National Library of Energy BETA

Sample records for vaisala ceilometer vceil

  1. ARM - PI Product - Vaisala CL51 ceilometer

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

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  2. ARM: ARSCL: multiple outputs from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

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

    Karen Johnson; Michael Jensen

    ARSCL: multiple outputs from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

  3. ARM: ARSCL: cloud boundaries from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

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

    Karen Johnson; Michael Jensen

    ARSCL: cloud boundaries from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

  4. ARM: ARSCL: cloud base height from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

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

    Karen Johnson; Michael Jensen

    ARSCL: cloud base height from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

  5. Impact of Vaisala Radiosonde Humidity Corrections on ARM IOP Data

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

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  6. ARM - Instrument - vceil

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

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  7. Balloon-borne sounding system (BBSS): Vaisala-processed winds, press., temp, and RH

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

    Coulter, Richard; Ritsche, Michael

    Balloon-borne sounding system (BBSS): Vaisala-processed winds, press., temp, and RH. The balloon-borne sounding system (SONDE) provides in situ measurements (vertical profiles) of both the thermodynamic state of the atmosphere, and the wind speed and direction.

  8. TOWARDS A CLOUD CEILOMETER NETWORK REPORTING MIXING LAYER HEIGHT Wiel M.F. Wauben

    E-Print Network [OSTI]

    Wauben, Wiel

    profiles if the aerosol concentrations are not too low. Since aerosol is well mixed in the atmospheric in the backscatter profile (cf. Wauben et al., 2006). Sometimes, medium and low clouds can also be missed or falsely1 TOWARDS A CLOUD CEILOMETER NETWORK REPORTING MIXING LAYER HEIGHT Wiel M.F. Wauben 1 , Marijn de

  9. Comparison of Vaisala radiosondes RS41 and RS92 at the ARM Southern Great Plains Site

    SciTech Connect (OSTI)

    Jensen, M. P.; Holdridge, D.; Survo, P.; Lehtinen, R.; Baxter, S.; Toto, T.; Johnson, K. L.

    2015-11-02

    In the fall of 2013, the Vaisala RS41-SG (4th generation) radiosonde was introduced as a replacement for the RS92-SGP radiosonde with improvements in measurement accuracy of profiles of atmospheric temperature, humidity and pressure. Thus, in order to help characterize these improvements, an intercomparison campaign was undertaken at the US Department of Energy's Atmospheric Radiation Measurement (ARM) Facility site in north Central Oklahoma USA. During 3–8 June 2014, a total of 20 twin-radiosonde flights were performed in a variety of atmospheric conditions representing typical midlatitude continental summertime conditions. The results suggest that the RS92 and RS41 measurements generally agree within manufacturer specified tolerances with notable exceptions when exiting liquid cloud layers where the "wet bulbing" effect is mitigated in the RS41 observations. The RS41 measurements also appear to show a smaller impact from solar heating. These results suggest that the RS41 does provide important improvements, particularly in cloudy conditions, but under most observational conditions the RS41 and RS92 measurements agree within the manufacturer specified limits and so a switch to RS41 radiosondes will have little impact on long-term observational records.

  10. Comparison of Vaisala radiosondes RS41 and RS92 at the ARM Southern Great Plains Site

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

    Jensen, M. P.; Holdridge, D.; Survo, P.; Lehtinen, R.; Baxter, S.; Toto, T.; Johnson, K. L.

    2015-11-02

    In the fall of 2013, the Vaisala RS41-SG (4th generation) radiosonde was introduced as a replacement for the RS92-SGP radiosonde with improvements in measurement accuracy of profiles of atmospheric temperature, humidity and pressure. Thus, in order to help characterize these improvements, an intercomparison campaign was undertaken at the US Department of Energy's Atmospheric Radiation Measurement (ARM) Facility site in north Central Oklahoma USA. During 3–8 June 2014, a total of 20 twin-radiosonde flights were performed in a variety of atmospheric conditions representing typical midlatitude continental summertime conditions. The results suggest that the RS92 and RS41 measurements generally agree within manufacturermore »specified tolerances with notable exceptions when exiting liquid cloud layers where the "wet bulbing" effect is mitigated in the RS41 observations. The RS41 measurements also appear to show a smaller impact from solar heating. These results suggest that the RS41 does provide important improvements, particularly in cloudy conditions, but under most observational conditions the RS41 and RS92 measurements agree within the manufacturer specified limits and so a switch to RS41 radiosondes will have little impact on long-term observational records.« less

  11. Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

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

    Dzambo, A. M.; Turner, D. D.; Mlawer, E. J.

