Aerosol Research and Model Parameterizations

    SciTech Connect (OSTI)

    Kenneth Sassen

    2007-03-01

    In this report are summarized our contributions to the Atmospheric Measurement (ARM) program supported by the Department of Energy. Our involvement commenced in 1990 during the planning stages of the design of the ARM Cloud and Radiation Testbed (CART) sites. We have worked continuously (up to 2006) on our ARM research objectives, building on our earlier findings to advance our knowledge in several areas. Below we summarize our research over this period, with an emphasis on the most recent work. We have participated in several aircraft-supported deployments at the SGP and NSA sites. In addition to deploying the Polarization Diversity Lidar (PDL) system (Sassen 1994; Noel and Sassen 2005) designed and constructed under ARM funding, we have operated other sophisticated instruments W-band polarimetric Doppler radar, and midinfrared radiometer for intercalibration and student training purposes. We have worked closely with University of North Dakota scientists, twice co-directing the Citation operations through ground-to-air communications, and serving as the CART ground-based mission coordinator with NASA aircraft during the 1996 SUCCESS/IOP campaign. We have also taken a leading role in initiating case study research involving a number of ARM coinvestigators. Analyses of several case studies from these IOPs have been reported in journal articles, as we show in Table 1. The PDL has also participated in other major field projects, including FIRE II and CRYSTAL-FACE. In general, the published results of our IOP research can be divided into two categories: comprehensive cloud case study analyses to shed light on fundamental cloud processes using the unique CART IOP measurement capabilities, and the analysis of in situ data for the testing of remote sensing cloud retrieval algorithms. One of the goals of the case study approach is to provide sufficiently detailed descriptions of cloud systems from the data-rich CART environment to make them suitable for application to

  5. Parameterizing correlations between hydrometeor species in mixed-phase Arctic clouds

    SciTech Connect (OSTI)

    Larson, Vincent E.; Nielsen, Brandon J.; Fan, Jiwen; Ovchinnikov, Mikhail

    2011-08-16

    Mixed-phase Arctic clouds, like other clouds, contain small-scale variability in hydrometeor fields, such as cloud water or snow mixing ratio. This variability may be worth parameterizing in coarse-resolution numerical models. In particular, for modeling processes such as accretion and aggregation, it would be useful to parameterize subgrid correlations among hydrometeor species. However, one difficulty is that there exist many hydrometeor species and many microphysical processes, leading to complexity and computational expense.Existing lower and upper bounds (inequalities) on linear correlation coefficients provide useful guidance, but these bounds are too loose to serve directly as a method to predict subgrid correlations. Therefore, this paper proposes an alternative method that is based on a blend of theory and empiricism. The method begins with the spherical parameterization framework of Pinheiro and Bates (1996), which expresses the correlation matrix in terms of its Cholesky factorization. The values of the elements of the Cholesky matrix are parameterized here using a cosine row-wise formula that is inspired by the aforementioned bounds on correlations. The method has three advantages: 1) the computational expense is tolerable; 2) the correlations are, by construction, guaranteed to be consistent with each other; and 3) the methodology is fairly general and hence may be applicable to other problems. The method is tested non-interactively using simulations of three Arctic mixed-phase cloud cases from two different field experiments: the Indirect and Semi-Direct Aerosol Campaign (ISDAC) and the Mixed-Phase Arctic Cloud Experiment (M-PACE). Benchmark simulations are performed using a large-eddy simulation (LES) model that includes a bin microphysical scheme. The correlations estimated by the new method satisfactorily approximate the correlations produced by the LES.

  6. Indirect and semi-direct aerosol campaign: The impact of Arctic aerosols on clouds

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    McFarquhar, Greg M.; Ghan, Steven; Verlinde, Johannes; Korolev, Alexei; Strapp, J. Walter; Schmid, Beat; Tomlinson, Jason M.; Wolde, Menqistu; Brooks, Sarah D.; Cziczo, Dan; et al

    2011-02-01

    A comprehensive dataset of microphysical and radiative properties of aerosols and clouds in the boundary layer in the vicinity of Barrow, Alaska, was collected in April 2008 during the Indirect and Semi-Direct Aerosol Campaign (ISDAC). ISDAC's primary aim was to examine the effects of aerosols, including those generated by Asian wildfires, on clouds that contain both liquid and ice. ISDAC utilized the Atmospheric Radiation Measurement Pro- gram's permanent observational facilities at Barrow and specially deployed instruments measuring aerosol, ice fog, precipitation, and radiation. The National Research Council of Canada Convair-580 flew 27 sorties and collected data using an unprecedented 41more » stateof- the-art cloud and aerosol instruments for more than 100 h on 12 different days. Aerosol compositions, including fresh and processed sea salt, biomassburning particles, organics, and sulfates mixed with organics, varied between flights. Observations in a dense arctic haze on 19 April and above, within, and below the single-layer stratocumulus on 8 and 26 April are enabling a process-oriented understanding of how aerosols affect arctic clouds. Inhomogeneities in reflectivity, a close coupling of upward and downward Doppler motion, and a nearly constant ice profile in the single-layer stratocumulus suggests that vertical mixing is responsible for its longevity observed during ISDAC. Data acquired in cirrus on flights between Barrow and Fairbanks, Alaska, are improving the understanding of the performance of cloud probes in ice. Furthermore, ISDAC data will improve the representation of cloud and aerosol processes in models covering a variety of spatial and temporal scales, and determine the extent to which surface measurements can provide retrievals of aerosols, clouds, precipitation, and radiative heating.« less

  7. The role of ice nuclei recycling in the maintenance of cloud ice in Arctic mixed-phase stratocumulus

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Solomon, A.; Feingold, G.; Shupe, M. D.

    2015-04-21

    This study investigates the maintenance of cloud ice production in Arctic mixed phase stratocumulus in large-eddy simulations that include a prognostic ice nuclei (IN) formulation and a diurnal cycle. Balances derived from a mixed-layer model and phase analyses are used to provide insight into buffering mechanisms that maintain ice in these cloud systems. We find that for the case under investigation, IN recycling through subcloud sublimation considerably prolongs ice production over a multi-day integration. This effective source of IN to the cloud dominates over mixing sources from above or below the cloud-driven mixed layer. Competing feedbacks between dynamical mixing andmore » recycling are found to slow the rate of ice lost from the mixed layer when a diurnal cycle is simulated. The results of this study have important implications for maintaining phase partitioning of cloud ice and liquid that determine the radiative forcing of Arctic mixed-phase clouds.« less

  8. The role of ice nuclei recycling in the maintenance of cloud ice in Arctic mixed-phase stratocumulus

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Solomon, A.; Feingold, G.; Shupe, M. D.

    2015-09-25

    This study investigates the maintenance of cloud ice production in Arctic mixed-phase stratocumulus in large eddy simulations that include a prognostic ice nuclei (IN) formulation and a diurnal cycle. Balances derived from a mixed-layer model and phase analyses are used to provide insight into buffering mechanisms that maintain ice in these cloud systems. We find that, for the case under investigation, IN recycling through subcloud sublimation considerably prolongs ice production over a multi-day integration. This effective source of IN to the cloud dominates over mixing sources from above or below the cloud-driven mixed layer. Competing feedbacks between dynamical mixing andmore » recycling are found to slow the rate of ice lost from the mixed layer when a diurnal cycle is simulated. The results of this study have important implications for maintaining phase partitioning of cloud ice and liquid that determine the radiative forcing of Arctic mixed-phase clouds.« less

  9. The influence of mixed and phase clouds on surface shortwave irradiance during the Arctic spring

    SciTech Connect (OSTI)

    Lubin D.; Vogelmann A.

    2011-10-13

    The influence of mixed-phase stratiform clouds on the surface shortwave irradiance is examined using unique spectral shortwave irradiance measurements made during the Indirect and Semi-Direct Aerosol Campaign (ISDAC), supported by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program. An Analytical Spectral Devices (ASD, Inc.) spectroradiometer measured downwelling spectral irradiance from 350 to 2200 nm in one-minute averages throughout April-May 2008 from the ARM Climate Research Facility's North Slope of Alaska (NSA) site at Barrow. This study examines spectral irradiance measurements made under single-layer, overcast cloud decks having geometric thickness < 3000 m. Cloud optical depth is retrieved from irradiance in the interval 1022-1033 nm. The contrasting surface radiative influences of mixed-phase clouds and liquid-water clouds are discerned using irradiances in the 1.6-{micro}m window. Compared with liquid-water clouds, mixed-phase clouds during the Arctic spring cause a greater reduction of shortwave irradiance at the surface. At fixed conservative-scattering optical depth (constant optical depth for wavelengths {lambda} < 1100 nm), the presence of ice water in cloud reduces the near-IR surface irradiance by an additional several watts-per-meter-squared. This additional reduction, or supplemental ice absorption, is typically {approx}5 W m{sup -2} near solar noon over Barrow, and decreases with increasing solar zenith angle. However, for some cloud decks this additional absorption can be as large as 8-10 W m{sup -2}.

  10. Simulations of arctic mixed-phase clouds in forecasts with CAM3 and AM2 for M-PACE

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Xie, Shaocheng; Boyle, James; Klein, Stephen A.; Liu, Xiaohong; Ghan, Steven

    2008-02-27

    [1] Simulations of mixed-phase clouds in forecasts with the NCAR Atmosphere Model version 3 (CAM3) and the GFDL Atmospheric Model version 2 (AM2) for the Mixed-Phase Arctic Cloud Experiment (M-PACE) are performed using analysis data from numerical weather prediction centers. CAM3 significantly underestimates the observed boundary layer mixed-phase cloud fraction and cannot realistically simulate the variations of liquid water fraction with temperature and cloud height due to its oversimplified cloud microphysical scheme. In contrast, AM2 reasonably reproduces the observed boundary layer cloud fraction while its clouds contain much less cloud condensate than CAM3 and the observations. The simulation of themore » boundary layer mixed-phase clouds and their microphysical properties is considerably improved in CAM3 when a new physically based cloud microphysical scheme is used (CAM3LIU). The new scheme also leads to an improved simulation of the surface and top of the atmosphere longwave radiative fluxes. Sensitivity tests show that these results are not sensitive to the analysis data used for model initialization. Increasing model horizontal resolution helps capture the subgrid-scale features in Arctic frontal clouds but does not help improve the simulation of the single-layer boundary layer clouds. AM2 simulated cloud fraction and LWP are sensitive to the change in cloud ice number concentrations used in the Wegener-Bergeron-Findeisen process while CAM3LIU only shows moderate sensitivity in its cloud fields to this change. Furthermore, this paper shows that the Wegener-Bergeron-Findeisen process is important for these models to correctly simulate the observed features of mixed-phase clouds.« less

  11. Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. Part I: Single layer cloud

    SciTech Connect (OSTI)

    Klein, S A; McCoy, R B; Morrison, H; Ackerman, A; Avramov, A; deBoer, G; Chen, M; Cole, J; DelGenio, A; Golaz, J; Hashino, T; Harrington, J; Hoose, C; Khairoutdinov, M; Larson, V; Liu, X; Luo, Y; McFarquhar, G; Menon, S; Neggers, R; Park, S; Poellot, M; von Salzen, K; Schmidt, J; Sednev, I; Shipway, B; Shupe, M; Spangenberg, D; Sud, Y; Turner, D; Veron, D; Falk, M; Foster, M; Fridlind, A; Walker, G; Wang, Z; Wolf, A; Xie, S; Xu, K; Yang, F; Zhang, G

    2008-02-27

    Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) program's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud top temperature of -15 C. The observed liquid water path of around 160 g m{sup -2} was about two-thirds of the adiabatic value and much greater than the mass of ice crystal precipitation which when integrated from the surface to cloud top was around 15 g m{sup -2}. The simulations were performed by seventeen single-column models (SCMs) and nine cloud-resolving models (CRMs). While the simulated ice water path is generally consistent with the observed values, the median SCM and CRM liquid water path is a factor of three smaller than observed. Results from a sensitivity study in which models removed ice microphysics indicate that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path. Despite this general underestimate, the simulated liquid and ice water paths of several models are consistent with the observed values. Furthermore, there is some evidence that models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter is also present. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics. This case study, which has been well observed from both aircraft and ground-based remote sensors, could be a benchmark for model simulations of mixed-phase clouds.

  12. A 10 Year Climatology of Arctic Cloud Fraction and Radiative Forcing at Barrow, Alaska

    SciTech Connect (OSTI)

    Dong, Xiquan; Xi, Baike; Crosby, Kathryn; Long, Charles N.; Stone, R. S.; Shupe, Matthew D.

    2010-09-15

    A 10-yr record of Arctic cloud fraction and surface radiation budget has been generated using data collected from June 1998 to May 2008 at the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site and the nearby NOAA Barrow Observatory (BRW). The record includes the seasonal variations of cloud fraction (CF), cloud liquid water path (LWP), precipitable water vapor (PWV), surface albedo, shortwave (SW) and longwave (LW) fluxes and cloud radative forcings (CRFs), as well as their decadal variations. Values of CF derived from different instruments and methods agree well, having an annual average of ~0.74. Cloudiness increases from March to May, remains high (~0.8-0.9) from May to October, and then decreases over winter. More clouds and higher LWP and PWV occurred during the warm season (May-October) than the cold season (November-April). These results are strongly associated with southerly flow which transports warm, moist air masses to Barrow from the North Pacific and over area of Alaska already free of snow during the warm season and with a dipole pattern of pressure in which a high is centered over the Beaufort Sea and low over the Aleutians during the cold season. The monthly means of estimated clear-sky and measured allsky SW-down and LW-down fluxes at the two facilities are almost identical with the annual mean differences less than 1.6 W m-2. The downwelling and upwelling LW fluxes remain almost constant from January to March, then increase from March and peak during July-August. SW-down fluxes are primarily determined by seasonal changes in the intensity and duration of insolation over Northern Alaska, and are also strongly dependent on cloud fraction and optical depth, and surface albedo. The monthly variations of NET CRF generally follow the cycle of SW CRF, modulated by LW effects. On annual average, the negative SW CRF and positive LW CRF tend to cancel, resulting in annual average NET CRF of 2-4.5 Wm-2. Arctic clouds have a 3 net

  13. Remote Sensing and In-Situ Observations of Arctic Mixed-Phase and Cirrus Clouds Acquired During Mixed-Phase Arctic Cloud Experiment: Atmospheric Radiation Measurement Uninhabited Aerospace Vehicle Participation

    SciTech Connect (OSTI)

    McFarquhar, G.M.; Freer, M.; Um, J.; McCoy, R.; Bolton, W.

    2005-03-18

    The Atmospheric Radiation Monitor (ARM) uninhabited aerospace vehicle (UAV) program aims to develop measurement techniques and instruments suitable for a new class of high altitude, long endurance UAVs while supporting the climate community with valuable data sets. Using the Scaled Composites Proteus aircraft, ARM UAV participated in Mixed-Phase Arctic Cloud Experiment (M-PACE), obtaining unique data to help understand the interaction of clouds with solar and infrared radiation. Many measurements obtained using the Proteus were coincident with in-situ observations made by the UND Citation. Data from M-PACE are needed to understand interactions between clouds, the atmosphere and ocean in the Arctic, critical interactions given large-scale models suggest enhanced warming compared to lower latitudes is occurring.

