scholarly journals Carbon monoxide mixing ratios over Oklahoma between 2002 and 2009 retrieved from Atmospheric Emitted Radiance Interferometer spectra

2010 ◽  
Vol 3 (5) ◽  
pp. 1319-1331 ◽  
Author(s):  
L. Yurganov ◽  
W. McMillan ◽  
C. Wilson ◽  
M. Fischer ◽  
S. Biraud ◽  
...  
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Retrieval Algorithm ◽  
Southern Great Plains ◽  
Diurnal Cycles ◽  
Mixing Ratios ◽  
Clear Sky ◽  
The Tropics

Abstract. CO mixing ratios for the lowermost 2-km atmospheric layer were retrieved from downwelling infrared (IR) radiance spectra of the clear sky measured between 2002 and 2009 by a zenith-viewing Atmospheric Emitted Radiance Interferometer (AERI) deployed at the Southern Great Plains (SGP) observatory of the Atmospheric Radiation Measurements (ARM) Program near Lamont, Oklahoma. A version of a published earlier retrieval algorithm was improved and validated. Archived temperature and water vapor profiles retrieved from the same AERI spectra through automated ARM processing were used as input data for the CO retrievals. We found the archived water vapor profiles required additional constraint using SGP Microwave Radiometer retrievals of total precipitable water vapor. A correction for scattered solar light was developed as well. The retrieved CO was validated using simultaneous independently measured CO profiles from an aircraft. These tropospheric CO profiles were measured from the surface to altitudes of 4572 m a.s.l. once or twice a week between March 2006 and December 2008. The aircraft measurements were supplemented with ground-based CO measurements using a non-dispersive infrared gas correlation instrument at the SGP and retrievals from the Atmospheric IR Sounder (AIRS) above 5 km to create full tropospheric CO profiles. Comparison of the profiles convolved with averaging kernels to the AERI CO retrievals found a squared correlation coefficient of 0.57, a standard deviation of ±11.7 ppbv, a bias of -16 ppbv, and a slope of 0.92. Averaged seasonal and diurnal cycles measured by the AERI are compared with those measured continuously in situ at the SGP in the boundary layer. Monthly mean CO values measured by the AERI between 2002 and 2009 are compared with those measured by the AIRS over North America, the Northern Hemisphere mid-latitudes, and over the tropics.

scholarly journals Carbon monoxide mixing ratios over Oklahoma between 2002 and 2009 retrieved from Atmospheric Emitted Radiance Interferometer spectra

2010 ◽  
Vol 3 (2) ◽  
pp. 1263-1301 ◽  
Author(s):  
L. Yurganov ◽  
W. McMillan ◽  
C. Wilson ◽  
M. Fischer ◽  
S. Biraud
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Great Plains ◽  
A Priori ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Diurnal Cycles ◽  
Mixing Ratios ◽  
True Profile

Abstract. CO mixing ratios weighted over the bottom 2-km thick atmospheric layer between 2002 and 2009 were retrieved from downwelling infrared (IR) radiance spectra of the clear sky measured by a zenith-viewing Atmospheric Emitted Radiance Interferometer (AERI) deployed at the Southern Great Plains (SGP) observatory of the Atmospheric Radiation Measurements (ARM) Program near Lamont, Oklahoma. A version of the algorithm proposed by He at al. (2001) was significantly improved and validated. Essentially, the new algorithm retrieves a CO mixing ratio that is determined by the convolution of the a priori profile (assumed to be constant with altitude), the true profile, and the averaging kernel which maximizes near the surface. Approximately 70% of the CO signal comes from the boundary layer and the remaining 30% come from the lower part of the free troposphere. Archived temperature and water vapor profiles retrieved from the same AERI spectra through automated ARM processing were used as input data for the CO retrievals. We found the archived water vapor profiles required additional constraint using SGP Microwave Radiometer retrievals of total precipitable water vapor. Additionally, a correction for scattered solar light was developed. The retrieved CO was validated using simultaneous independently measured CO profiles. An aircraft supplied in situ CO measurements at altitudes up to 4572 m above sea level once or twice a week between March 2006 and December 2008. The aircraft measurements were supplemented with ground-based CO measurements at the SGP and retrievals from the Atmospheric IR Sounder (AIRS) above 5 km to create full tropospheric CO profiles. Comparison of the convolved profiles to the AERI CO retrievals found a squared correlation coefficient of 0.57, a standard deviation of ±11.7 ppbv, a bias of 16 ppbv, and a slope of 0.92. Averaged seasonal and diurnal cycles measured by AERI are compared with those measured continuously in situ at the SGP in the boundary layer. Monthly mean CO values measured by AERI between 2002 and 2009 are compared with those measured by AIRS over North America, the Northern Hemisphere mid-latitudes, and over the tropics.

