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Title: Analysis of Radiosonde and ground-based remotely sensed PWV data from the 2004 North Slope of Alaska Arctic Winter Radiometric Experiment.

Abstract

No abstract prepared.

Authors:
; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
914765
Report Number(s):
ANL/DIS/JA-54788
TRN: US200812%%53
DOE Contract Number:
DE-AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: J. Atmos. Ocean. Technol.; Journal Volume: 24; Journal Issue: 3 ; Mar. 2007
Country of Publication:
United States
Language:
ENGLISH
Subject:
54 ENVIRONMENTAL SCIENCES; 47 OTHER INSTRUMENTATION; ALASKA; WATER VAPOR; REMOTE SENSING; PERFORMANCE TESTING; MEASURING INSTRUMENTS; ATMOSPHERIC CHEMISTRY

Citation Formats

Mattioli, V., Westwater, E. R., Cimini, D., Liljegren, J. C., Lesht, B. M., Gutman, S. I., Schmidlin, F. J., Univ. of Perugia, Univ. of Colorado, NOAA Forecast Systems Lab., NASA, Enviornmental Technology Lab., and Science and Technology Corp. Analysis of Radiosonde and ground-based remotely sensed PWV data from the 2004 North Slope of Alaska Arctic Winter Radiometric Experiment.. United States: N. p., 2007. Web. doi:10.1175/JTECH1982.1.
Mattioli, V., Westwater, E. R., Cimini, D., Liljegren, J. C., Lesht, B. M., Gutman, S. I., Schmidlin, F. J., Univ. of Perugia, Univ. of Colorado, NOAA Forecast Systems Lab., NASA, Enviornmental Technology Lab., & Science and Technology Corp. Analysis of Radiosonde and ground-based remotely sensed PWV data from the 2004 North Slope of Alaska Arctic Winter Radiometric Experiment.. United States. doi:10.1175/JTECH1982.1.
Mattioli, V., Westwater, E. R., Cimini, D., Liljegren, J. C., Lesht, B. M., Gutman, S. I., Schmidlin, F. J., Univ. of Perugia, Univ. of Colorado, NOAA Forecast Systems Lab., NASA, Enviornmental Technology Lab., and Science and Technology Corp. Thu . "Analysis of Radiosonde and ground-based remotely sensed PWV data from the 2004 North Slope of Alaska Arctic Winter Radiometric Experiment.". United States. doi:10.1175/JTECH1982.1.
@article{osti_914765,
title = {Analysis of Radiosonde and ground-based remotely sensed PWV data from the 2004 North Slope of Alaska Arctic Winter Radiometric Experiment.},
author = {Mattioli, V. and Westwater, E. R. and Cimini, D. and Liljegren, J. C. and Lesht, B. M. and Gutman, S. I. and Schmidlin, F. J. and Univ. of Perugia and Univ. of Colorado and NOAA Forecast Systems Lab. and NASA and Enviornmental Technology Lab. and Science and Technology Corp.},
abstractNote = {No abstract prepared.},
doi = {10.1175/JTECH1982.1},
journal = {J. Atmos. Ocean. Technol.},
number = 3 ; Mar. 2007,
volume = 24,
place = {United States},
year = {Thu Mar 01 00:00:00 EST 2007},
month = {Thu Mar 01 00:00:00 EST 2007}
}
  • The 2004 Arctic Winter Radiometric Experiment was conducted at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) Program field site near Barrow, Alaska from March 9 to April 9, 2004. The goals of the experiment were: to study the microwave and millimeter wave radiometric response to water vapor and clouds during cold and dry conditions; to obtain data for forward model studies at frequencies ranging from 22.235 to 400 GHz, to demonstrate new Environmental Technology Laboratory's (ETL) radiometric receiver and calibration technology and to compare both radiometric and in situ measurements of water vapor.
  • European Centre for Medium-Range Weather Forecasts (ECMWF) analysis and model forecast data are evaluated using observations collected during the Atmospheric Radiation Measurement (ARM) October 2004 Mixed-Phase Arctic Cloud Experiment (M-PACE) at its North Slope of Alaska (NSA) site. It is shown that the ECMWF analysis reasonably represents the dynamic and thermodynamic structures of the large-scale systems that affected the NSA during M-PACE. The model-analyzed near-surface horizontal winds, temperature, and relative humidity also agree well with the M-PACE surface measurements. Given the well-represented large-scale fields, the model shows overall good skill in predicting various cloud types observed during M-PACE; however, themore » physical properties of single-layer boundary layer clouds are in substantial error. At these times, the model substantially underestimates the liquid water path in these clouds, with the concomitant result that the model largely underpredicts the downwelling longwave radiation at the surface and overpredicts the outgoing longwave radiation at the top of the atmosphere. The model also overestimates the net surface shortwave radiation, mainly because of the underestimation of the surface albedo. The problem in the surface albedo is primarily associated with errors in the surface snow prediction. Principally because of the underestimation of the surface downwelling longwave radiation at the times of single-layer boundary layer clouds, the model shows a much larger energy loss (-20.9 W m-2) than the observation (-9.6 W m-2) at the surface during the M-PACE period.« less