  14. SRI2007 Conference - Poster Download

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    SRI Poster Download Click here to download poster (30MB pdf)

  15. Using Radar, Lidar and Radiometer Data from NSA and SHEBA to Quantify Cloud Property Effects on the Surface Heat Budget in the Arctic

    SciTech Connect (OSTI)

    Janet Intrieri; Mathhew Shupe

    2005-01-01

    Cloud and radiation data from two distinctly different Arctic areas are analyzed to study the differences between coastal Alaskan and open Arctic Ocean region clouds and their respective influence on the surface radiation budget. The cloud and radiation datasets were obtained from (1) the DOE North Slope of Alaska (NSA) facility in the coastal town of Barrow, Alaska, and (2) the SHEBA field program, which was conducted from an icebreaker frozen in, and drifting with, the sea-ice for one year in the Western Arctic Ocean. Radar, lidar, radiometer, and sounding measurements from both locations were used to produce annual cycles of cloud occurrence and height, atmospheric temperature and humidity, surface longwave and shortwave broadband fluxes, surface albedo, and cloud radiative forcing. In general, both regions revealed a similar annual trend of cloud occurrence fraction with minimum values in winter (60-75%) and maximum values during spring, summer and fall (80-90%). However, the annual average cloud occurrence fraction for SHEBA (76%) was lower than the 6-year average cloud occurrence at NSA (92%). Both Arctic areas also showed similar annual cycle trends of cloud forcing with clouds warming the surface through most of the year and a period of surface cooling during the summer, when cloud shading effects overwhelm cloud greenhouse effects. The greatest difference between the two regions was observed in the magnitude of the cloud cooling effect (i.e., shortwave cloud forcing), which was significantly stronger at NSA and lasted for a longer period of time than at SHEBA. This is predominantly due to the longer and stronger melt season at NSA (i.e., albedo values that are much lower coupled with Sun angles that are somewhat higher) than the melt season observed over the ice pack at SHEBA. Longwave cloud forcing values were comparable between the two sites indicating a general similarity in cloudiness and atmospheric temperature and humidity structure between the two

  16. Downloads | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... computing, & computer science --Agent-based modeling --Applied mathematics --Artificial intelligence --Cloud computing --Computational differentiation --Geographic information ...

  17. Downloads | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    computing, & computer science --Agent-based modeling --Applied mathematics --Artificial intelligence --Cloud computing --Computational differentiation --Geographic information ...

  18. Downloads | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... mathematics --Artificial intelligence --Cloud computing ... --Networking & data management --Parallel ... October 6, 2015 Tiny Particles with Big Magnetic Power ...

  19. download | OpenEI Community

    Open Energy Info (EERE)

    download Home Graham7781's picture Submitted by Graham7781(2017) Super contributor 15 March, 2013 - 10:23 Quarterly Smart Grid Data available for download on OpenEI data download...

  20. Downloads | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Downloads Topic - Any - General Argonne Information -Awards -Honors Energy -Energy efficiency --Vehicles ---Alternative fuels ---Automotive engineering ---Biofuels ---Diesel ---Electric drive technology ---Fuel economy ---Fuel injection ---Heavy-duty vehicles ---Hybrid & electric vehicles ---Hydrogen & fuel cells ---Internal combustion ---Maglev systems ---Powertrain research ---Vehicle testing --Building design ---Construction ---Industrial heating & cooling ---Industrial lighting

  1. Downloads | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Downloads Topic - Any - General Argonne Information -Awards -Honors Energy -Energy efficiency --Vehicles ---Alternative fuels ---Automotive engineering ---Biofuels ---Diesel ---Electric drive technology ---Fuel economy ---Fuel injection ---Heavy-duty vehicles ---Hybrid & electric vehicles ---Hydrogen & fuel cells ---Internal combustion ---Maglev systems ---Powertrain research ---Vehicle testing --Building design ---Construction ---Industrial heating & cooling ---Industrial lighting

  2. Using A-Train Arctic cloud observations to constrain and improve...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    radiation anomalies to the 2007 Arctic sea ice loss Jennifer E. Kay 1,2 Andrew Gettelman 1 , Tristan L'Ecuyer 2 ,Graeme Stephens 2 , and Chris O'Dell 2 1 National Center for...

  3. CACTUS Software Download

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    CACTUS Software Download - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary Power Energy Conversion Efficiency Solar Energy Wind Energy Water Power Supercritical CO2 Geothermal Natural Gas Safety, Security & Resilience of the Energy Infrastructure Energy Storage Nuclear Power & Engineering Grid Modernization Battery Testing Nuclear Energy Defense Waste Management Programs Advanced

  4. Intercomparison of Large-eddy Simulations of Arctic Mixed-phase Clouds: Importance of Ice Size Distribution Assumptions

    SciTech Connect (OSTI)

    Ovchinnikov, Mikhail; Ackerman, Andrew; Avramov, Alex; Cheng, Anning; Fan, Jiwen; Fridlind, Ann; Ghan, Steven J.; Harrington, Jerry Y.; Hoose, Corinna; Korolev, Alexei; McFarquhar, Greg; Morrison, H.; Paukert, Marco; Savre, Julien; Shipway, Ben; Shupe, Matthew D.; Solomon, Amy; Sulia, Kara

    2014-03-14

    Large-eddy simulations of mixed-phase Arctic clouds by 11 different models are analyzed with the goal of improving understanding and model representation of processes controlling the evolution of these clouds. In a case based on observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC), it is found that ice number concentration, Ni, exerts significant influence on the cloud structure. Increasing Ni leads to a substantial reduction in liquid water path (LWP) and potential cloud dissipation, in agreement with earlier studies. By comparing simulations with the same microphysics coupled to different dynamical cores as well as the same dynamics coupled to different microphysics schemes, it is found that the ice water path (IWP) is mainly controlled by ice microphysics, while the inter-model differences in LWP are largely driven by physics and numerics of the dynamical cores. In contrast to previous intercomparisons, all models here use the same ice particle properties (i.e., mass-size, mass-fall speed, and mass-capacitance relationships) and a common radiation parameterization. The constrained setup exposes the importance of ice particle size distributions (PSD) in influencing cloud evolution. A clear separation in LWP and IWP predicted by models with bin and bulk microphysical treatments is documented and attributed primarily to the assumed shape of ice PSD used in bulk schemes. Compared to the bin schemes that explicitly predict the PSD, schemes assuming exponential ice PSD underestimate ice growth by vapor deposition and overestimate mass-weighted fall speed leading to an underprediction of IWP by a factor of two in the considered case.

  5. Software & Downloads | Open Energy Information

    Open Energy Info (EERE)

    Wind Industry Careers Skystream 3.7 Downloads These resources require a Windows computer. Skyview is the manufacturer-provided wind turbine communication software. It is...

  6. Download GETEM, August 2012 Beta | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Download GETEM, August 2012 Beta Download GETEM, August 2012 Beta

  7. Alternative Fuels Data Center: Data Downloads

    Alternative Fuels and Advanced Vehicles Data Center [Office of Energy Efficiency and Renewable Energy (EERE)]

    Tools Printable Version Share this resource Send a link to Alternative Fuels Data Center: Data Downloads to someone by E-mail Share Alternative Fuels Data Center: Data Downloads on Facebook Tweet about Alternative Fuels Data Center: Data Downloads on Twitter Bookmark Alternative Fuels Data Center: Data Downloads on Google Bookmark Alternative Fuels Data Center: Data Downloads on Delicious Rank Alternative Fuels Data Center: Data Downloads on Digg Find More places to share Alternative Fuels Data

  8. Evaluation of Mixed-Phase Cloud Microphysics Parameterizations...

    Office of Scientific and Technical Information (OSTI)

    the partitioning of condensed water into liquid droplets and ice crystals in these Arctic clouds, which affect modeled cloud phase, cloud lifetime and radiative properties. ...

  9. OHA Archive Download Page | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    OHA Archive Download Page OHA Archive Download Page OHA FOIA Cases Archive File OHA Security Cases Archive File OHA Whistleblower Cases Archive File OHA EIA Cases Archive File OHA ...

  10. NGEE Arctic Webcam Photographs, Barrow Environmental Observatory, Barrow, Alaska

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Bob Busey; Larry Hinzman

    2012-04-01

    The NGEE Arctic Webcam (PTZ Camera) captures two views of seasonal transitions from its generally south-facing position on a tower located at the Barrow Environmental Observatory near Barrow, Alaska. Images are captured every 30 minutes. Historical images are available for download. The camera is operated by the U.S. DOE sponsored Next Generation Ecosystem Experiments - Arctic (NGEE Arctic) project.

  11. NGEE Arctic Webcam Photographs, Barrow Environmental Observatory, Barrow, Alaska

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Bob Busey; Larry Hinzman

    The NGEE Arctic Webcam (PTZ Camera) captures two views of seasonal transitions from its generally south-facing position on a tower located at the Barrow Environmental Observatory near Barrow, Alaska. Images are captured every 30 minutes. Historical images are available for download. The camera is operated by the U.S. DOE sponsored Next Generation Ecosystem Experiments - Arctic (NGEE Arctic) project.

  12. QTR 2015 - Downloads | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    2015 - Downloads QTR 2015 - Downloads Additional documents related to QTR 2015. Quadrennial Technology Review 2015 - Full Report (35.48 MB) QTR 2015 - Front Matter (307.83 KB) QTR 2015 - Glossary (173.85 KB) QTR 2015 - Acronyms (145.72 KB) QTR 2015 - Authors, Contributors and Reviewers (192.34 KB) More Documents & Publications QTR 2015 - Downloads Chapter 7 - Advancing Systems and Technologies to Produce Cleaner Fuels QTR 2015 - Downloads Chapter 10 - Concepts in Integrated Analysis Chapter

  13. Acquisition Forecast Download | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Acquisition Forecast Download Acquisition Forecast Download Click on the link to download a copy of the DOE HQ Acquisition Forecast. Acquisition-Forecast-2016-07-20.xlsx (72.85 KB) More Documents & Publications Small Business Program Manager Directory EA-1900: Notice of Availability of a Draft Environmental Assessment Assessment Report: OAS-V-15-01

  14. Parameterization of the Extinction Coefficient in Ice and Mixed-Phase Arctic Clouds during the ISDAC Field Campaign

    SciTech Connect (OSTI)

    Korolev, A; Shashkov, A; Barker, H

    2012-03-06

    This report documents the history of attempts to directly measure cloud extinction, the current measurement device known as the Cloud Extinction Probe (CEP), specific problems with direct measurement of extinction coefficient, and the attempts made here to address these problems. Extinction coefficient is one of the fundamental microphysical parameters characterizing bulk properties of clouds. Knowledge of extinction coefficient is of crucial importance for radiative transfer calculations in weather prediction and climate models given that Earth's radiation budget (ERB) is modulated much by clouds. In order for a large-scale model to properly account for ERB and perturbations to it, it must ultimately be able to simulate cloud extinction coefficient well. In turn this requires adequate and simultaneous simulation of profiles of cloud water content and particle habit and size. Similarly, remote inference of cloud properties requires assumptions to be made about cloud phase and associated single-scattering properties, of which extinction coefficient is crucial. Hence, extinction coefficient plays an important role in both application and validation of methods for remote inference of cloud properties from data obtained from both satellite and surface sensors (e.g., Barker et al. 2008). While estimation of extinction coefficient within large-scale models is relatively straightforward for pure water droplets, thanks to Mie theory, mixed-phase and ice clouds still present problems. This is because of the myriad forms and sizes that crystals can achieve, each having their own unique extinction properties. For the foreseeable future, large-scale models will have to be content with diagnostic parametrization of crystal size and type. However, before they are able to provide satisfactory values needed for calculation of radiative transfer, they require the intermediate step of assigning single-scattering properties to particles. The most basic of these is extinction

  15. A comparison of cloud properties at a coastal and inland site...

    Office of Scientific and Technical Information (OSTI)

    have examined differences in cloud liquid water paths (LWPs) at a coastal (Barrow) and an ... KEYWORDS: arctic clouds, cloud liquid water, microwave radiometer, ECMWF model, ...

  16. Energy.gov File Naming Conventions for Downloads | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    File Naming Conventions for Downloads Energy.gov File Naming Conventions for Downloads When uploading files to download pages in Energy.gov's content management system (CMS), ...

  17. Downloads: August 6-8, 2013 National Veterans Small Business...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Downloads: August 6-8, 2013 National Veterans Small Business Conference Downloads: August 6-8, 2013 National Veterans Small Business Conference Download materials from the August ...

  18. Scientific Cloud Computing Misconceptions

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Scientific Cloud Computing Misconceptions Scientific Cloud Computing Misconceptions July 1, 2011 Part of the Magellan project was to understand both the possibilities and the limitations of cloud computing in the pursuit of science. At a recent conference, Magellan investigator Shane Canon outlined some persistent misconceptions about doing science in the cloud - and what Magellan has taught us about them. » Read the ISGTW story. » Download the slides (PDF, 4.1MB

  19. AVTA: 2012 Chevrolet Volt PHEV Downloadable Dynamometer Database...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Chevrolet Volt PHEV Downloadable Dynamometer Database Reports AVTA: 2012 Chevrolet Volt PHEV Downloadable Dynamometer Database Reports The Vehicle Technologies Office's Advanced ...

  20. AVTA: 2012 Toyota Prius PHEV Downloadable Dynamometer Database...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Toyota Prius PHEV Downloadable Dynamometer Database Reports AVTA: 2012 Toyota Prius PHEV Downloadable Dynamometer Database Reports The Vehicle Technologies Office's Advanced ...

  1. Intercomparison of model simulations of mixed-phase clouds observed...

    Office of Scientific and Technical Information (OSTI)

    Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. Part I: Single layer cloud Citation Details In-Document ...

  2. Ice in Arctic Mixed-phase Stratocumulus

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Ice Nuclei Recycling in the Maintenance of Cloud Ice in Arctic Mixed-phase Stratocumulus For original submission and image(s), see ARM Research Highlights http:www.arm.gov...

  3. SunShot Solar Projects Download

    Office of Energy Efficiency and Renewable Energy (EERE)

    The Department of Energy's SunShot Initiative funds projects by private companies, universities, state and local governments, nonprofit organizations, and national laboratories to drive down the cost of solar electricity. We work to make it faster, easier, and more affordable for Americans to choose solar energy in their daily lives. Download the data driving the SunShot Project map [link], including SunShot’s active and inactive projects, funding amounts, and program areas.

  4. Energy Escalation Rate Calculator Download | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    You can download EERC 2.0-15 by clicking on the link for your operating system below. ... Mac OS X: After downloading, double click eercinstall. Please note you must have Java 1.7 ...

  5. ARM - Publications: Science Team Meeting Documents: Clouds and...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Clouds and radiation in the Arctic coastal system - effects of local heterogeneity Key, Erica University of Miami, RSMAS Minnett, Peter University of Miami Improving our...

  6. Validation of MODIS-Retrieved Cloud Fractions Using Whole Sky...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    (ARM) locales for the year 2002. Statistics concerning the frequency of cloud ... especially apparent in the arctic environment, will be shown and a preliminary ...

  7. Intercomparison of Large-eddy Simulations of Arctic Mixed-phase...

    Office of Scientific and Technical Information (OSTI)

    Intercomparison of Large-eddy Simulations of Arctic Mixed-phase Clouds: Importance of Ice Size Distribution Assumptions Citation Details In-Document Search Title: Intercomparison ...

  8. Modeling the summertime Arctic cloudy boundary layer

    SciTech Connect (OSTI)

    Curry, J.A.; Pinto, J.O.; McInnes, K.L.

