Microwave and Millimeter-Wave Radiometric and Radiosonde Observations in an Arctic Environment

2008 ◽  
Vol 25 (10) ◽  
pp. 1768-1777 ◽  
Author(s):  
V. Mattioli ◽  
E. R. Westwater ◽  
D. Cimini ◽  
A. J. Gasiewski ◽  
M. Klein ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
The Arctic ◽  
Atmospheric Radiation ◽  
Stratospheric Water Vapor ◽  
Significant Difference ◽  
Atmospheric Radiation Measurement ◽  
Microwave Radiometers ◽  
The U.S

Abstract In a recent paper by Mattioli et al., a significant difference was observed between upper-tropospheric and lower-stratospheric water vapor profiles as observed by two radiosonde systems operating in the Arctic. The first was the Vaisala RS90 system as operated by the U.S. Department of Energy’s Atmospheric Radiation Measurement Program; the second was the operational radiosondes launched by the U.S. National Weather Service that used the Sippican VIZ-B2 type. Observations of precipitable water vapor by ground-based microwave radiometers and GPS did not reveal these differences. However, both the microwave radiometer profiler (MWRP) and the ground-based scanning radiometer (GSR) contain channels that receive a significant response from the upper-tropospheric region. In this paper, it is shown that brightness temperature (Tb) observations from these instruments are in consistent agreement with calculations based on the RS90 data but differ by several degrees with calculations based on the VIZ radiosondes. It is also shown that calculations of Tb can serve as a gross quality control of upper-tropospheric soundings.

scholarly journals Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2015 ◽  
Vol 8 (10) ◽  
pp. 10755-10792
Author(s):  
A. M. Dzambo ◽  
D. D. Turner ◽  
E. J. Mlawer
Keyword(s):  
Water Vapor ◽  
Statistical Significance ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Upper Troposphere ◽  
Clear Sky ◽  
Microwave Radiometers ◽  
The Impact ◽  
Dry Bias ◽  
Better Than

Abstract. 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), 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.

scholarly journals Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2016 ◽  
Vol 9 (4) ◽  
pp. 1613-1626 ◽  
Author(s):  
Andrew M. Dzambo ◽  
David D. Turner ◽  
Eli J. Mlawer
Keyword(s):  
Water Vapor ◽  
Statistical Significance ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Upper Troposphere ◽  
Clear Sky ◽  
Microwave Radiometers ◽  
The Impact ◽  
Dry Bias ◽  
Better Than

Abstract. 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), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km m.s.l.), 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, midlatitude, 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 RHs 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.

scholarly journals The Atmospheric Radiation Measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals

2013 ◽  
Vol 6 (2) ◽  
pp. 3723-3763 ◽  
Author(s):  
M. P. Cadeddu ◽  
J. C. Liljegren ◽  
D. D. Turner
Keyword(s):  
Scientific Community ◽  
Early Stage ◽  
Precipitable Water ◽  
Atmospheric Radiation ◽  
Radiation Measurement ◽  
Liquid Water Path ◽  
Atmospheric Radiation Measurement ◽  
Cloud Liquid Water Path ◽  
Microwave Radiometers ◽  
Microwave Instrumentation

Abstract. The Climate Research Facility of the US Department of Energy's Atmospheric Radiation Measurement (ARM) Program operates a network of ground-based microwave radiometers. Data and retrievals from these instruments have been available to the scientific community for almost 20 yr. In the past five years the network has been expanded to include a total of 22 microwave radiometers deployed in various locations around the world. The new instruments cover a frequency range between 22 and 197 GHz and are consistently and automatically calibrated. The latest addition to the network is a new generation of three-channel radiometers currently in the early stage of deployment at all ARM sites. The network has been specifically designed to achieve increased accuracy in the retrieval of precipitable water vapor (PWV) and cloud liquid water path (LWP) with the long-term goal of providing the scientific community with reliable, calibrated radiometric data and retrievals of important geophysical quantities with well-characterized uncertainties. The radiometers provide high-quality, continuous datasets that can be utilized in a wealth of applications and scientific studies. This paper presents an overview of the microwave instrumentation, calibration procedures, data, and retrievals that are available for download from the ARM data archive.