  • This paper presents a new neural network (NN) algorithm for real-time retrievals of low amounts of precipitable water vapor (PWV) and integrated liquid water from millimeter-wave ground-based observations. Measurements are collected by the 183.3-GHz G-band vapor radiometer (GVR) operating at the Atmospheric Radiation Measurement (ARM) Program Climate Research Facility, Barrow, AK. The NN provides the means to explore the nonlinear regime of the measurements and investigate the physical boundaries of the operability of the instrument. A methodology to compute individual error bars associated with the NN output is developed, and a detailed error analysis of the network output is provided.more » Through the error analysis, it is possible to isolate several components contributing to the overall retrieval errors and to analyze the dependence of the errors on the inputs. The network outputs and associated errors are then compared with results from a physical retrieval and with the ARM two-channel microwave radiometer (MWR) statistical retrieval. When the NN is trained with a seasonal training data set, the retrievals of water vapor yield results that are comparable to those obtained from a traditional physical retrieval, with a retrieval error percentage of {approx}5% when the PWV is between 2 and 10 mm, but with the advantages that the NN algorithm does not require vertical profiles of temperature and humidity as input and is significantly faster computationally. Liquid water path (LWP) retrievals from the NN have a significantly improved clear-sky bias (mean of {approx}2.4 g/m{sup 2}) and a retrieval error varying from 1 to about 10 g/m{sup 2} when the PWV amount is between 1 and 10 mm. As an independent validation of the LWP retrieval, the longwave downwelling surface flux was computed and compared with observations. The comparison shows a significant improvement with respect to the MWR statistical retrievals, particularly for LWP amounts of less than 60 g/m{sup 2}.« less
  • The authors propose a method to estimate sea surface nitrate (N) from space using satellite measurements of sea surface temperature (SST) and chlorophyll a (chl a). The procedure relies on empirical relationships between shipboard measurements of N and its predictor variables, temperature (T) and chl a in surface and near surface waters. Although N appears to be controlled primarily by T, the addition of the biological variable chl a helps improve N prediction by reducing local and regional differences in the character of the temperature-nitrate (T-N) relationship. In the present study, the authors have applied these empirical algorithms to SSTmore » and chl a data from the Ocean Color and Temperature Scanner (OCTS) on board the Advanced Earth Observation Satellite (ADEOS). The results clearly suggest that measurements of SST and chl a now possible by modern-day ocean satellites could be exploited usefully to extend the resolution of shipboard N measurements over large spatial and temporal scales. Systematic errors in estimates of N that could result from errors in satellite estimates of SST and chl a are examined through sensitivity analyses.« less
  • Accurate detection and modeling of the influences of global climate change on existing habitats require measurement instruments that can integrate the combined effects of external forces on vegetation at a spatial scale adequate for resolving the influences of climate. Advanced very high resolution radiometers (AVHRR) aboard NOAA polar orbiting satellites were used to develop a GIS of primary productivity indices (NDVI) for the growing season over the North slope of Alaska for 1990-1992. Plots of NDVI through the growing season as the response variable, differences were detected between years, watersheds, and ecoregions. An early onset of growing conditions in 1990more » appeared to be responsible for high overall primary productivity, while the late onset of growing conditions in 1992 appears to have limited productivity. Differences in annual integrated NDVI appear to be related to length of season not peak productivity levels which were similar in all years. Continuing research will include extending the timeline and testing for linkages between climatic variables and NDVI patterns.« less