    1996-04-01

    Global climate models have particular difficulty in simulating the low-level clouds during the Arctic summer. Model problems are exacerbated in the polar regions by the complicated vertical structure of the Arctic boundary layer. The presence of multiple cloud layers, a humidity inversion above cloud top, and vertical fluxes in the cloud that are decoupled from the surface fluxes, identified in Curry et al. (1988), suggest that models containing sophisticated physical parameterizations would be required to accurately model this region. Accurate modeling of the vertical structure of multiple cloud layers in climate models is important for determination of the surface radiative fluxes. This study focuses on the problem of modeling the layered structure of the Arctic summertime boundary-layer clouds and in particular, the representation of the more complex boundary layer type consisting of a stable foggy surface layer surmounted by a cloud-topped mixed layer. A hierarchical modeling/diagnosis approach is used. A case study from the summertime Arctic Stratus Experiment is examined. A high-resolution, one-dimensional model of turbulence and radiation is tested against the observations and is then used in sensitivity studies to infer the optimal conditions for maintaining two separate layers in the Arctic summertime boundary layer. A three-dimensional mesoscale atmospheric model is then used to simulate the interaction of this cloud deck with the large-scale atmospheric dynamics. An assessment of the improvements needed to the parameterizations of the boundary layer, cloud microphysics, and radiation in the 3-D model is made.

  9. Testing Cloud Microphysics Parameterizations in NCAR CAM5 with...

    Office of Scientific and Technical Information (OSTI)

    Title: Testing Cloud Microphysics Parameterizations in NCAR CAM5 with ISDAC and M-PACE Observations Arctic clouds simulated by the NCAR Community Atmospheric Model version 5 (CAM5) ...

  10. Cloud-Scale Vertical Velocity and Turbulent Dissipation Rate...

    Office of Scientific and Technical Information (OSTI)

    Files are available for manually-selected, stratiform, mixed-phase cloud cases observed at the North Slope of Alaska (NSA) site during periods covering the Mixed-Phase Arctic Cloud ...

  11. Energy Intensity Indicators: Methodology Downloads | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Indicators: Methodology Downloads Energy Intensity Indicators: Methodology Downloads The files listed below contain methodology documentation and related studies that support the information presented on this website. The files are available to view and/or download as Adobe Acrobat PDF files. Energy Indicators System: Index Construction Methodology (101.17 KB) Changing the Base Year for the Index (23.98 KB) "A Note on the Fisher Ideal Index Decomposition for Structural Change in Energy

  12. Biographies Download: Ambassadors for the Minorities in Energy...

    Broader source: Energy.gov (indexed) [DOE]

    economic development, STEM education, and climate change. Download the biographies and photos of our Ambassadors below. Ambassador Bios More Documents & Publications MIE Milestone...

  13. Download LPO's Illustrated Poster Series | Department of Energy

    Energy Savers [EERE]

    Want to print your own? Download the high-resolution PDF files below: ADVANCED NUCLEAR ENERGY POSTER (2.85 MB) BIOENERGY & BIOFUELS POSTER (9.26 MB) CLEAN VEHICLE MANUFACTURING ...

  14. Download Energy.gov's Women's History Month Illustrations

    Broader source: Energy.gov [DOE]

    During Women's History Month we rolled out some beautiful illustrations of Mae Jemison, Annie Easley, Chien-Shiung Wu and Ellen Ochoa. You can now download the poster-sized versions of these illustrations.

  15. Microsoft Word - Using Green Button Download.doc

    Office of Environmental Management (EM)

    How a Customer Accesses Green Button Data on PGE.com Step 1: Log in to pge.com account with username and password. Step 2: Click on "My Usage" tab. Step 3: Select "Download My ...

  16. Management & Operating Subcontract Reporting Capability (MOSRC) Downloads |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy Management & Operating Subcontract Reporting Capability (MOSRC) Downloads Management & Operating Subcontract Reporting Capability (MOSRC) Downloads FY2015 MO Small Business Subcontracting Summary Report (13.31 KB) MOSRC Field Definitions (86.31 KB) FY2015 MO Small Business Subcontracting Report_Public.xlsx (183.75 KB) More Documents & Publications Service Contract Inventory Federal Reporting Recipient Information Federal Reporting Recipient Information

  17. Energy 101 Dialogue Series Downloads | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Dialogue Series Downloads Energy 101 Dialogue Series Downloads The Energy 101 dialogue series goals are to identify best practices surrounding teaching energy in the post-secondary setting, to amplify those best practices, and to support the creation of a community of practice of energy education experts. The first dialogue concentrated on issues surrounding teaching energy fundamentals in the classroom within the first two years of post-secondary education and featured presentations from energy

  18. The Arctic Lower Troposphere Observed Structure (ALTOS) Campaign

    SciTech Connect (OSTI)

    Verlinde, J

    2010-10-18

    The ALTOS campaign focuses on operating a tethered observing system for routine in situ sampling of low-level (< 2 km) Arctic clouds. It has been a long-term hope to fly tethered systems at Barrow, Alaska, but it is clear that the Federal Aviation Administration (FAA) will not permit in-cloud tether systems at Barrow, even if unmanned aerial vehicle (UAV) operations are allowed in the future. We have provided the scientific rationale for long-term, routine in situ measurements of cloud and aerosol properties in the Arctic. The existing restricted air space at Oliktok offers an opportunity to do so.

  19. Radiative Influences on Glaciation Time-Scales of Mixed-Phase Clouds

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Radiative Influences on Glaciation Time-Scales of Mixed-Phase Clouds Harrington, Jerry The Pennsylvania State University Category: Modeling Mixed-phase stratus clouds are dominant in the Arctic during much of the year. These clouds typically have liquid tops that precipitate ice. Time scales for the complete glaciation of such clouds (the Bergeron process) are typically computed using the classical mass growth equations for crystals and liquid drops. However, mixed phase arctic stratus have

  20. DOE Booth Presentations From Grainger Show 2015 Downloads | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy From Grainger Show 2015 Downloads DOE Booth Presentations From Grainger Show 2015 Downloads Today's Lighting Products: What You Need to Know (8 MB) It's 2015-Should All Your Sockets Be Filled with LEDs? (2.48 MB) The Lowdown on Downlights (1.68 MB) Next Generation Luminaires-A Guide to the Best and the Brightest in LED Fixtures (1.82 MB) LED Solutions for the Dark Hours (1.41 MB) Just the Facts, Ma'am: Getting the Most Out of LED Lighting Facts and Other LED Product Data (2.16 MB)

  1. Arctic Energy Summit

    Broader source: Energy.gov [DOE]

    The 2015 Arctic Energy Summit is a multi-disciplinary event expected to draw several hundred industry officials, scientists, academics, policy makers, energy professionals, and community leaders together to collaborate and share leading approaches on Arctic energy issues.

  2. Arctic Climate Measurements

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable ... Arctic Climate Measurements Global Climate Models Software Sustainable Subsurface ...

  3. Arctic Clouds Infrared Imaging Field Campaign Report

    Office of Scientific and Technical Information (OSTI)

    ... European Journal of Physics 34(6): S111-S121. 9 JA Shaw, March 2016, DOESC-ARM-16-002 Shupe, MD, VP Walden, E Eloranta, T Uttal, JR Campbell, SM Starkweather, and M Shiobara. ...

  4. OPEN HOUSE - Climate Prisms: Arctic

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    An interactive exploration of Arctic climate science through prisms of the visual arts, literary arts, info-vis, scientific presentations and more. Climate Prisms: Arctic is...

  5. Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties

    SciTech Connect (OSTI)

    Wang, Zhien

    2010-06-29

    The project is mainly focused on the characterization of cloud macrophysical and microphysical properties, especially for mixed-phased clouds and middle level ice clouds by combining radar, lidar, and radiometer measurements available from the ACRF sites. First, an advanced mixed-phase cloud retrieval algorithm will be developed to cover all mixed-phase clouds observed at the ACRF NSA site. The algorithm will be applied to the ACRF NSA observations to generate a long-term arctic mixed-phase cloud product for model validations and arctic mixed-phase cloud processes studies. To improve the representation of arctic mixed-phase clouds in GCMs, an advanced understanding of mixed-phase cloud processes is needed. By combining retrieved mixed-phase cloud microphysical properties with in situ data and large-scale meteorological data, the project aim to better understand the generations of ice crystals in supercooled water clouds, the maintenance mechanisms of the arctic mixed-phase clouds, and their connections with large-scale dynamics. The project will try to develop a new retrieval algorithm to study more complex mixed-phase clouds observed at the ACRF SGP site. Compared with optically thin ice clouds, optically thick middle level ice clouds are less studied because of limited available tools. The project will develop a new two wavelength radar technique for optically thick ice cloud study at SGP site by combining the MMCR with the W-band radar measurements. With this new algorithm, the SGP site will have a better capability to study all ice clouds. Another area of the proposal is to generate long-term cloud type classification product for the multiple ACRF sites. The cloud type classification product will not only facilitates the generation of the integrated cloud product by applying different retrieval algorithms to different types of clouds operationally, but will also support other research to better understand cloud properties and to validate model simulations. The

  6. Arctic Economics Model

    Energy Science and Technology Software Center (OSTI)

    1995-03-01

    AEM (Arctic Economics Model) for oil and gas was developed to provide an analytic framework for understanding the arctic area resources. It provides the capacity for integrating the resource and technology information gathered by the arctic research and development (R&D) program, measuring the benefits of alternaive R&D programs, and providing updated estimates of the future oil and gas potential from arctic areas. AEM enables the user to examine field or basin-level oil and gas recovery,more » costs, and economics. It provides a standard set of selected basin-specified input values or allows the user to input their own values. AEM consists of five integrated submodels: geologic/resource submodel, which distributes the arctic resource into 15 master regions, consisting of nine arctic offshore regions, three arctic onshore regions, and three souhtern Alaska (non-arctic) regions; technology submodel, which selects the most appropriate exploration and production structure (platform) for each arctic basin and water depth; oil and gas production submodel, which contains the relationship of per well recovery as a function of field size, production decline curves, and production decline curves by product; engineering costing and field development submodel, which develops the capital and operating costs associated with arctic oil and gas development; and the economics submodel, which captures the engineering costs and development timing and links these to oil and gas prices, corporate taxes and tax credits, depreciation, and timing of investment. AEM provides measures of producible oil and gas, costs, and ecomonic viability under alternative technology or financial conditions.« less

  7. Arctic Climate Systems Analysis

    SciTech Connect (OSTI)

    Ivey, Mark D.; Robinson, David G.; Boslough, Mark B.; Backus, George A.; Peterson, Kara J.; van Bloemen Waanders, Bart G.; Swiler, Laura Painton; Desilets, Darin Maurice; Reinert, Rhonda Karen

    2015-03-01

    This study began with a challenge from program area managers at Sandia National Laboratories to technical staff in the energy, climate, and infrastructure security areas: apply a systems-level perspective to existing science and technology program areas in order to determine technology gaps, identify new technical capabilities at Sandia that could be applied to these areas, and identify opportunities for innovation. The Arctic was selected as one of these areas for systems level analyses, and this report documents the results. In this study, an emphasis was placed on the arctic atmosphere since Sandia has been active in atmospheric research in the Arctic since 1997. This study begins with a discussion of the challenges and benefits of analyzing the Arctic as a system. It goes on to discuss current and future needs of the defense, scientific, energy, and intelligence communities for more comprehensive data products related to the Arctic; assess the current state of atmospheric measurement resources available for the Arctic; and explain how the capabilities at Sandia National Laboratories can be used to address the identified technological, data, and modeling needs of the defense, scientific, energy, and intelligence communities for Arctic support.

  8. The role of ice nuclei recycling in the maintenance of cloud...

    Office of Scientific and Technical Information (OSTI)

    The role of ice nuclei recycling in the maintenance of cloud ice in Arctic mixed-phase stratocumulus Citation Details In-Document Search Title: The role of ice nuclei recycling in ...

  9. Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties

    SciTech Connect (OSTI)

    Wang, Zhien

    2006-01-04

    The project is concerned with the characterization of cloud macrophysical and microphysical properties by combining radar, lidar, and radiometer measurements available from the U.S. Department of Energy's ARM Climate Research Facility (ACRF). To facilitate the production of integrated cloud product by applying different algorithms to the ARM data streams, an advanced cloud classification algorithm was developed to classified clouds into eight types at the SGP site based on ground-based active and passive measurements. Cloud type then can be used as a guidance to select an optimal retrieval algorithm for cloud microphysical property retrieval. The ultimate goal of the effort is to develop an operational cloud classification algorithm for ARM data streams. The vision 1 IDL code of the cloud classification algorithm based on the SGP ACRF site observations was delivered to the ARM cloud translator during 2004 ARM science team meeting. Another goal of the project is to study midlevel clouds, especially mixed-phase clouds, by developing new retrieval algorithms using integrated observations at the ACRF sites. Mixed-phase clouds play a particular role in the Arctic climate system. A multiple remote sensor based algorithm, which can provide ice water content and effective size profiles, liquid water path, and layer-mean effective radius of water droplet, was developed to study arctic mixed-phase clouds. The algorithm is applied to long-term ARM observations at the NSA ACRF site. Based on these retrieval results, we are studying seasonal and interannual variations of arctic mixed-phase cloud macro- and micro-physical properties.

  10. Downloads: August 6-8, 2013 National Veterans Small Business Conference |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy Downloads: August 6-8, 2013 National Veterans Small Business Conference Downloads: August 6-8, 2013 National Veterans Small Business Conference Download materials from the August 6-8, 2013 - National Veterans Small Business Conference, in St. Louis, Missouri. John Hale on Small Business Funding Aug 2013 (349.4 KB) Nickolas Demer on Doing Business with Energy (2.33 MB) More Documents & Publications Business Opportunity Session July 29 2013 December 12, 2013 Business

  11. Precipitating clouds

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    A suggestion for a new focus on cloud microphysical process study in the ARM program 1. Retrieving precipitating mixed- phase cloud properties Zhien Wang University of Wyoming zwang@uwyo.edu Retrieving Precipitating Mixed-phase Cloud Properties Global distribution of supercooled water topped stratiform clouds (top > 1 km and length> 14km) Most of them are mixed-phase with precipitation or virga An multiple sensor based approach to provide water phase as well as ice phase properties

  12. OPEN HOUSE - Climate Prisms: Arctic

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    OPEN HOUSE - Climate Prisms: Arctic OPEN HOUSE - Climate Prisms: Arctic WHEN: Jul 17, 2015 12:00 PM - 1:00 PM WHERE: Bradbury Science Museum 1350 Central Ave, Los Alamos, NM 87544, ...

  13. Cloud-Scale Vertical Velocity and Turbulent Dissipation Rate Retrievals

    SciTech Connect (OSTI)

    Shupe, Matthew

    2013-05-22

    Time-height fields of retrieved in-cloud vertical wind velocity and turbulent dissipation rate, both retrieved primarily from vertically-pointing, Ka-band cloud radar measurements. Files are available for manually-selected, stratiform, mixed-phase cloud cases observed at the North Slope of Alaska (NSA) site during periods covering the Mixed-Phase Arctic Cloud Experiment (MPACE, late September through early November 2004) and the Indirect and Semi-Direct Aerosol Campaign (ISDAC, April-early May 2008). These time periods will be expanded in a future submission.

  14. Cloud-Scale Vertical Velocity and Turbulent Dissipation Rate Retrievals

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Shupe, Matthew

    Time-height fields of retrieved in-cloud vertical wind velocity and turbulent dissipation rate, both retrieved primarily from vertically-pointing, Ka-band cloud radar measurements. Files are available for manually-selected, stratiform, mixed-phase cloud cases observed at the North Slope of Alaska (NSA) site during periods covering the Mixed-Phase Arctic Cloud Experiment (MPACE, late September through early November 2004) and the Indirect and Semi-Direct Aerosol Campaign (ISDAC, April-early May 2008). These time periods will be expanded in a future submission.

  15. ARM - Measurement - Cloud size

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    measurements as cloud thickness, cloud area, and cloud aspect ratio. Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the...

  16. Parameterizing Size Distribution in Ice Clouds

    SciTech Connect (OSTI)

    DeSlover, Daniel; Mitchell, David L.