    2015-10-20

    Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV),more »downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km MSL), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS). The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from ground-based microwave radiometers at tropical, mid-latitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the ground-based retrievals. The two closure activities – done for clear-sky scenes – use the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG- and MILO-corrected RH are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover – a condition not accounted for in the radiance closure experiments.« less

  12. ARM - Evaluation Product - Ceilometer Corrected for Ship Motion

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

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  13. Evaluation of the Vaisala FD12P 1.91S firmware with insect filtering

    E-Print Network [OSTI]

    Wauben, Wiel

    for aviation is restricted to values below 2 km. A so-called forward scatter sensor (FS) measures the amount), lower maintenance due to lesser sensitivity of MOR measurements to contamination of lenses

  14. MHK ISDB/Instruments/Vaisala WINDCAP Ultrasonic Wind Sensor WMT700 | Open

    Open Energy Info (EERE)

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  15. In Situ Validation of a Correction for Time-Lag and Bias Errors in Vaisala RS80-H Radiosonde Humidity Measurements

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  16. Cassandra Wheeler Univ. of Colorado Department of Atmospheric and Oceanic Sciences (ATOC)

    E-Print Network [OSTI]

    .) #12;1. Overview of ASCOS Field Campaign and Remote Sensors 2. Vertically Pointing Radars 3. Ceilometer on the energy budget NOAA's Contribution: Remotely observe cloud layers and environmental conditions Svalbard Oden #12;Ka-Band Radar S-Band Radar Wind Profiler Scanning Radiometer Lidar Ceilometer 2-Channel

  17. Energy efficiency in OpenStack clouds

    E-Print Network [OSTI]

    Lefčvre, Laurent

    (Keystone) - Dashboard (Horizon) Recently added: - Metering / billing (Ceilometer) Incubation: - Energy) - Object Storage (Swift) - Block Storage (Cinder) - Networking (Quantum) - Identity (Keystone) - Dashboard Storage (Cinder) - Networking (Quantum) - Identity (Keystone) - Dashboard (Horizon) Recently added

  18. Radiosondes Corrected for Inaccuracy in RH Measurements

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

    Miloshevich, Larry

    2008-01-15

    Corrections for inaccuracy in Vaisala radiosonde RH measurements have been applied to ARM SGP radiosonde soundings. The magnitude of the corrections can vary considerably between soundings. The radiosonde measurement accuracy, and therefore the correction magnitude, is a function of atmospheric conditions, mainly T, RH, and dRH/dt (humidity gradient). The corrections are also very sensitive to the RH sensor type, and there are 3 Vaisala sensor types represented in this dataset (RS80-H, RS90, and RS92). Depending on the sensor type and the radiosonde production date, one or more of the following three corrections were applied to the RH data: Temperature-Dependence correction (TD), Contamination-Dry Bias correction (C), Time Lag correction (TL). The estimated absolute accuracy of NIGHTTIME corrected and uncorrected Vaisala RH measurements, as determined by comparison to simultaneous reference-quality measurements from Holger Voemel's (CU/CIRES) cryogenic frostpoint hygrometer (CFH), is given by Miloshevich et al. (2006).

  19. Radiosondes Corrected for Inaccuracy in RH Measurements

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

    Miloshevich, Larry

    Corrections for inaccuracy in Vaisala radiosonde RH measurements have been applied to ARM SGP radiosonde soundings. The magnitude of the corrections can vary considerably between soundings. The radiosonde measurement accuracy, and therefore the correction magnitude, is a function of atmospheric conditions, mainly T, RH, and dRH/dt (humidity gradient). The corrections are also very sensitive to the RH sensor type, and there are 3 Vaisala sensor types represented in this dataset (RS80-H, RS90, and RS92). Depending on the sensor type and the radiosonde production date, one or more of the following three corrections were applied to the RH data: Temperature-Dependence correction (TD), Contamination-Dry Bias correction (C), Time Lag correction (TL). The estimated absolute accuracy of NIGHTTIME corrected and uncorrected Vaisala RH measurements, as determined by comparison to simultaneous reference-quality measurements from Holger Voemel's (CU/CIRES) cryogenic frostpoint hygrometer (CFH), is given by Miloshevich et al. (2006).