Improved Daytime Column-Integrated Precipitable Water Vapor from Vaisala Radiosonde Humidity Sensors

2008 ◽  
Vol 25 (6) ◽  
pp. 873-883 ◽  
Author(s):  
K. E. Cady-Pereira ◽  
M. W. Shephard ◽  
D. D. Turner ◽  
E. J. Mlawer ◽  
S. A. Clough ◽  
...  
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Transmittance ◽  
Dry Bias

Abstract Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.

scholarly journals The Atmospheric radiation measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals

2013 ◽  
Vol 6 (9) ◽  
pp. 2359-2372 ◽  
Author(s):  
M. P. Cadeddu ◽  
J. C. Liljegren ◽  
D. D. Turner
Keyword(s):  
Scientific Community ◽  
Early Stage ◽  
Precipitable Water ◽  
Atmospheric Radiation ◽  
Radiation Measurement ◽  
Liquid Water Path ◽  
Atmospheric Radiation Measurement ◽  
Cloud Liquid Water Path ◽  
Microwave Radiometers ◽  
Microwave Instrumentation

Abstract. The Climate Research Facility of the US Department of Energy's Atmospheric Radiation Measurement (ARM) Program operates a network of ground-based microwave radiometers. Data and retrievals from these instruments have been available to the scientific community for almost 20 yr. In the past five years the network has expanded to include a total of 22 microwave radiometers deployed in various locations around the world. The new instruments cover a frequency range between 22 and 197 GHz and are consistently and automatically calibrated. The latest addition to the network is a new generation of three-channel radiometers, currently in the early stage of deployment at all ARM sites. The network has been specifically designed to achieve increased accuracy in the retrieval of precipitable water vapor (PWV) and cloud liquid water path (LWP) with the long-term goal of providing the scientific community with reliable, calibrated radiometric data and retrievals of important geophysical quantities with well-characterized uncertainties. The radiometers provide high-quality, continuous datasets that can be utilized in a wealth of applications and scientific studies. This paper presents an overview of the microwave instrumentation, calibration procedures, data, and retrievals that are available for download from the ARM data archive.

scholarly journals Assessment of Regional Global Climate Model Water Vapor Bias and Trends Using Precipitable Water Vapor (PWV) Observations from a Network of Global Positioning Satellite (GPS) Receivers in the U.S. Great Plains and Midwest

2012 ◽  
Vol 25 (16) ◽  
pp. 5471-5493 ◽  
Author(s):  
Jacola A. Roman ◽  
Robert O. Knuteson ◽  
Steven A. Ackerman ◽  
David C. Tobin ◽  
Henry E. Revercomb
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Precipitable Water ◽  
Specific Humidity ◽  
Model Output ◽  
Global Positioning ◽  
The U.S

Abstract Precipitable water vapor (PWV) observations from the National Center of Atmospheric Research (NCAR) SuomiNet networks of ground-based global positioning system (GPS) receivers and the National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) are used in the regional assessment of global climate models. Study regions in the U.S. Great Plains and Midwest highlight the differences among global climate model output from the Fourth Assessment Report (AR4) Special Report on Emissions Scenarios (SRES) A2 scenario in their seasonal representation of column water vapor and the vertical distribution of moisture. In particular, the Community Climate System model, version 3 (CCSM3) is shown to exhibit a dry bias of over 30% in the summertime water vapor column, while the Goddard Institute for Space Studies Model E20 (GISS E20) agrees well with PWV observations. A detailed assessment of vertical profiles of temperature, relative humidity, and specific humidity confirm that only GISS E20 was able to represent the summertime specific humidity profile in the atmospheric boundary layer (<3%) and thus the correct total column water vapor. All models show good agreement in the winter season for the region. Regional trends using station-elevation-corrected GPS PWV data from two complimentary networks are found to be consistent with null trends predicted in the AR4 A2 scenario model output for the period 2000–09. The time to detect (TTD) a 0.05 mm yr−1 PWV trend, as predicted in the A2 scenario for the period 2000–2100, is shown to be 25–30 yr with 95% confidence in the Oklahoma–Kansas region.

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