    2009-09-25

    PARAMETERIZING SIZE DISTRIBUTIONS IN ICE CLOUDS David L. Mitchell and Daniel H. DeSlover ABSTRACT An outstanding problem that contributes considerable uncertainty to Global Climate Model (GCM) predictions of future climate is the characterization of ice particle sizes in cirrus clouds. Recent parameterizations of ice cloud effective diameter differ by a factor of three, which, for overcast conditions, often translate to changes in outgoing longwave radiation (OLR) of 55 W m-2 or more. Much of this uncertainty in cirrus particle sizes is related to the problem of ice particle shattering during in situ sampling of the ice particle size distribution (PSD). Ice particles often shatter into many smaller ice fragments upon collision with the rim of the probe inlet tube. These small ice artifacts are counted as real ice crystals, resulting in anomalously high concentrations of small ice crystals (D < 100 m) and underestimates of the mean and effective size of the PSD. Half of the cirrus cloud optical depth calculated from these in situ measurements can be due to this shattering phenomenon. Another challenge is the determination of ice and liquid water amounts in mixed phase clouds. Mixed phase clouds in the Arctic contain mostly liquid water, and the presence of ice is important for determining their lifecycle. Colder high clouds between -20 and -36 oC may also be mixed phase but in this case their condensate is mostly ice with low levels of liquid water. Rather than affecting their lifecycle, the presence of liquid dramatically affects the cloud optical properties, which affects cloud-climate feedback processes in GCMs. This project has made advancements in solving both of these problems. Regarding the first problem, PSD in ice clouds are uncertain due to the inability to reliably measure the concentrations of the smallest crystals (D < 100 m), known as the small mode. Rather than using in situ probe measurements aboard aircraft, we employed a treatment of ice cloud

  17. Testing cloud microphysics parameterizations in NCAR CAM5 with ISDAC and M-PACE observations

    SciTech Connect (OSTI)

    Liu X.; Lin W.; Xie, S.; Boyle, J.; Klein, S. A.; Shi, X.; Wang, Z.; Ghan, S. J.; Earle, M.; Liu, P. S. K.; Zelenyuk, A.

    2011-12-24

    Arctic clouds simulated by the National Center for Atmospheric Research (NCAR) Community Atmospheric Model version 5 (CAM5) are evaluated with observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Indirect and Semi-Direct Aerosol Campaign (ISDAC) and Mixed-Phase Arctic Cloud Experiment (M-PACE), which were conducted at its North Slope of Alaska site in April 2008 and October 2004, respectively. Model forecasts for the Arctic spring and fall seasons performed under the Cloud-Associated Parameterizations Testbed framework generally reproduce the spatial distributions of cloud fraction for single-layer boundary-layer mixed-phase stratocumulus and multilayer or deep frontal clouds. However, for low-level stratocumulus, the model significantly underestimates the observed cloud liquid water content in both seasons. As a result, CAM5 significantly underestimates the surface downward longwave radiative fluxes by 20-40 W m{sup -2}. Introducing a new ice nucleation parameterization slightly improves the model performance for low-level mixed-phase clouds by increasing cloud liquid water content through the reduction of the conversion rate from cloud liquid to ice by the Wegener-Bergeron-Findeisen process. The CAM5 single-column model testing shows that changing the instantaneous freezing temperature of rain to form snow from -5 C to -40 C causes a large increase in modeled cloud liquid water content through the slowing down of cloud liquid and rain-related processes (e.g., autoconversion of cloud liquid to rain). The underestimation of aerosol concentrations in CAM5 in the Arctic also plays an important role in the low bias of cloud liquid water in the single-layer mixed-phase clouds. In addition, numerical issues related to the coupling of model physics and time stepping in CAM5 are responsible for the model biases and will be explored in future studies.

  18. Testing Cloud Microphysics Parameterizations in NCAR CAM5 with ISDAC and M-PACE Observations

    SciTech Connect (OSTI)

    Liu, Xiaohong; Xie, Shaocheng; Boyle, James; Klein, Stephen A.; Shi, Xiangjun; Wang, Zhien; Lin, Wuyin; Ghan, Steven J.; Earle, Michael; Liu, Peter; Zelenyuk, Alla

    2011-12-24

    Arctic clouds simulated by the NCAR Community Atmospheric Model version 5 (CAM5) are evaluated with observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Indirect and Semi-Direct Aerosol Campaign (ISDAC) and Mixed-Phase Arctic Cloud Experiment (M-PACE), which were conducted at its North Slope of Alaska site in April 2008 and October 2004, respectively. Model forecasts for the Arctic Spring and Fall seasons performed under the Cloud- Associated Parameterizations Testbed (CAPT) framework generally reproduce the spatial distributions of cloud fraction for single-layer boundary layer mixed-phase stratocumulus, and multilayer or deep frontal clouds. However, for low-level clouds, the model significantly underestimates the observed cloud liquid water content in both seasons and cloud fraction in the Spring season. As a result, CAM5 significantly underestimates the surface downward longwave (LW) radiative fluxes by 20-40 W m-2. The model with a new ice nucleation parameterization moderately improves the model simulations by increasing cloud liquid water content in mixed-phase clouds through the reduction of the conversion rate from cloud liquid to ice by the Wegener-Bergeron- Findeisen (WBF) process. The CAM5 single column model testing shows that change in the homogeneous freezing temperature of rain to form snow from -5 C to -40 C has a substantial impact on the modeled liquid water content through the slowing-down of liquid and rain-related processes. In contrast, collections of cloud ice by snow and cloud liquid by rain are of minor importance for single-layer boundary layer mixed-phase clouds in the Arctic.

  19. Downloaded 01 Jul 2002 to 128.104.222.103. Redistribution subject to AIP license

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    1 Jul 2002 to 128.104.222.103. Redistribution subject to AIP license or copyright, see http://ojps.aip.org/pop/popcr.jsp Downloaded 01 Jul 2002 to 128.104.222.103. Redistribution subject to AIP license or copyright, see http://ojps.aip.org/pop/popcr.jsp Downloaded 01 Jul 2002 to 128.104.222.103. Redistribution subject to AIP license or copyright, see http://ojps.aip.org/pop/popcr.jsp Downloaded 01 Jul 2002 to 128.104.222.103. Redistribution subject to AIP license or copyright, see

  20. Downloaded 10 Jan 2005 to 128.104.223.90. Redistribution subject to AIP license

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    10 Jan 2005 to 128.104.223.90. Redistribution subject to AIP license or copyright, see http://pof.aip.org/pof/copyright.jsp Downloaded 10 Jan 2005 to 128.104.223.90. Redistribution subject to AIP license or copyright, see http://pof.aip.org/pof/copyright.jsp Downloaded 10 Jan 2005 to 128.104.223.90. Redistribution subject to AIP license or copyright, see http://pof.aip.org/pof/copyright.jsp Downloaded 10 Jan 2005 to 128.104.223.90. Redistribution subject to AIP license or copyright, see

  1. Arctic ice islands

    SciTech Connect (OSTI)

    Sackinger, W.M.; Jeffries, M.O.; Lu, M.C.; Li, F.C.

    1988-01-01

    The development of offshore oil and gas resources in the Arctic waters of Alaska requires offshore structures which successfully resist the lateral forces due to moving, drifting ice. Ice islands are floating, a tabular icebergs, up to 60 meters thick, of solid ice throughout their thickness. The ice islands are thus regarded as the strongest ice features in the Arctic; fixed offshore structures which can directly withstand the impact of ice islands are possible but in some locations may be so expensive as to make oilfield development uneconomic. The resolution of the ice island problem requires two research steps: (1) calculation of the probability of interaction between an ice island and an offshore structure in a given region; and (2) if the probability if sufficiently large, then the study of possible interactions between ice island and structure, to discover mitigative measures to deal with the moving ice island. The ice island research conducted during the 1983-1988 interval, which is summarized in this report, was concerned with the first step. Monte Carlo simulations of ice island generation and movement suggest that ice island lifetimes range from 0 to 70 years, and that 85% of the lifetimes are less then 35 years. The simulation shows a mean value of 18 ice islands present at any time in the Arctic Ocean, with a 90% probability of less than 30 ice islands. At this time, approximately 34 ice islands are known, from observations, to exist in the Arctic Ocean, not including the 10-meter thick class of ice islands. Return interval plots from the simulation show that coastal zones of the Beaufort and Chukchi Seas, already leased for oil development, have ice island recurrences of 10 to 100 years. This implies that the ice island hazard must be considered thoroughly, and appropriate safety measures adopted, when offshore oil production plans are formulated for the Alaskan Arctic offshore. 132 refs., 161 figs., 17 tabs.

  2. Download the SunShot Initiative 2014 Portfolio | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Download the SunShot Initiative 2014 Portfolio Download the SunShot Initiative 2014 Portfolio SunShot Initiative_Tackling Challenges in Solar_2014 Portfolio.pdf (8.3 MB) More Documents & Publications 2014 SunShot Initiative Portfolio Book: Tackling Challenges in Solar Energy 2014 SunShot Initiative Portfolio Book: Photovoltaics 2014 SunShot Initiative Portfolio Book: Systems Integration

  3. Climate Perspectives: Change in the Terrestrial Arctic

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Perspectives: Change in the Terrestrial Arctic Climate Perspectives An interactive exploration of Arctic climate science through prisms of the visual arts, literary arts, info-vis, ...

  4. ARM - Measurement - Cloud type

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Measurement : Cloud type Cloud type such as cirrus, stratus, cumulus etc Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the...

  5. CloudSat as a Global Radar Calibrator

    SciTech Connect (OSTI)

    Protat, Alain; Bouniol, Dominique; O'Connor, E. J.; Baltink, Henk K.; Verlinde, J.; Widener, Kevin B.

    2011-03-01

    The calibration of the CloudSat spaceborne cloud radar has been thoroughly assessed using very accurate internal link budgets before launch, comparisons with predicted ocean surface backscatter at 94 GHz, direct comparisons with airborne cloud radars, and statistical comparisons with ground-based cloud radars at different locations of the world. It is believed that the calibration of CloudSat is accurate to within 0.5 to 1 dB. In the present paper it is shown that an approach similar to that used for the statistical comparisons with ground-based radars can now be adopted the other way around to calibrate other ground-based or airborne radars against CloudSat and / or detect anomalies in long time series of ground-based radar measurements, provided that the calibration of CloudSat is followed up closely (which is the case). The power of using CloudSat as a Global Radar Calibrator is demonstrated using the Atmospheric Radiation Measurement cloud radar data taken at Barrow, Alaska, the cloud radar data from the Cabauw site, The Netherlands, and airborne Doppler cloud radar measurements taken along the CloudSat track in the Arctic by the RASTA (Radar SysTem Airborne) cloud radar installed in the French ATR-42 aircraft for the first time. It is found that the Barrow radar data in 2008 are calibrated too high by 9.8 dB, while the Cabauw radar data in 2008 are calibrated too low by 8.0 dB. The calibration of the RASTA airborne cloud radar using direct comparisons with CloudSat agrees well with the expected gains and losses due to the change in configuration which required verification of the RASTA calibration.

  6. Dispelling Clouds of Uncertainty

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Lewis, Ernie; Teixeira, João

    2015-06-15

    How do you build a climate model that accounts for cloud physics and the transitions between cloud regimes? Use MAGIC.

  7. Arctic Sea ice model sensitivities.

    SciTech Connect (OSTI)

    Peterson, Kara J.; Bochev, Pavel Blagoveston; Paskaleva, Biliana Stefanova

    2010-12-01

    Arctic sea ice is an important component of the global climate system and, due to feedback effects, the Arctic ice cover is changing rapidly. Predictive mathematical models are of paramount importance for accurate estimates of the future ice trajectory. However, the sea ice components of Global Climate Models (GCMs) vary significantly in their prediction of the future state of Arctic sea ice and have generally underestimated the rate of decline in minimum sea ice extent seen over the past thirty years. One of the contributing factors to this variability is the sensitivity of the sea ice state to internal model parameters. A new sea ice model that holds some promise for improving sea ice predictions incorporates an anisotropic elastic-decohesive rheology and dynamics solved using the material-point method (MPM), which combines Lagrangian particles for advection with a background grid for gradient computations. We evaluate the variability of this MPM sea ice code and compare it with the Los Alamos National Laboratory CICE code for a single year simulation of the Arctic basin using consistent ocean and atmospheric forcing. Sensitivities of ice volume, ice area, ice extent, root mean square (RMS) ice speed, central Arctic ice thickness,and central Arctic ice speed with respect to ten different dynamic and thermodynamic parameters are evaluated both individually and in combination using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). We find similar responses for the two codes and some interesting seasonal variability in the strength of the parameters on the solution.

  8. Arctic Clouds Infrared Imaging Field Campaign Report (Technical...

    Office of Scientific and Technical Information (OSTI)

    This objective was successfully completed with a comparison of the two-year data set calibrated with and without the onboard blackbody. The two different calibration methods ...

  9. Simulating Arctic mixed-phase clouds: Sensitivity to environmental

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Simplifying Solar Project Financing Simplifying Solar Project Financing January 16, 2014 - 10:05am Addthis The installation of this 2,244-panel photovoltaic system on Pinnacle Charter School in Denver, Colorado was fully funded by 456 American investors in 10 days. The project is expected to save the school $1.6 million in electricity costs over the next ten years.| Photo courtesy of Mosaic The installation of this 2,244-panel photovoltaic system on Pinnacle Charter School in Denver, Colorado

  10. Relationship Between Arctic Clouds and Synoptic-Scale Variability

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    strategyID strategyTitle decision Date RelatedUIIs ombInitiative useOfSavingsAvoidance netOr Gross amountType FY2012 Amount FY2013 Amount FY2014 Amount FY2015 Amount FY2016 Amount 2 Fossil Energy's (FE) Rocky Mountain Oilfield Test Center 11/01/2011 019-000000236 Other Per Congressional direction, RMOTC was decommissioned in FY2014 and the field site facility is closed. The Casper, Wyoming site (administrative office) reduced IT personnel by 2 FTEs as part of the disposition plan. DOE will

  11. Using Doppler spectra to separate hydrometeor populations and analyze ice precipitation in multilayered mixed-phase clouds

    SciTech Connect (OSTI)

    Rambukkange, Mahlon P.; Verlinde, J.; Eloranta, E. W.; Flynn, Connor J.; Clothiaux, Eugene E.

    2011-01-31

    Multimodality of cloud radar Doppler spectra is used to partition cloud particle phases and to separate distinct ice populations in the radar sample volume, thereby facilitating analysis of individual ice showers in multilayered mixed-phase clouds. A 35-GHz cloud radar located at Barrow, Alaska, during the Mixed-Phase Arctic Cloud Experiment collected the Doppler spectra. Data from a pair of collocated depolarization lidars confirmed the presence of two liquid cloud layers reported in this study. Surprisingly, both of these cloud layers were embedded in ice precipitation yet maintained their liquid. Our spectral separation of the ice precipitation yielded two distinct ice populations: ice initiated within the two liquid cloud layers and ice precipitation formed in higher cloud layers. Comparisons of ice fall velocity versus radar reflectivity relationships derived for distinct showers reveal that a single relationship might not properly represent the ice showers during this period.