  20. STATUS, EVALUATION AND NEW DEVELOPMENTS IN THE AUTOMATED CLOUD OBSERVATIONS IN THE NETHERLANDS

    E-Print Network [OSTI]

    Wauben, Wiel

    STATUS, EVALUATION AND NEW DEVELOPMENTS IN THE AUTOMATED CLOUD OBSERVATIONS IN THE NETHERLANDS Wiel infrared radiometer. The evaluation of the automated cloud observations will address: (i) effects every 10 minutes. In 2006 and 2007 LD-40 sensors will replace the ceilometers at 7 Dutch Royal Air Force

  1. UITITYD ST.41CES DEPARTMQIT OF COMMERCP: Charles Sabyer, Secretary

    E-Print Network [OSTI]

    Charte, lapse rate briefing f o r aviation 1450 Adiabatic Diagram, SREW T-log p 41-42 Agricultural Automotive Equipment, use 95-46 Wea,ther reporting, influenoe t o change observation 8-47 Aviation, Officials provieions a8 t o compliance with Ceilometer, raob, rawin, installation and maintenance 4 6 4 7 Certificates

  2. 9.IP.002.5.en.do Item 5: Obs

    E-Print Network [OSTI]

    Wauben, Wiel

    latter Vaisala stitute ng the sensor port. 9-IP/2 #12;AMOFSG/9-IP/2 - 2 - 2. SOLUTION AND EVALUATION 2 KNMI decide OR at civil ai International forward scatte used for visib ficant reductio r sensors hav forecasting a EOROLOG FORWARD (Present SU s in the meteo er sensors hav ts in the meas nsor firmwar es

  3. Proceedings of the Midwest Poultry Federation Convention, St. Paul, MN. March 16-18, 2004 Ammonia Emission from Iowa Layer Houses

    E-Print Network [OSTI]

    Kentucky, University of

    ., Pittsburg, PA) for NH3 measurement and infrared sensor (0-5000 ± 20 ppm; Vaisala, Inc., Woburn, MA) for CO2 there is a pressing need for research-based data on aerial emissions and evaluation of mitigation techniques under houses and evaluate the efficacy of certain management practices. Selected layer houses of both types

  4. Total lightning observations of severe convection over North Texas 

    E-Print Network [OSTI]

    McKinney, Christopher Michael

    2009-05-15

    potential. Total lightning data were obtained from Vaisala Inc.’s Dallas/Fort Worth (D/FW) Lightning Detection and Ranging (LDAR) network. Radar data from two Weather Surveillance Radar – 1988 Doppler (WSR-88D) sites were used for position data...

  5. Environmental Data Collection Using Autonomous Wave Gliders

    E-Print Network [OSTI]

    model ­AIRMAR PB200 weather station Pressure, Temperature, Wind Speed and Direction 10 min averaged ­Forecast model evaluation #12;Near-Surface Physical Processes #12;Naval Applications ·Forecast Temperature Relative humidity Vaisala Weather Transmitter WTX520 Wind speed and direction Barometric pressure

  6. IMPACT OF ARM RADIOSONDE HUMIDITY CORRECTION ON CALCULATION OF CONVECTIVE INDICES

    E-Print Network [OSTI]

    IMPACT OF ARM RADIOSONDE HUMIDITY CORRECTION ON CALCULATION OF CONVECTIVE INDICES David Troyan the course of the history of the ARM and ASR Programs, there have been efforts to improve the humidity of humidity calibration in ARM- used Vaisala soundings. Determining additional problems, devising

  7. Comparison of Mixed Layer Heights from Airborne High Spectral Resolution Lidar, Ground-based Measurements, and the WRP-Chem Model during CalNex and CARES

    SciTech Connect (OSTI)

    Scarino, Amy Jo; Obland, Michael; Fast, Jerome D.; Burton, S. P.; Ferrare, R. A.; Hostetler, Chris A.; Berg, Larry K.; Lefer, Barry; Haman, C.; Hair, John; Rogers, Ray; Butler, Carolyn; Cook, A. L.; Harper, David