  12. View and download all Security Smarts for your safety and security meetings

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    and download all Security Smarts for your safety and security meetings http://int.lanl.gov/security/documents/index.shtml#security-smarts Note: Security Smarts are current as of the date of publication. SecuritySmart Proof of United States Citizenship for the Badge Offce The Badge Offce is required by law to verify US citizenship. The following lists the acceptable documents that individuals can bring to the Badge Offce. Native-born US Citizens Primary Evidence For an individual born in the

  13. Version 24 of the Composite Materials Database Now Available for Download

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    24 of the Composite Materials Database Now Available for Download - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary Power Energy Conversion Efficiency Solar Energy Wind Energy Water Power Supercritical CO2 Geothermal Natural Gas Safety, Security & Resilience of the Energy Infrastructure Energy Storage Nuclear Power & Engineering Grid Modernization Battery Testing Nuclear Energy

  14. 3rd Annual Arctic Encounter Symposium Seattle

    Broader source: Energy.gov [DOE]

    The Arctic Encounter Symposium will convene policymakers, industry leaders, and leading experts to confront the leading issues in Arctic policy, innovation, and development. The two-day program includes two keynote luncheons, expert plenary sessions and breakout sessions.

  15. Time varying arctic climate change amplification

    SciTech Connect (OSTI)

    Chylek, Petr; Dubey, Manvendra K; Lesins, Glen; Wang, Muyin

    2009-01-01

    During the past 130 years the global mean surface air temperature has risen by about 0.75 K. Due to feedbacks -- including the snow/ice albedo feedback -- the warming in the Arctic is expected to proceed at a faster rate than the global average. Climate model simulations suggest that this Arctic amplification produces warming that is two to three times larger than the global mean. Understanding the Arctic amplification is essential for projections of future Arctic climate including sea ice extent and melting of the Greenland ice sheet. We use the temperature records from the Arctic stations to show that (a) the Arctic amplification is larger at latitudes above 700 N compared to those within 64-70oN belt, and that, surprisingly; (b) the ratio of the Arctic to global rate of temperature change is not constant but varies on the decadal timescale. This time dependence will affect future projections of climate changes in the Arctic.

  16. Arctic & Offshore Technical Data System

    Energy Science and Technology Software Center (OSTI)

    1990-07-01

    AORIS is a computerized information system to assist the technology and planning community in the development of Arctic oil and gas resources. In general, AORIS is geographically dependent and, where possible, site specific. The main topics are sea ice, geotechnology, oceanography, meteorology, and Arctic engineering, as they relate to such offshore oil and gas activities as exploration, production, storage, and transportation. AORIS consists of a directory component that identifies 85 Arctic energy-related databases and tellsmore » how to access them; a bibliographic/management information system or bibliographic component containing over 8,000 references and abstracts on Arctic energy-related research; and a scientific and engineering information system, or data component, containing over 800 data sets, in both tabular and graphical formats, on sea ice characteristics taken from the bibliographic citations. AORIS also contains much of the so-called grey literature, i.e., data and/or locations of Arctic data collected, but never published. The three components are linked so the user may easily move from one component to another. A generic information system is provided to allow users to create their own information systems. The generic programs have the same query and updating features as AORIS, except that there is no directory component.« less

  17. Arctic & Offshore Technical Data System

    Energy Science and Technology Software Center (OSTI)

    1990-07-01

    AORIS is a computerized information system to assist the technology and planning community in the development of Arctic oil and gas resources. In general, AORIS is geographically dependent and, where possible, site specific. The main topics are sea ice, geotechnology, oceanography, meteorology, and Arctic engineering, as they relate to such offshore oil and gas activities as exploration, production, storage, and transportation. AORIS consists of a directory component that identifies 85 Arctic energy-related databases and tellsmorehow to access them; a bibliographic/management information system or bibliographic component containing over 8,000 references and abstracts on Arctic energy-related research; and a scientific and engineering information system, or data component, containing over 800 data sets, in both tabular and graphical formats, on sea ice characteristics taken from the bibliographic citations. AORIS also contains much of the so-called grey literature, i.e., data and/or locations of Arctic data collected, but never published. The three components are linked so the user may easily move from one component to another. A generic information system is provided to allow users to create their own information systems. The generic programs have the same query and updating features as AORIS, except that there is no directory component.less

  18. Climate change and the Arctic

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Climate change and the Arctic Climate change and the Arctic WHEN: May 19, 2016 5:30 PM - 7:00 PM WHERE: UnQuarked Wine Room 145 Central Park Square, Los Alamos, New Mexico 87544 USA CONTACT: Linda Anderman (505) 665-9196 CATEGORY: Bradbury INTERNAL: Calendar Login Event Description Join us for convivial discussion on May 19 at 5:30 p.m. at UnQuarked Cathy Wilson, who heads the Lab's Atmosphere, Climate and Ecosystem Science team, is working to better understand what happens when warming climate

  19. Dispelling Clouds of Uncertainty

    SciTech Connect (OSTI)

    Lewis, Ernie; Teixeira, João

    2015-06-15

    How do you build a climate model that accounts for cloud physics and the transitions between cloud regimes? Use MAGIC.

  20. ARM - Measurement - Cloud location

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    point in space and time, typically expressed as a binary cloud mask. Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the...

  1. Russian anthropogenic black carbon: Emission reconstruction and Arctic black carbon simulation

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Huang, Kan; Fu, Joshua S.; Prikhodko, Vitaly Y.; Storey, John M.; Romanov, Alexander; Hodson, Elke L.; Cresko, Joe; Morozova, Irina; Ignatieva, Yulia; Cabaniss, John

    2015-10-02

    , and Tiksi) in the Arctic Circle, surface BC simulations are improved the most during the Arctic haze periods (October–March). The poor performance of Arctic BC simulations in previous studies may be partly ascribed to the Russian BC emissions built on out-of-date and/or missing information, which could result in biases to both emission rates and the spatial distribution of emissions. Finally, this study highlights that the impact of Russian emissions on the Arctic haze has likely been underestimated, and its role in the Arctic climate system needs to be reassessed. The Russian black carbon emission source data generated in this study can be obtained via http://abci.ornl.gov/download.shtml or http://acs.engr.utk.edu/Data.php.« less

  2. Russian anthropogenic black carbon: Emission reconstruction and Arctic black carbon simulation

    SciTech Connect (OSTI)

    Huang, Kan; Fu, Joshua S.; Prikhodko, Vitaly Y.; Storey, John M.; Romanov, Alexander; Hodson, Elke L.; Cresko, Joe; Ignatieva, Yulia; Cabaniss, John

    2015-10-02

    Tiksi) in the Arctic Circle, surface BC simulations are improved the most during the Arctic haze periods (October–March). The poor performance of Arctic BC simulations in previous studies may be partly ascribed to the Russian BC emissions built on out-of-date and/or missing information, which could result in biases to both emission rates and the spatial distribution of emissions. Finally, this study highlights that the impact of Russian emissions on the Arctic haze has likely been underestimated, and its role in the Arctic climate system needs to be reassessed. The Russian black carbon emission source data generated in this study can be obtained via http://abci.ornl.gov/download.shtml or http://acs.engr.utk.edu/Data.php.

  3. Science Cloud 2011

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Science Cloud 2011 Science Cloud 2011 June 17, 2011 The Magellan teams at NERSC and Argonne recently presented a joint paper detailing their progress and conclusions. At Science Cloud 2011: The Second Workshop on Scientific Cloud Computing, in a paper titled "Magellan: Experiences from a Science Cloud" (PDF, 320KB), lead author Lavanya Ramakrishnan outlined the groups' most recent achievements and conclusions, including a successful run of real-time data analysis for the STAR

  4. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Intercomparison of Model Simulations of Arctic Mixed-Phase Clouds Download a printable PDF Submitter: Morrison, H. C., NCAR Area of Research: Cloud Distributions/Characterizations Working Group(s): Cloud Life Cycle Journal Reference: Morrison H, P Zuidema, AS Ackerman, A Avramov, G de Boer, J Fan, AM Fridlind, T Hashino, JY Harrington, Y Luo, M Ovchinnikov, and B Shipway. 2011. "Intercomparison of cloud model simulations of Arctic mixed-phase boundary layer clouds observed during

  5. Short-Term Arctic Cloud Statistics at NSA from the Infrared Cloud Imager

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    November 2002 1 Short-Term Energy Outlook November 2002 Overview World Oil Markets: During the past 3-4 months, OPEC 10 production has risen more quickly than projected, thus reducing upward pressure on prices. More specifically, while the West Texas Intermediate (WTI) crude oil spot price averaged $28.84 in October, about $6.70 per barrel above the year-ago level (Figure 1), the WTI average price for fourth quarter 2002 is now projected to soften to $28.20, which is about $2 per barrel below

  6. Latitudinal distribution of the recent Arctic warming

    SciTech Connect (OSTI)

    Chylek, Petr; Lesins, Glen K; Wang, Muyin

    2010-12-08

    Increasing Arctic temperature, disappearance of Arctic sea ice, melting of the Greenland ice sheet, sea level rise, increasing strength of Atlantic hurricanes are these impending climate catastrophes supported by observations? Are the recent data really unprecedented during the observational records? Our analysis of Arctic temperature records shows that the Arctic and temperatures in the 1930s and 1940s were almost as high as they are today. We argue that the current warming of the Arctic region is affected more by the multi-decadal climate variability than by an increasing concentration of carbon dioxide. Unfortunately, none of the existing coupled Atmosphere-Ocean General Circulation Models used in the IPCC 2007 cIimate change assessment is able to reproduce neither the observed 20th century Arctic cIimate variability nor the latitudinal distribution of the warming.

  7. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Unraveling the Complexity of Arctic Mixed-Phase Clouds Download a printable PDF Submitter: Morrison, H. C., NCAR Area of Research: Radiation Processes Working Group(s): Cloud Life Cycle Journal Reference: Morrison H, G de Boer, G Feingold, J Harrington, M Shupe, and K Sulia. 2011. "Resilience of persistent Arctic mixed-phase clouds." Nature Geoscience, 5, doi:10.1038/ngeo1332. A conceptual model that illustrates the primary processes and basic physical structure of persistent Arctic

  8. Evaluating Model Parameterizations of Arctic Processes

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Model Parameterizations of Arctic Processes S. D. Greenberg, A. R. Metcalf, J. Y. Harrington, and J. Verlinde Pennsylvania State University University Park, Pennsylvania Introduction An understanding of the arctic climate system has become a high priority research area because of its importance to global climate change (IPCC 1990). Unfortunately, our studies of this region are in their infancy and we lack a broad knowledge of the Arctic. This deficiency is due to the scarcity of observations and

  9. Plant Root Characteristics and Dynamics in Arctic Tundra Ecosystems...

    Office of Scientific and Technical Information (OSTI)

    Dataset: Plant Root Characteristics and Dynamics in Arctic Tundra Ecosystems, 1960-2012 Citation Details In-Document Search Title: Plant Root Characteristics and Dynamics in Arctic...

  10. Arctic Stratus and Tropical Deep Convection. Integrating Measurements...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Arctic Stratus and Tropical Deep Convection. Integrating Measurements and Simulations Citation Details In-Document Search Title: Arctic Stratus and Tropical Deep ...

  11. The Arctic Lower Troposphere Observed Structure (ALTOS) Campaign...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: The Arctic Lower Troposphere Observed Structure (ALTOS) Campaign Citation Details In-Document Search Title: The Arctic Lower Troposphere Observed Structure ...

  12. The North Slope of Alaska and Adjacent Arctic Ocean (NSA/AAO) cart site begins operation: Collaboration with SHEBA and FIRE

    SciTech Connect (OSTI)

    Zak, D. B.; Church, H.; Ivey, M.; Yellowhorse, L.; Zirzow, J.; Widener, K. B.; Rhodes, P.; Turney, C.; Koontz, A.; Stamnes, K.; Storvold, R.; Eide, H. A.; Utley, P.; Eagan, R.; Cook, D.; Hart, D.; Wesely, M.

    2000-04-04

    Since the 1997 Atmospheric Radiation Measurement (ARM) Science Team Meeting, the North Slope of Alaska and Adjacent Arctic Ocean (NSA/AAO) Cloud and Radiation Testbed (CART) site has come into being. Much has happened even since the 1998 Science Team Meeting at which this paper was presented. To maximize its usefulness, this paper has been updated to include developments through July 1998.

  13. Determining Best Estimates and Uncertainties in Cloud Microphysical Parameters from ARM Field Data: Implications for Models, Retrieval Schemes and Aerosol-Cloud-Radiation Interactions

    SciTech Connect (OSTI)

    McFarquhar, Greg

    2015-12-28

    We proposed to analyze in-situ cloud data collected during ARM/ASR field campaigns to create databases of cloud microphysical properties and their uncertainties as needed for the development of improved cloud parameterizations for models and remote sensing retrievals, and for evaluation of model simulations and retrievals. In particular, we proposed to analyze data collected over the Southern Great Plains (SGP) during the Mid-latitude Continental Convective Clouds Experiment (MC3E), the Storm Peak Laboratory Cloud Property Validation Experiment (STORMVEX), the Small Particles in Cirrus (SPARTICUS) Experiment and the Routine AAF Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign, over the North Slope of Alaska during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) and the Mixed-Phase Arctic Cloud Experiment (M-PACE), and over the Tropical Western Pacific (TWP) during The Tropical Warm Pool International Cloud Experiment (TWP-ICE), to meet the following 3 objectives; derive statistical databases of single ice particle properties (aspect ratio AR, dominant habit, mass, projected area) and distributions of ice crystals (size distributions SDs, mass-dimension m-D, area-dimension A-D relations, mass-weighted fall speeds, single-scattering properties, total concentrations N, ice mass contents IWC), complete with uncertainty estimates; assess processes by which aerosols modulate cloud properties in arctic stratus and mid-latitude cumuli, and quantify aerosol’s influence in context of varying meteorological and surface conditions; and determine how ice cloud microphysical, single-scattering and fall-out properties and contributions of small ice crystals to such properties vary according to location, environment, surface, meteorological and aerosol conditions, and develop parameterizations of such effects.In this report we describe the accomplishments that we made on all 3 research objectives.

  14. ARM - Arctic Lower Troposphere Observed Structure (ALTOS)

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    These data will also help researchers gain a better understanding of the driving processes that control climate changes and determine the final state of the Arctic climate system....

  15. Cloud Properties Working Group Low Clouds Update

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Cloud Properties Working Group Low Clouds Update Low Clouds Update Jennifer Comstock Jennifer Comstock Dave Turner Dave Turner Andy Andy Vogelmann Vogelmann Instruments Instruments 90/150 GHz microwave radiometer 90/150 GHz microwave radiometer Deployed during COPS AMF Deployed during COPS AMF Exploring calibration w/ DPR ( Exploring calibration w/ DPR ( Crewell Crewell & & L L ö ö hnert hnert ) ) See COPS Breakout, Wednesday evening See COPS Breakout, Wednesday evening 183 GHz (GVR)

  16. Arctic Microclimate Activity.doc

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Arctic Microclimates ARM Education Program Objective To identify, measure, and average microclimatic temperatures in a particular region. Materials Large white piece of paper Pencils and erasers 1 thermometer per student or group Important Points to Understand Have you ever noticed how much cooler it is in the shade than in direct sunlight? Of course! Temperature differences within a small area are indications of microclimates: very small-scale climate conditions. The following are a few examp

  17. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    The Complexity of Arctic Clouds Download a printable PDF Submitter: Shupe, M., University of Colorado Area of Research: Cloud Processes Working Group(s): Cloud Life Cycle Journal Reference: Morrison H, G de Boer, G Feingold, J Harrington, M Shupe, and K Sulia. 2011. "Resilience of persistent Arctic mixed-phase clouds." Nature Geoscience, 5, doi:10.1038/ngeo1332. Arctic climate feedbacks: The processes that allow mixed-phased clouds to persist in the Arctic are surprisingly complex and

  18. ARM - Campaign Instrument - swfluxanal

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    MArine Stratus Radiation Aerosol and Drizzle (MASRAD) IOP Download Data Point Reyes CA, USA; Mobile Facility, 2005.03.14 - 2005.09.14 Mixed-Phase Arctic Cloud Experiment...