    2014-06-05

    The California Research at the Nexus of Air Quality and Climate Change (CalNex) and Carbonaceous Aerosol and Radiative Effects Study (CARES) field campaigns during May and June 2010 provided a data set appropriate for studying characteristics of the planetary boundary layer (PBL). The NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) was deployed to California onboard the NASA LaRC B-200 aircraft to aid incharacterizing aerosol properties during these two field campaigns. Measurements of aerosol extinction (532 nm), backscatter (532 and 1064 nm), and depolarization (532 and 1064 nm) profiles during 31 flights, many in coordination with other research aircraft and ground sites, constitute a diverse data set for use in characterizing the spatial and temporal distribution of aerosols, as well as the depth and variability of the daytime mixed layer (ML), which is a subset within the PBL. This work illustrates the temporal and spatial variability of the ML in the vicinity of Los Angeles and Sacramento, CA. ML heights derived from HSRL measurements are compared to PBL heights derived from radiosonde profiles, ML heights measured from ceilometers, and simulated PBL heights from the Weather Research and Forecasting Chemistry (WRF-Chem) community model. Comparisons between the HSRL ML heights and the radiosonde profiles in Sacramento result in a correlation coefficient value (R) of 0.93 (root7 mean-square (RMS) difference of 157 m and bias difference (HSRL radiosonde) of 5 m). HSRL ML heights compare well with those from the ceilometer in the LA Basin with an R of 0.89 (RMS difference of 108 m and bias difference (HSRL Ceilometer) of -9.7 m) for distances of up to 30 km between the B-200 flight track and the ceilometer site. Simulated PBL heights from WRF-Chem were compared with those obtained from all flights for each campaign, producing an R of 0.58 (RMS difference of 604 m and a bias difference (WRF-Chem HSRL) of -157 m) for CalNex and 0.59 (RMS difference of 689 m and a bias difference (WRF-Chem HSRL) of 220 m) for CARES. Aerosol backscatter simulations are also available from WRF15 Chem and are compared to those from HSRL to examine differences among the methods used to derive ML heights.

  8. X-band Scanning ARM Precipitation Radar (X-SAPR) Instrument Handbook

    SciTech Connect (OSTI)

    Widener, K; Bharadwaj, N

    2012-10-29

    The X-band scanning ARM cloud radar (X-SAPR) is a full-hemispherical scanning polarimetric Doppler radar transmitting simultaneously in both H and V polarizations. With a 200 kW magnetron transmitter, this puts 100 kW of transmitted power for each polarization. The receiver for the X-SAPR is a Vaisala Sigmet RVP-900 operating in a coherent-on-receive mode. Three X-SAPRs are deployed around the Southern Great Plains (SGP) Central Facility in a triangular array. A fourth X-SAPR is deployed near Barrow, Alaska on top of the Barrow Arctic Research Center.

  9. 1

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  10. ARM - People Address Information

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

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  11. ARM - Point Reyes News

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

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  12. ARM - Possible Benefits of Global Warming on Agriculture

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

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  13. ARM - Posters

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

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  14. SOAR Data: Data from Shipboard Oceanographic and Atmospheric Radiation (SOAR)1999 through 2001

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

    Click on the DATA menu button and then click on a specific ship to find instructions on accessing data from that particular cruise. Instructions will lead you to an FTP site from which data can be downloaded. SOAR data for 1999 through 2001 is reported. SOAR is a global network of research and volunteer ships that carry global change instrumentation. The primary emphasis for SOAR is solar and IR radiation but some ships cary ceilometers, meteorological instruments, and related equipment. All data are collected in a central data collection computer and the flexible data collection software can be adapted to any other user instrumentation. Currently SOAR is installed pas permanent instrumentation on four ships operating in the western Pacific, eastern tropical Pacific, West Indies, and an oceanographic ship that operates around the world. In addition, six other system are used on cruises of opportunity. [Taken from SOAR homepage at http://www.gim.bnl.gov/soar/index.html

  15. A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

    SciTech Connect (OSTI)

    Ahn, M.-H.; Han, D.; Won, H. Y.; Morris, V.

    2015-01-01

    For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

  16. A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

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

    Ahn, M. H.; Han, D.; Won, H. Y.; Morris, Victor R.

    2015-02-03

    For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperaturemore »and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.« less

  17. Atmospheric sensing for the H.E.S.S. array

    SciTech Connect (OSTI)

    Aye, K.-M.; Brown, A.M.; Chadwick, P.M.; Hadjichristidis, C.; Latham, I.J.; Le Gallou, R.; McComb, T.J.L.; Nolan, S.J.; Noutsos, A.; Orford, K.J.; Osborne, J.L.; Rayner, S.M.