  19. Short-lived pollutants in the Arctic: their climate impact and possible mitigation strategies

    SciTech Connect (OSTI)

    Menon, Surabi; Quinn, P.K.; Bates, T.S.; Baum, E.; Doubleday, N.; Fiore, A.M.; Flanner, M.; Fridlind, A.; Garrett, T.J.; Koch, D.; Menon, S.; Shindell, D.; Stohl, A.; Warren, S.G.

    2007-09-24

    Several short-lived pollutants known to impact Arctic climate may be contributing to the accelerated rates of warming observed in this region relative to the global annually averaged temperature increase. Here, we present a summary of the short-lived pollutants that impact Arctic climate including methane, tropospheric ozone, and tropospheric aerosols. For each pollutant, we provide a description of the major sources and the mechanism of forcing. We also provide the first seasonally averaged forcing and corresponding temperature response estimates focused specifically on the Arctic. The calculations indicate that the forcings due to black carbon, methane, and tropospheric ozone lead to a positive surface temperature response indicating the need to reduce emissions of these species within and outside the Arctic. Additional aerosol species may also lead to surface warming if the aerosol is coincident with thin, low lying clouds. We suggest strategies for reducing the warming based on current knowledge and discuss directions for future research to address the large remaining uncertainties.

  20. ARM - Measurement - Cloud extinction

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Cloud extinction The removal of radiant energy from an incident beam by the process of cloud absorption andor ...

  1. Application of Stochastic Radiative Transfer Theory to the ARM Cloud-Radiative Parameterization Problem

    SciTech Connect (OSTI)

    Dana E. Veron

    2012-04-09

    This project had two primary goals: (1) development of stochastic radiative transfer as a parameterization that could be employed in an AGCM environment, and (2) exploration of the stochastic approach as a means for representing shortwave radiative transfer through mixed-phase layer clouds. To achieve these goals, climatology of cloud properties was developed at the ARM CART sites, an analysis of the performance of the stochastic approach was performed, a simple stochastic cloud-radiation parameterization for an AGCM was developed and tested, a statistical description of Arctic mixed phase clouds was developed and the appropriateness of stochastic approach for representing radiative transfer through mixed-phase clouds was assessed. Significant progress has been made in all of these areas and is detailed in the final report.

  2. Arctic Oil and Natural Gas Potential

    Reports and Publications (EIA)

    2009-01-01

    This paper examines the discovered and undiscovered Arctic oil and natural gas resource base with respect to their location and concentration. The paper also discusses the cost and impediments to developing Arctic oil and natural gas resources, including those issues associated with environmental habitats and political boundaries.

  3. Atmospheric Radiation Measurement (ARM) Data from Steamboat Springs, Colorado, for the Storm Peak Laboratory Cloud Property Validation Experiment (STORMVEX)

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    In October 2010, the initial deployment of the second ARM Mobile Facility (AMF2) took place at Steamboat Springs, Colorado, for the Storm Peak Laboratory Cloud Property Validation Experiment (STORMVEX). The objective of this field campaign was to obtain data about liquid and mixed-phase clouds using AMF2 instruments in conjunction with Storm Peak Laboratory (located at an elevation of 3220 meters on Mt. Werner), a cloud and aerosol research facility operated by the Desert Research Institute. STORMVEX datasets are freely available for viewing and download. Users are asked to register with the ARM Archive; the user's email address is used from that time forward as the login name.

  4. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    The Influence of Parameterized Ice Habit on Simulated Mixed-Phase Arctic Clouds Download a printable PDF Submitter: Harrington, J. Y., Pennsylvania State University Avramov, A., Columbia University Area of Research: Cloud Distributions/Characterizations Working Group(s): Cloud Modeling Journal Reference: Avramov A and JY Harrington. 2010. "Influence of parameterized ice habit on simulated mixed phase Arctic clouds." Journal of Geophysical Research - Atmospheres, 115, D03205,

  5. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Characterizing Clouds at Arctic Atmospheric Observatories Download a printable PDF Submitter: Shupe, M., University of Colorado Area of Research: Cloud Distributions/Characterizations Working Group(s): Cloud Life Cycle Journal Reference: Shupe MD, VP Walden, E Eloranta, T Uttal, JR Campbell, SM Starkweather, and M Shiobara. 2011. "Clouds at Arctic atmospheric observatories, part I: occurrence and macrophysical properties." Journal of Applied Meteorology and Climatology, 50(3), 626-644.

  6. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Snow Particle Observations in Arctic Clouds Download a printable PDF Submitter: Morrison, H. C., NCAR Area of Research: Cloud Distributions/Characterizations Working Group(s): Cloud Life Cycle Journal Reference: Morrison H, P Zuidema, GM McFarquhar, A Bansemer, and AJ Heymsfield. 2011. "Microphysical observations in shallow mixed-phase and deep frontal Arctic cloud systems." Quarterly Journal Royal Meteorological Society, 137(659), doi:10.1002/qj.840. Fitted size distribution intercept

  7. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Liquid Water the Key to Arctic Cloud Radiative Closure Download a printable PDF Submitter: Shupe, M., University of Colorado Area of Research: Cloud Distributions/Characterizations Working Group(s): Cloud Life Cycle Journal Reference: Shupe MD, DD Turner, A Zwink, MM Thieman, EJ Mlawer, and T Shippert. 2015. "Deriving Arctic cloud microphysics at Barrow, Alaska: Algorithms, results, and radiative closure." Journal of Applied Meteorology and Climatology, 54(7),

  8. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    The Role of Ice Nuclei Recycling in the Maintenance of Cloud Ice in Arctic Mixed-phase Stratocumulus Download a printable PDF Submitter: Solomon, A., NOAA/ESRL/Physical Sciences Division Feingold, G., NOAA - Earth System Research Laboratory Area of Research: Cloud-Aerosol-Precipitation Interactions Working Group(s): Cloud Life Cycle Journal Reference: Solomon A, G Feingold, and MD Shupe. 2015. "The role of ice nuclei recycling in the maintenance of cloud ice in Arctic mixed-phase

  9. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Characterizing Arctic Mixed-Phase Cloud Structure Download a printable PDF Submitter: Dong, X., University of Arizona Area of Research: Cloud Distributions/Characterizations Working Group(s): Cloud Life Cycle Journal Reference: Qiu S, X Dong, B Xi, and F Li. 2015. "Characterizing Arctic mixed-phase cloud structure and its relationship with humidity and temperature inversion using ARM NSA observations." Journal of Geophysical Research - Atmospheres, 120, 10.1002/2014JD023022. Figure 1.

  10. ARM - Measurement - Cloud fraction

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Flux Analysis SWFLUXANAL : Shortwave Flux Analysis TSI : Total Sky Imager UAV-EGRETT : UAV-Egrett WSI : Whole Sky Imager WSICLOUD : Whole Sky Imager Cloud Products ...

  11. ARM - Measurement - Cloud phase

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    that involves property descriptors such as stratus, cumulus, and cirrus. Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the...

  12. Finance Idol Word Cloud

    Broader source: Energy.gov [DOE]

    This word cloud represents the topics discussed during the Big and Small Ideas: How to Lower Solar Financing Costs breakout session at the SunShot Grand Challenge.

  13. Application of Stochastic Radiative Transfer Theory to the ARM Cloud-Radiative Parameterization Problem

    SciTech Connect (OSTI)

    Veron, Dana E

    2009-03-12

    This project had two primary goals: 1) development of stochastic radiative transfer as a parameterization that could be employed in an AGCM environment, and 2) exploration of the stochastic approach as a means for representing shortwave radiative transfer through mixed-phase layer clouds. To achieve these goals, an analysis of the performance of the stochastic approach was performed, a simple stochastic cloud-radiation parameterization for an AGCM was developed and tested, a statistical description of Arctic mixed phase clouds was developed and the appropriateness of stochastic approach for representing radiative transfer through mixed-phase clouds was assessed. Significant progress has been made in all of these areas and is detailed below.

  14. Posters A One-Dimensional Radiative Convective Model with Detailed Cloud Microphysics

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    5 Posters A One-Dimensional Radiative Convective Model with Detailed Cloud Microphysics J. Simmons, O. Lie-Svendsen, and K. Stamnes Geophysical Institute University of Alaska Fairbanks, Alaska The Arctic is a key element in determining the radiation budget of the earth. Within the polar regions, the net radiation (incoming solar radiation minus outgoing infrared radiation) is negative. To understand the role this energy deficit plays in the overall radiation budget, one must examine the

  15. Boundary Layer Cloud Turbulence Characteristics

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Boundary Layer Cloud Turbulence Characteristics Virendra Ghate Bruce Albrecht Parameter Observational Readiness (/10) Modeling Need (/10) Cloud Boundaries 9 9 Cloud Fraction Variance Skewness Up/Downdraft coverage Dominant Freq. signal Dissipation rate ??? Observation-Modeling Interface

  16. Cloud computing security.

    SciTech Connect (OSTI)

    Shin, Dongwan; Claycomb, William R.; Urias, Vincent E.

    2010-10-01

    Cloud computing is a paradigm rapidly being embraced by government and industry as a solution for cost-savings, scalability, and collaboration. While a multitude of applications and services are available commercially for cloud-based solutions, research in this area has yet to fully embrace the full spectrum of potential challenges facing cloud computing. This tutorial aims to provide researchers with a fundamental understanding of cloud computing, with the goals of identifying a broad range of potential research topics, and inspiring a new surge in research to address current issues. We will also discuss real implementations of research-oriented cloud computing systems for both academia and government, including configuration options, hardware issues, challenges, and solutions.

  17. Temperature, Water Vapor, and Clouds"

    Office of Scientific and Technical Information (OSTI)

    Radiometric Studies of Temperature, Water Vapor, and Clouds" Project ID: 0011106 ... measurements of column amounts of water vapor and cloud liquid has been well ...

  18. ARM - Measurement - Cloud effective radius

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    the number size distribution of cloud particles, whether liquid or ice. Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the...

  19. TC_CLOUD_REGIME.cdr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    intensity (e.g. May and Ballinger, 2007) Resulting Cloud Properties Examine rain DSD using polarimetric radar Examine ice cloud properties using MMCR and MPL Expect...

  20. Gwich'in Solar and Energy Efficiency in the Arctic

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Manager Dave P-M - Tanana Chiefs Conference, Rural Energy Coordinator Gwich'in Solar and Energy Efficiency in the Arctic Yukon Flats Yukon Flats Region: * Arctic Village * 10...

  1. Picture of the Week: Climate feedbacks from a warming arctic

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    8 Climate feedbacks from a warming arctic Los Alamos National Laboratory scientists work to understand the fate of this carbon using computer simulations such as this model of snowmelt draining from polygonal ground near Barrow, Alaska. April 26, 2015 Climate feedbacks from a warming arctic x Arctic soils currently store nearly 20 years worth of human emissions of carbon in frozen permafrost, but the Arctic is warming faster than most of the rest of the Earth, meaning that this carbon may soon

  2. Melting of ice wedges adds to arctic warming

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Can we someday predict earthquakes? Melting of ice wedges adds to arctic warming New ways of looking at seismic information and innovative laboratory experiments are offering tantalizing clues to what triggers earthquakes-and when. March 14, 2016 Ice throughout the Arctic is vanishing due to a rapidly warming climate. Ice throughout the Arctic is vanishing due to a rapidly warming climate. Melting of ice wedges adds to arctic warming Ice wedges are a particularly cool surface feature in the

  3. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    How Aerosols Affect Cloud Properties in Arctic Mixed-Phase Stratocumulus Download a printable PDF Submitter: McFarquhar, G., University of Illinois, Urbana Area of Research: Cloud-Aerosol-Precipitation Interactions Working Group(s): Cloud-Aerosol-Precipitation Interactions Journal Reference: Jackson RC, GM McFarquhar, AV Korolev, ME Earle, PS Liu, RP Lawson, S Brooks, M Wolde, A Laskin, and M Freer. 2012. "The dependence of ice microphysics on aerosol concentration in arctic mixed-phase

  4. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Linking Ice Nucleation to Aerosols and Its Impact on CAM5 Simulated Arctic Clouds and Radiation Download a printable PDF Submitter: Xie, S., Lawrence Livermore National Laboratory Area of Research: General Circulation and Single Column Models/Parameterizations Working Group(s): Cloud Life Cycle Journal Reference: Xie S, X Liu, C Zhao, and Y Zhang. 2013. "Sensitivity of CAM5 simulated arctic clouds and radiation to ice nucleation parameterization." Journal of Climate, 26(16),

  5. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Arctic Winter Frost Flowers Have Negligible Influence on Cloud Longwave Warming Download a printable PDF Submitter: Xu, L., University of California, San Diego Russell, L. M., Scripps Institution of Oceanography Area of Research: Aerosol Processes Working Group(s): Aerosol Life Cycle, Cloud-Aerosol-Precipitation Interactions Journal Reference: Xu L, LM Russell, RC Somerville, and PK Quinn. 2013. "Frost flower aerosol effects on Arctic wintertime longwave cloud radiative forcing."

  6. Magellan: A Cloud Computing Testbed

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Magellan News & Announcements Archive Petascale Initiative Exascale Computing APEX Home » R & D » Archive » Magellan: A Cloud Computing Testbed Magellan: A Cloud Computing Testbed Cloud computing is gaining a foothold in the business world, but can clouds meet the specialized needs of scientists? That was one of the questions NERSC's Magellan cloud computing testbed explored between 2009 and 2011. The goal of Magellan, a project funded through the U.S. Department of Energy (DOE) Oce

  7. Development and testing of an aerosol-stratus cloud parameterization scheme for middle and high latitudes

    SciTech Connect (OSTI)

    Olsson, P.Q.; Meyers, M.P.; Kreidenweis, S.; Cotton, W.R.

    1996-04-01

    The aim of this new project is to develop an aerosol/cloud microphysics parameterization of mixed-phase stratus and boundary layer clouds. Our approach is to create, test, and implement a bulk-microphysics/aerosol model using data from Atmospheric Radiation Measurement (ARM) Cloud and Radiation Testbed (CART) sites and large-eddy simulation (LES) explicit bin-resolving aerosol/microphysics models. The primary objectives of this work are twofold. First, we need the prediction of number concentrations of activated aerosol which are transferred to the droplet spectrum, so that the aerosol population directly affects the cloud formation and microphysics. Second, we plan to couple the aerosol model to the gas and aqueous-chemistry module that will drive the aerosol formation and growth. We begin by exploring the feasibility of performing cloud-resolving simulations of Arctic stratus clouds over the North Slope CART site. These simulations using Colorado State University`s regional atmospheric modeling system (RAMS) will be useful in designing the structure of the cloud-resolving model and in interpreting data acquired at the North Slope site.

  8. Arctic Energy Technology Development Laboratory

    SciTech Connect (OSTI)

    Sukumar Bandopadhyay; Charles Chamberlin; Robert Chaney; Gang Chen; Godwin Chukwu; James Clough; Steve Colt; Anthony Covescek; Robert Crosby; Abhijit Dandekar; Paul Decker; Brandon Galloway; Rajive Ganguli; Catherine Hanks; Rich Haut; Kristie Hilton; Larry Hinzman; Gwen Holdman; Kristie Holland; Robert Hunter; Ron Johnson; Thomas Johnson; Doug Kame; Mikhail Kaneveskly; Tristan Kenny; Santanu Khataniar; Abhijeet Kulkami; Peter Lehman; Mary Beth Leigh; Jenn-Tai Liang; Michael Lilly; Chuen-Sen Lin; Paul Martin; Pete McGrail; Dan Miller; Debasmita Misra; Nagendra Nagabhushana; David Ogbe; Amanda Osborne; Antoinette Owen; Sharish Patil; Rocky Reifenstuhl; Doug Reynolds; Eric Robertson; Todd Schaef; Jack Schmid; Yuri Shur; Arion Tussing; Jack Walker; Katey Walter; Shannon Watson; Daniel White; Gregory White; Mark White; Richard Wies; Tom Williams; Dennis Witmer; Craig Wollard; Tao Zhu

    2008-12-31

    The Arctic Energy Technology Development Laboratory was created by the University of Alaska Fairbanks in response to a congressionally mandated funding opportunity through the U.S. Department of Energy (DOE), specifically to encourage research partnerships between the university, the Alaskan energy industry, and the DOE. The enabling legislation permitted research in a broad variety of topics particularly of interest to Alaska, including providing more efficient and economical electrical power generation in rural villages, as well as research in coal, oil, and gas. The contract was managed as a cooperative research agreement, with active project monitoring and management from the DOE. In the eight years of this partnership, approximately 30 projects were funded and completed. These projects, which were selected using an industry panel of Alaskan energy industry engineers and managers, cover a wide range of topics, such as diesel engine efficiency, fuel cells, coal combustion, methane gas hydrates, heavy oil recovery, and water issues associated with ice road construction in the oil fields of the North Slope. Each project was managed as a separate DOE contract, and the final technical report for each completed project is included with this final report. The intent of this process was to address the energy research needs of Alaska and to develop research capability at the university. As such, the intent from the beginning of this process was to encourage development of partnerships and skills that would permit a transition to direct competitive funding opportunities managed from funding sources. This project has succeeded at both the individual project level and at the institutional development level, as many of the researchers at the university are currently submitting proposals to funding agencies, with some success.

  9. First observations of tracking clouds using scanning ARM cloud radars

    SciTech Connect (OSTI)

    Borque, Paloma; Giangrande, Scott; Kollias, Pavlos

    2014-12-01

    Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large drop formation (‘‘first echo’’). These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator (AW-RHI) observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning ARM cloud radar (SACR) at the DOE Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger scale context of the cloud field and to highlight the advantages of the SACR to detect the numerous, small, non-precipitating cloud elements. A new Cloud Identification and Tracking Algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2-D observations (30 sec) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived non-precipitating clouds having an apparent life cycle shorter than 15 minutes. The advantages and disadvantages of cloud tracking using an SACR are discussed.

  10. First observations of tracking clouds using scanning ARM cloud radars

    SciTech Connect (OSTI)

    Borque, Paloma; Giangrande, Scott; Kollias, Pavlos

    2014-12-01

    Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large drop formation (first echo). These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator (AW-RHI) observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning ARM cloud radar (SACR) at the DOE Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger scale context of the cloud field and to highlight the advantages of the SACR to detect the numerous, small, non-precipitating cloud elements. A new Cloud Identification and Tracking Algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2-D observations (30 sec) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived non-precipitating clouds having an apparent life cycle shorter than 15 minutes. The advantages and disadvantages of cloud tracking using an SACR are discussed.

  11. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Cloud-Radiation Effects on Sea Ice Loss Download a printable PDF Submitter: Stephens, G. L., Colorado State University Area of Research: Cloud Distributions/Characterizations Working Group(s): Cloud Properties Journal Reference: Kay, JE, T L'Ecuyer, A Gettelman, G Stephens, and C O'Dell. "The contribution of cloud and radiation anomalies to the 2007 Arctic sea ice extent minimum." To appear in Geophysical Research Letters. Clouds and downwelling radiation 2007-2006 differences (June

  12. Science on the Hill: Methane cloud hunting

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Methane cloud hunting Science on the Hill: Methane cloud hunting Los Alamos researchers go ... Science on the Hill: Methane cloud hunting When our team from Los Alamos National ...

  13. Evaluation of high‐level clouds in cloud resolving model...

    Office of Scientific and Technical Information (OSTI)

    Evaluation of high-level clouds in cloud resolving model 10.10022015MS000478 simulations with ARM and KWAJEX observations Key Points: * Two-moment microphysics improves simulated ...

  14. Opaque cloud detection

    DOE Patents [OSTI]

    Roskovensky, John K.

    2009-01-20

    A method of detecting clouds in a digital image comprising, for an area of the digital image, determining a reflectance value in at least three discrete electromagnetic spectrum bands, computing a first ratio of one reflectance value minus another reflectance value and the same two values added together, computing a second ratio of one reflectance value and another reflectance value, choosing one of the reflectance values, and concluding that an opaque cloud exists in the area if the results of each of the two computing steps and the choosing step fall within three corresponding predetermined ranges.

  15. Bringing Clouds into Focus

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Bringing Clouds into Focus Bringing Clouds into Focus A New Global Climate Model May Reduce the Uncertainty of Climate Forecasting May 11, 2010 Contact: John Hules, JAHules@lbl.gov , +1 510 486 6008 Randall-fig4.png The large data sets generated by the GCRM require new analysis and visualization capabilities. This 3D plot of vorticity isosurfaces was developed using VisIt, a 3D visualization tool with a parallel distributed architecture, which is being extended to support the geodesic grid used

  16. Cloud Based Applications and Platforms (Presentation)

    SciTech Connect (OSTI)

    Brodt-Giles, D.

    2014-05-15

    Presentation to the Cloud Computing East 2014 Conference, where we are highlighting our cloud computing strategy, describing the platforms on the cloud (including Smartgrid.gov), and defining our process for implementing cloud based applications.

  17. TWP Island Cloud Trail Studies

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    These island cloud trails have been observed from both the islands of Nauru and Manus, Papua New Guinea. Figure 2 shows an island cloud at Manus observed from MTI and from the ...

  18. ARM - Measurement - Images of Clouds

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    govMeasurementsImages of Clouds ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Images of Clouds Digital images of cloud scenes (various formats) from satellite, aircraft, and ground-based platforms. Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a

  19. ARM - Measurement - Total cloud water

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    cloud water ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Total cloud water The total concentration (mass/vol) of ice and liquid water particles in a cloud; this includes condensed water content (CWC). Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a

  20. National Strategy for the Arctic Region | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    National Strategy for the Arctic Region National Strategy for the Arctic Region The National Strategy for the Arctic Region (NSAR) outlines strategic priorities intended to position the United States to respond effectively to emerging opportunities in the region, while simultaneously pursuing efforts to protect and conserve its unique environment. Signed by President Obama on May 10, 2013, the National Strategy builds upon existing initiatives by federal, state, local, and tribal authorities;

  1. Leveraging Lighting for Energy Savings: GSA Northwest/Arctic Region |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy Leveraging Lighting for Energy Savings: GSA Northwest/Arctic Region Leveraging Lighting for Energy Savings: GSA Northwest/Arctic Region Case study describes how the Northwest/Arctic Region branch of the General Services Administration (GSA) improved safety and energy efficiency in its Fairbanks Federal Building parking garage used by federal employees, U.S. Marshals, and the District Court. A 74% savings was realized by replacing 220 high-pressure sodium fixtures with

  2. Potential Oil Production from the Coastal Plain of the Arctic...

    U.S. Energy Information Administration (EIA) Indexed Site

    Potential Oil Production from the Coastal Plain of the Arctic National Wildlife Refuge: ... Section 1002 of ANILCA deferred a decision on the management of oil and gas exploration ...

  3. The Rush to Exploit an Increasingly Ice-Free Arctic

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Rush to Exploit an Increasingly Ice-Free Arctic - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy ...

  4. An active atmospheric methane sink in high Arctic mineral cryosols...

    Office of Scientific and Technical Information (OSTI)

    conditions coupled with -omics analysis indicate (1) mineral cryosols in the Canadian high Arctic contain atmospheric CH-oxidizing bacteria; (2) the atmospheric CH uptake ...

  5. Potential Oil Production from the Coastal Plain of the Arctic...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Oil Production from the Coastal Plain of the Arctic National Wildlife Refuge: Updated Assessment 2. Analysis Discussion Resource Assessment The USGS most recent assessment of oil ...

  6. Potential Oil Production from the Coastal Plain of the Arctic...

    U.S. Energy Information Administration (EIA) Indexed Site

    Potential Oil Production from the Coastal Plain of the Arctic National Wildlife Refuge: Updated Assessment Executive Summary This Service Report, Potential Oil Production from the ...

  7. Potential Oil Production from the Coastal Plain of the Arctic...

    U.S. Energy Information Administration (EIA) Indexed Site

    Potential Oil Production from the Coastal Plain of the Arctic National Wildlife Refuge: ... of technically recoverable undiscovered oil are in the ANWR coastal plain, a 5 percent ...

  8. ARM - Publications: Science Team Meeting Documents: An Arctic...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    An Arctic Springtime Mixed-Phase Cloudy Boundary Layer observed during SHEBA Zuidema, Paquita RSMASMPO University of Miami Han, Yong NASA Goddard Space Flight Center Intrieri,...

  9. Arctic Haze: Effect of Anthropogenic and Biomass Burning

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Haze: Effect of Anthropogenic and Biomass Burning Aerosols Transported from Europe to the Arctic For original submission and image(s), see ARM Research Highlights http:...

  10. Plant Root Characteristics and Dynamics in Arctic Tundra Ecosystems...

    Office of Scientific and Technical Information (OSTI)

    and dynamics, and their role in key ecosystem processes in the Arctic. Authors: Sullivan, Paddy ; Sloan, Victoria ; Warren, Jeff ; McGuire, Dave ; Euskirchen, Eugenie ;...

  11. First observations of tracking clouds using scanning ARM cloud radars

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Borque, Paloma; Giangrande, Scott; Kollias, Pavlos

    2014-12-01

    Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large drop formation (‘‘first echo’’). These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator (AW-RHI) observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning ARM cloud radar (SACR) at the DOE Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger scale context of the cloud fieldmore » and to highlight the advantages of the SACR to detect the numerous, small, non-precipitating cloud elements. A new Cloud Identification and Tracking Algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2-D observations (30 sec) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived non-precipitating clouds having an apparent life cycle shorter than 15 minutes. The advantages and disadvantages of cloud tracking using an SACR are discussed.« less

  12. Climate-derived tensions in Arctic security.

    SciTech Connect (OSTI)

    Backus, George A.; Strickland, James Hassler

    2008-09-01

    Globally, there is no lack of security threats. Many of them demand priority engagement and there can never be adequate resources to address all threats. In this context, climate is just another aspect of global security and the Arctic just another region. In light of physical and budgetary constraints, new security needs must be integrated and prioritized with existing ones. This discussion approaches the security impacts of climate from that perspective, starting with the broad security picture and establishing how climate may affect it. This method provides a different view from one that starts with climate and projects it, in isolation, as the source of a hypothetical security burden. That said, the Arctic does appear to present high-priority security challenges. Uncertainty in the timing of an ice-free Arctic affects how quickly it will become a security priority. Uncertainty in the emergent extreme and variable weather conditions will determine the difficulty (cost) of maintaining adequate security (order) in the area. The resolution of sovereignty boundaries affects the ability to enforce security measures, and the U.S. will most probably need a military presence to back-up negotiated sovereignty agreements. Without additional global warming, technology already allows the Arctic to become a strategic link in the global supply chain, possibly with northern Russia as its main hub. Additionally, the multinational corporations reaping the economic bounty may affect security tensions more than nation-states themselves. Countries will depend ever more heavily on the global supply chains. China has particular needs to protect its trade flows. In matters of security, nation-state and multinational-corporate interests will become heavily intertwined.

  13. Critical Mechanisms for the Formation of Extreme Arctic Sea-Ice Extent in the Summers of 2007 and 1996

    SciTech Connect (OSTI)

    Dong, Xiquan; Zib, Benjamin J.; Xi, Baike; Stanfield, Ryan; Deng, Yi; Zhang, Xiangdong; Lin, B.; Long, Charles N.

    2014-07-29

    A warming Arctic climate is undergoing significant e 21 nvironmental change, most evidenced by the reduction of Arctic sea-ice extent during the summer. In this study, we examine two extreme anomalies of September sea-ice extent in 2007 and 1996, and investigate the impacts of cloud fraction (CF), atmospheric precipitable water vapor (PWV), downwelling longwave flux (DLF), surface air temperature (SAT), pressure and winds on the sea-ice variation in 2007 and 1996 using both satellite-derived sea-ice products and MERRA reanalysis. The area of the Laptev, East Siberian and West Chukchi seas (70-90oN, 90-180oE) has experienced the largest variation in sea-ice extent from year-to-year and defined here as the Area Of Focus (AOF). The record low September sea-ice extent in 2007 was associated with positive anomalies 30 of CF, PWV, DLF, and SAT over the AOF. Persistent anti-cyclone positioned over the Beaufort Sea coupled with low pressure over Eurasia induced easterly zonal and southerly meridional winds. In contrast, negative CF, PWV, DLF and SAT anomalies, as well as opposite wind patterns to those in 2007, characterized the 1996 high September sea-ice extent. Through this study, we hypothesize the following positive feedbacks of clouds, water vapor, radiation and atmospheric variables on the sea-ice retreat during the summer 2007. The record low sea-ice extent during the summer 2007 is initially triggered by the atmospheric circulation anomaly. The southerly winds across the Chukchi and East Siberian seas transport warm, moist air from the north Pacific, which is not only enhancing sea-ice melt across the AOF, but also increasing clouds. The positive cloud feedback results in higher SAT and more sea-ice melt. Therefore, 40 more water vapor could be evaporated from open seas and higher SAT to form more clouds, which will enhance positive cloud feedback. This enhanced positive cloud feedback will then further increase SAT and accelerate the sea-ice retreat during the

  14. Downloads | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    economy ---Fuel injection ---Heavy-duty vehicles ---Hybrid & electric vehicles ... design -Physics --Low-energy physics --Medium-energy physics --High-energy physics ...

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    & fuel cells ---Internal combustion ---Maglev systems ---Powertrain research ---Vehicle testing --Building design ---Construction ---Industrial heating & cooling...

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    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ---Industrial heating & cooling ---Industrial lighting --Manufacturing -Energy sources --Renewable energy ---Bioenergy ---Geothermal energy ---Hydropower ---Solar energy ---Wind...

  17. EERC Linux Download

    Broader source: Energy.gov [DOE]

    EERC computes an average annual escalation rate for a specified time period, which can be used as an escalation rate for contract payments in energy savings performance contracts and utility energy services contracts.

  18. downloadForm.asp

    Energy Savers [EERE]

    ... 17.33 12012 - Certified Occupational Therapist Assistant ... 15.86 12210 - Nuclear Medicine Technologist 34.45 12221 ... all contractors and subcontractors subject to this wage ...

  19. downloadForm.asp

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

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    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

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  3. Downloads | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... Bioluminescence more accurate, forgiving than typical targeting methods October 6, 2015 CNM Scientific Contact List A list of scientific contacts for the Center for Nanoscale ...

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  5. CACTUS Software Download

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

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  6. Downloads | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Technologies Available for Licensing Energy Storage Industrial & Manufacturing Processes ... reactor materials ----Nuclear system technologies & diagnostics -Energy ...

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  8. downloadForm.asp

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  11. NERSC Software Downloads

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  12. Downloads | Argonne National Laboratory

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    ... --Petascale & exascale computing --Statistics --Software development ... --Site renewable energy --Site energy & water conservation Operations -Leadership Institute ...

  13. Wind Software Downloads

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  14. Downloads | Argonne National Laboratory

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    --Environmental policy & planning --Geochemistry ... -Decision science --Emergency & disaster management ... most critical scientific problems, the Mathematics and ...