    2005-02-21

    Several atmospheric monitoring instruments have been installed at the H.E.S.S. gamma-ray observatory in Namibia. Firstly, Heitronics KT19 infrared radiometers, aligned paraxially with the H.E.S.S. telescopes, measure the infrared radiation of the water molecules. These allow us to detect clouds crossing the telescopes' field of view and to estimate the humidity present in the atmosphere. For a general estimate of the atmosphere's transmittance, i.e. the detection of any light-attenuating aerosols, a ceilometer, which is a LIDAR with built-in atmospheric data reduction code, is being used. It will be complemented soon by an instrument which will measure the transmissivity of the atmosphere at different wavelengths up to 500m above the ground. The overall status of the weather is monitored by a fully automated weatherstation. This paper describes the setup, the data analysis and how this will be used in order to improve the knowledge of the telescopes' effective collection area.

  18. Spatial Variability of Surface Irradiance Measurements at the Manus ARM Site

    SciTech Connect (OSTI)

    Riihimaki, Laura D.; Long, Charles N.

    2014-05-16

    The location of the Atmospheric Radiation Measurement (ARM) site on Manus island in Papua New Guinea was chosen because it is very close the coast, in a geographically at, near-sea level area of the island, minimizing the impact of local island effects on the meteorology of the measurements [Ackerman et al., 1999]. In this study, we confirm that the Manus site is in deed less impacted by the island meteorology than slightly inland by comparing over a year of broadband surface irradiance and ceilometer measurements and derived quantities at the standard Manus site and a second location 7 km away as part of the AMIE-Manus campaign. The two sites show statistically similar distributions of irradiance and other derived quantities for all wind directions except easterly winds, when the inland site is down wind from the standard Manus site. Under easterly wind conditions, which occur 17% of the time, there is a higher occurrence of cloudiness at the down wind site likely do to land heating and orographic effects. This increased cloudiness is caused by shallow, broken clouds often with bases around 700 m in altitude. While the central Manus site consistently measures a frequency of occurrence of low clouds (cloud base height less than 1200 m) about 25+4% regardless of wind direction, the AMIE site has higher frequencies of low clouds (38%) when winds are from the east. This increase in low, locally produced clouds causes an additional -20 W/m2 shortwave surface cloud radiative effect at the AMIE site in easterly conditions than in other meteorological conditions that exhibit better agreement between the two sites.

  19. AMIE (ARM MJO Investigation Experiment): Observations of the Madden-Julian Oscillation for Modeling Studies Science Plan

    SciTech Connect (OSTI)

    Long, C; Del Genio, A; Gustafson, W; Houze, R; Jakob, C; Jensen, M; Klein, S; Leung, L Ruby; Liu, X; Luke, E; May, P; McFarlane, S; Minnis, P; Schumacher, C; Vogelmann, A; Wang, Y; Wu, X; Xie, S

    2010-03-22

    Deep convection in the tropics plays an important role in driving global circulations and the transport of energy from the tropics to the mid-latitudes. Understanding the mechanisms that control tropical convection is a key to improving climate modeling simulations of the global energy balance. One of the dominant sources of tropical convective variability is the Madden-Julian Oscillation (MJO), which has a period of approximately 30–60 days. There is no agreed-upon explanation for the underlying physics that maintain the MJO. Many climate models do not show well-defined MJO signals, and those that do have problems accurately simulating the amplitude, propagation speed, and/or seasonality of the MJO signal. Therefore, the MJO is a very important modeling target for the ARM modeling community geared specifically toward improving climate models. The ARM MJO Investigation Experiment (AMIE) period coincides with a large international MJO initiation field campaign called CINDY2011 (Cooperative Indian Ocean experiment on intraseasonal variability in the Year 2011) that will take place in and around the Indian Ocean from October 2011 to January 2012. AMIE, in conjunction with CINDY2011 efforts, will provide an unprecedented data set that will allow investigation of the evolution of convection within the framework of the MJO. AMIE observations will also complement the long-term MJO statistics produced using ARM Manus data and will allow testing of several of the current hypotheses related to the MJO phenomenon. Taking advantage of the expected deployment of a C-POL scanning precipitation radar and an ECOR surface flux tower at the ARM Manus site, we propose to increase the number of sonde launches to eight per day starting in about mid-October of the field experiment year, which is climatologically a period of generally suppressed conditions at Manus and just prior to the climatologically strongest MJO period. The field experiment will last until the end of the MJO season (typically March), affording the documentation of conditions before, during, and after the peak MJO season. The increased frequency of sonde launches throughout the experimental period will provide better diurnal understanding of the thermodynamic profiles, and thus a better representation within the variational analysis data set. Finally, a small surface radiation and ceilometer system will be deployed at the PNG Lombrum Naval Base about 6 km away from the ARM Manus site in order to provide some documentation of scale variability with respect to the representativeness of the ARM measurements.