  15. Downloads | Argonne National Laboratory

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  16. Downloads | Argonne National Laboratory

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  17. Evaluation of high-level clouds in cloud resolving model simulations...

    Office of Scientific and Technical Information (OSTI)

    Title: Evaluation of high-level clouds in cloud resolving model simulations with ARM and KWAJEX observations: HIGH CLOUD IN CRM Authors: Liu, Zheng 1 ; Muhlbauer, Andreas 2 ; ...

  18. A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Riihimaki, Laura D.; Comstock, Jennifer M.; Anderson, Kevin K.; Holmes, Aimee; Luke, Edward

    2016-06-10

    Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty informationmore » on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.« less

  19. Assessing Cloud Spatial and Vertical Distribution with Infrared Cloud Analyzer

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Cloud Spatial and Vertical Distribution with Infrared Cloud Analyzer I. Genkova and C. N. Long Pacific Northwest National Laboratory Richland, Washington T. Besnard ATMOS SARL Le Mans, France D. Gillotay Institute d'Aeronomie Spatiale de Belgique Brussels, Belgium Introduction In the effort to resolve uncertainties about global climate change, the Atmospheric Radiation Measurement (ARM) Program (www.arm.gov) is improving the treatment of cloud radiative forcing and feedbacks in general

  20. National Strategy for the Arctic Region Stakeholder Outreach Meeting: Barrow

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) is announcing the second round of tribal consultations and stakeholder outreach meetings on the National Strategy for the Arctic Region (NSAR), 10-Year Plan to accelerate renewable energy deployment in the Arctic Region.

  1. National Strategy for the Arctic Region Stakeholder Outreach Meeting: Fairbanks

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) is announcing the second round of tribal consultations and stakeholder outreach meetings on the National Strategy for the Arctic Region (NSAR), 10-Year Plan to accelerate renewable energy deployment in the Arctic Region.

  2. National Strategy for the Arctic Region Stakeholder Outreach Meeting: Nome

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) is announcing the second round of tribal consultations and stakeholder outreach meetings on the National Strategy for the Arctic Region (NSAR), 10-Year Plan to accelerate renewable energy deployment in the Arctic Region.

  3. National Strategy for the Arctic Region Stakeholder Outreach Meeting: Bethel

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) is announcing the second round of tribal consultations and stakeholder outreach meetings on the National Strategy for the Arctic Region (NSAR), 10-Year Plan to accelerate renewable energy deployment in the Arctic Region.

  4. National Strategy for the Arctic Region Stakeholder Outreach Meeting: Anchorage

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) is announcing the second round of tribal consultations and stakeholder outreach meetings on the National Strategy for the Arctic Region (NSAR), 10-Year Plan to accelerate renewable energy deployment in the Arctic Region. The purpose of this round is to give feedback on the elements of the draft plan.

  5. National Strategy for the Arctic Tribal Consultation Session: Fairbanks

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) is announcing the second round of tribal consultations and stakeholder outreach meetings on the National Strategy for the Arctic Region (NSAR), 10-Year Plan to accelerate renewable energy deployment in the Arctic Region.

  6. National Strategy for the Arctic Region Tribal Consultation Session: Bethel

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) is announcing the second round of tribal consultations and stakeholder outreach meetings on the National Strategy for the Arctic Region (NSAR), 10-Year Plan to accelerate renewable energy deployment in the Arctic Region.

  7. National Strategy for the Arctic Region Tribal Consultation Session: Nome

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) is announcing the second round of tribal consultations and stakeholder outreach meetings on the National Strategy for the Arctic Region (NSAR), 10-Year Plan to accelerate renewable energy deployment in the Arctic Region.

  8. National Strategy for the Arctic Region Tribal Consultation Session: Barrow

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) is announcing the second round of tribal consultations and stakeholder outreach meetings on the National Strategy for the Arctic Region (NSAR), 10-Year Plan to accelerate renewable energy deployment in the Arctic Region.

  9. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Use of ARM Products in Reanalysis Applications and IPCC Model Assessment Download a printable PDF Submitter: Walsh, J. E., University of Illinois, Urbana Area of Research: Cloud Distributions/Characterizations Working Group(s): Cloud Modeling Journal Reference: Walsh, J. E., W. L. Chapman, and D. H. Portis: Arctic clouds and radiative fluxes in large-scale atmospheric reanalysis. Submitted to the Journal of Climate. Figure 1. Monthly mean cloud fraction is shown here from ARM-observations

  10. Research Highlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Putting the Pieces Together Download a printable PDF Submitter: Fan, J., Pacific Northwest National Laboratory Area of Research: Cloud Distributions/Characterizations Working Group(s): Cloud Life Cycle Journal Reference: Fan J, S Ghan, M Ovchinnikov, X Liu, P Rasch, and A Korolev. 2011. "Representation of arctic mixed-phase clouds and the Wegener-Bergeron-Findeisen process in climate models: Perspectives from a cloud-resolving study." Journal of Geophysical Research - Atmospheres, 116,

  11. Microsoft PowerPoint - LIQUID_CLOUD_SUMMARY.final.amv.ppt [Compatibility Mode]

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Phase Subgroup Liquid Phase Subgroup B k t S f B k t S f Breakout Summary from Breakout Summary from Fall 2006 CPWG Fall 2006 CPWG Fall 2006 CPWG Fall 2006 CPWG (With Updates) (With Updates) Dave Turner and Andy Vogelmann ( p ) ( p ) Cloud Property Working Group Breakout Session 26 M h 2007 26 March 2007 Arm Science Team Meeting Monterey, California Sub Sub- -group Agenda: 9 Nov 2006 group Agenda: 9 Nov 2006 Presentations (7) Presentations (7) Wang Wang - - Refine Arctic Microwave Radiometer LWP

  12. First observations of tracking clouds using scanning ARM cloud...

    Office of Scientific and Technical Information (OSTI)

    These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator ...

  13. TURBULENCE DECAY AND CLOUD CORE RELAXATION IN MOLECULAR CLOUDS

    SciTech Connect (OSTI)

    Gao, Yang; Law, Chung K.; Xu, Haitao

    2015-02-01

    The turbulent motion within molecular clouds is a key factor controlling star formation. Turbulence supports molecular cloud cores from evolving to gravitational collapse and hence sets a lower bound on the size of molecular cloud cores in which star formation can occur. On the other hand, without a continuous external energy source maintaining the turbulence, such as in molecular clouds, the turbulence decays with an energy dissipation time comparable to the dynamic timescale of clouds, which could change the size limits obtained from Jean's criterion by assuming constant turbulence intensities. Here we adopt scaling relations of physical variables in decaying turbulence to analyze its specific effects on the formation of stars. We find that the decay of turbulence provides an additional approach for Jeans' criterion to be achieved, after which gravitational infall governs the motion of the cloud core. This epoch of turbulence decay is defined as cloud core relaxation. The existence of cloud core relaxation provides a more complete understanding of the effect of the competition between turbulence and gravity on the dynamics of molecular cloud cores and star formation.

  14. Holistic Interactions of Shallow Clouds,

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems Research Instrumentation HI-SCALE will utilize the ARM Aerial Facility's Gulfstream-159 (G-1), as well as ground instrumentation located at the SGP megasite. 7e G-1 will complete transects over the site at multiple altitudes within the boundary layer, within clouds, and above clouds. 7e payload on the G-1 includes: * high frequency meteorological and radiation (both up and downwelling) measurements that also permit computing

  15. Cumulus Clouds and Reflected Sunlight

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Cumulus Clouds and Reflected Sunlight from Landsat ETM+ G. Wen and L. Oreopoulos National Aeronautics and Space Administration Goddard Space Flight Center University of Maryland Baltimore County Joint Center of Earth System Technology Greenbelt, Maryland R. F. Cahalan and S. C. Tsay National Aeronautics and Space Administration Goddard Space Flight Center Greenbelt, Maryland Introduction Cumulus clouds attenuate solar radiation casting shows on the ground. Cumulus clouds can also enhance solar

  16. ARM - Measurement - Cloud top height

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    RUC : Rapid Update Cycle Model Data Field Campaign Instruments CO2LIDAR : Carbon Dioxide Doppler Lidar MPLCMASK : Cloud mask from Micropulse Lidar VARANAL : Constrained...

  17. ARM - Measurement - Cloud condensation nuclei

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AOS : Aerosol Observing System CCN : Cloud Condensation Nuclei Particle Counter TDMA : Tandem Differential Mobility Analyzer Field Campaign Instruments AMT : Aerosol Modeling...

  18. ARM - Measurement - Cloud ice particle

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    : Lear Jet PARTIMG : Particle imager UAV-PROTEUS-MICRO : Proteus Cloud Microphysics ... particle imager MET : Surface Meteorological Instrumentation UAV-PROTEUS : UAV Proteus

  19. ARM - Measurement - Cloud droplet size

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Impactor MIRAI : JAMSTEC Research Vessel Mirai PDI : Phase Doppler Interferometer UAV-PROTEUS-MICRO : Proteus Cloud Microphysics Instruments SPEC-CPI : Stratton Park ...

  20. ARM - Measurement - Cloud optical depth

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    TWST : Three Waveband Spectrally-agile Technique Sensor WRF-CHEM : Weather Research and Forecasting (WRF) Model Output Value-Added Products LBTM-MINNIS : Minnis Cloud Products...

  1. Path to Economic Sovereignty: Arctic Opportunities

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    to Economic Sovereignty: Arctic Opportunities Presented by Kip Knudson Office of Alaska Governor Bill Walker Slide Deck prepared by Sean Skaling, Director, Alaska Energy Authority Photo by Chuck Berray 200 remote microgrids spread over large area  Population: 735,000  Area: 660,000 sq. miles  1.2 people/sq. mile  New Jersey has 1,000 times the density  About 200 stand-alone microgrid communities 3 Alaska Electrical Generation Railbelt 72% of Pop 76% of Energy Natural Gas*

  2. Widget:LogoCloud | Open Energy Information

    Open Energy Info (EERE)

    LogoCloud Jump to: navigation, search This widget adds css selectors and javascript for the Template:LogoCloud. For example: Widget:LogoCloud Retrieved from "http:...

  3. Zenith Radiance Retrieval of Cloud Properties

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    retrievals of cloud properties from the AMF/COPS campaign Preliminary retrievals of cloud properties from the AMF/COPS campaign Christine Chiu, UMBC/JCET Alexander Marshak, GSFC Yuri Knyazikhin, Boston University Warren Wiscombe, GSFC Christine Chiu, UMBC/JCET Alexander Marshak, GSFC Yuri Knyazikhin, Boston University Warren Wiscombe, GSFC The cloud optical properties of interest are: The cloud optical properties of interest are: * Cloud optical depth τ - the great unknown * Radiative cloud

  4. Predicting and validating the tracking of a Volcanic Ash Cloud during the 2006 Eruption of Mt. Augustine Volcano

    SciTech Connect (OSTI)

    Webley, Peter W.; Atkinson, D.; Collins, Richard L.; Dean, K.; Fochesatto, J.; Sassen, Kenneth; Cahill, Catherine F.; Prata, A.; Flynn, Connor J.; Mizutani, K.

    2008-11-01

    On 11 January 2006, Mount Augustine volcano in southern Alaska began erupting after 20-year repose. The Anchorage Forecast Office of the National Weather Service (NWS) issued an advisory on 28 January for Kodiak City. On 31 January, Alaska Airlines cancelled all flights to and from Anchorage after multiple advisories from the NWS for Anchorage and the surrounding region. The Alaska Volcano Observatory (AVO) had reported the onset of the continuous eruption. AVO monitors the approximately 100 active volcanoes in the Northern Pacific. Ash clouds from these volcanoes can cause serious damage to an aircraft and pose a serious threat to the local communities, and to transcontinental air traffic throughout the Arctic and sub-Arctic region. Within AVO, a dispersion model has been developed to track the dispersion of volcanic ash clouds. The model, Puff, was used operational by AVO during the Augustine eruptive period. Here, we examine the dispersion of a volcanic ash cloud from Mount Augustine across Alaska from 29 January through the 2 February 2006. We present the synoptic meteorology, the Puff predictions, and measurements from aerosol samplers, laser radar (or lidar) systems, and satellites. UAF aerosol samplers revealed the presence of volcanic aerosols at the surface at sites where Puff predicted the ash clouds movement. Remote sensing satellite data showed the development of the ash cloud in close proximity to the volcano and a sulfur-dioxide cloud further from the volcano consistent with the Puff predictions. Lidars showed the presence of volcanic aerosol with consistent characteristics aloft over Alaska and were capable of detecting the aerosol, even in the presence of scattered clouds and where the cloud is too thin/disperse to be detected by remote sensing satellite data. The lidar measurements revealed the different trajectories of ash consistent with the Puff predictions. Dispersion models provide a forecast of volcanic ash cloud movement that might be

  5. [Multifractal cloud properties data assessment

    SciTech Connect (OSTI)

    Gautier, C.; Ricchiazzi, P.; Peterson, P.; Lavallee, D. ); Frouin, R.; Lubin, D. ); Lovejoy, S. ); Schertzer, D. )

    1992-05-06

    Our group has been very active over the last year, analyzing a number of data sets to characterize multifractal cloud properties and assess the effects of clouds on surface radiation properties (spectral and broadband). The data sets analyzed include: AVHRR observations of clouds over the ocean, SPOT observations of clouds over the ocean, SSM/I observations of clouds over the ocean, pyranometer data with all-sky photographs, pyrgeometer data all-sky photographs, and spectral surface irradiance all-sky photographs. A number of radiative transfer computations have been performed to help in the interpretation of these observations or provide theoretical guidance for their analysis. Finally 4 number of radiative transfer models have been acquired and tested to prepare for the interpretation of ARM/CART data.

  6. Satellite determination of stratus cloud microphysical properties...

    Office of Scientific and Technical Information (OSTI)

    of liquid water path from SSMI, broadband albedo from ERBE, and cloud characteristics from ISCCP are used to study stratus regions. An average cloud liquid water path of ...

  7. Clouds Environmental Ltd | Open Energy Information

    Open Energy Info (EERE)

    Clouds Environmental Ltd Jump to: navigation, search Name: Clouds Environmental Ltd Place: Portsmouth, United Kingdom Zip: PO3 5EG Product: Independent consultancy specialising in...

  8. Radiative Effects of Cloud Inhomogeneity and

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Ackerman et al. 1999), to develop cloud statistics and improve the treatment of subgrid ... and Curry 1989; Liang and Wang 1997). Statistics of Subgrid Cloud Variability We have ...

  9. ARM - Field Campaign - Arctic Lower Troposphere Observed Structure (ALTOS)

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    govCampaignsArctic Lower Troposphere Observed Structure (ALTOS) Campaign Links Science Plan ALTOS Website Related Campaigns Supplement to Arctic Lower Troposphere Observed Structure (ALTOS) 2010.10.01, Verlinde, NSA Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Campaign : Arctic Lower Troposphere Observed Structure (ALTOS) 2010.10.01 - 2010.12.31 Website : http://www.arm.gov/campaigns/altos/ Lead Scientist : Johannes Verlinde Abstract NOTE: An

  10. ARM - Field Campaign - Supplement to Arctic Lower Troposphere Observed

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Structure (ALTOS) govCampaignsSupplement to Arctic Lower Troposphere Observed Structure (ALTOS) Related Campaigns Arctic Lower Troposphere Observed Structure (ALTOS) 2010.10.01, Verlinde, NSA Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Campaign : Supplement to Arctic Lower Troposphere Observed Structure (ALTOS) 2010.10.01 - 2010.12.31 Lead Scientist : Johannes Verlinde Abstract NOTE: An unfortunate incident in the early stages of ALTOS