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.

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 Characterization of Upper-Troposphere Water Vapor Measurements during AFWEX Using LASE

2004 ◽  
Vol 21 (12) ◽  
pp. 1790-1808 ◽  
Author(s):  
R. A. Ferrare ◽  
E. V. Browell ◽  
S. Ismail ◽  
S. A. Kooi ◽  
L. H. Brasseur ◽  
...  
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Calibration Method ◽  
Department Of Energy ◽  
Raman Lidar ◽  
Upper Troposphere ◽  
Mean Differences

Abstract Water vapor mass mixing ratio profiles from NASA's Lidar Atmospheric Sensing Experiment (LASE) system acquired during the Atmospheric Radiation Measurement (ARM)–First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX) are used as a reference to characterize upper-troposphere water vapor (UTWV) measured by ground-based Raman lidars, radiosondes, and in situ aircraft sensors over the Department of Energy (DOE) ARM Southern Great Plains (SGP) site in northern Oklahoma. LASE was deployed from the NASA DC-8 aircraft and measured water vapor over the ARM SGP Central Facility (CF) site during seven flights between 27 November and 10 December 2000. Initially, the DOE ARM SGP Cloud and Radiation Testbed (CART) Raman lidar (CARL) UTWV profiles were about 5%–7% wetter than LASE in the upper troposphere, and the Vaisala RS80-H radiosonde profiles were about 10% drier than LASE between 8 and 12 km. Scaling the Vaisala water vapor profiles to match the precipitable water vapor (PWV) measured by the ARM SGP microwave radiometer (MWR) did not change these results significantly. By accounting for an overlap correction of the CARL water vapor profiles and by employing schemes designed to correct the Vaisala RS80-H calibration method and account for the time response of the Vaisala RS80-H water vapor sensor, the average differences between the CARL and Vaisala radiosonde upper-troposphere water vapor profiles are reduced to about 5%, which is within the ARM goal of mean differences of less than 10%. The LASE and DC-8 in situ diode laser hygrometer (DLH) UTWV measurements generally agreed to within about 3%–4%. The DC-8 in situ frost point cryogenic hygrometer and Snow White chilled-mirror measurements were drier than the LASE, Raman lidars, and corrected Vaisala RS80H measurements by about 10%–25% and 10%–15%, respectively. Sippican (formerly VIZ Manufacturing) carbon hygristor radiosondes exhibited large variabilities and poor agreement with the other measurements. PWV derived from the LASE profiles agreed to within about 3% on average with PWV derived from the ARM SGP microwave radiometer. The agreement between the LASE and MWR PWV and the LASE and CARL UTWV measurements supports the hypotheses that MWR measurements of the 22-GHz water vapor line can accurately constrain the total water vapor amount and that the CART Raman lidar, when calibrated using the MWR PWV, can provide an accurate, stable reference for characterizing upper-troposphere water vapor.

scholarly journals Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site

2021 ◽  
Vol 13 (12) ◽  
pp. 2309
Author(s):  
Jingjing Tian ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Likun Wang ◽  
Rusen Öktem ◽  
...  
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Detection Threshold ◽  
Department Of Energy ◽  
Surface Reflectance ◽  
Cloud Detection ◽  
Southern Great Plains ◽  
Clear Sky ◽  
Shallow Cumulus ◽  
Stereo Cameras

Summertime continental shallow cumulus clouds (ShCu) are detected using Geostationary Operational Environmental Satellite (GOES)-16 reflectance data, with cross-validation by observations from ground-based stereo cameras at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. A ShCu cloudy pixel is identified when the GOES reflectance exceeds the clear-sky surface reflectance by a reflectance detection threshold of ShCu, ΔR. We firstly construct diurnally varying clear-sky surface reflectance maps and then estimate the ∆R. A GOES simulator is designed, projecting the clouds reconstructed by stereo cameras towards the surface along the satellite’s slanted viewing direction. The dynamic ShCu detection threshold ΔR is determined by making the GOES cloud fraction (CF) equal to the CF from the GOES simulator. Although there are temporal variabilities in ΔR, cloud fractions and cloud size distributions can be well reproduced using a constant ΔR value of 0.045. The method presented in this study enables daytime ShCu detection, which is usually falsely reported as clear sky in the GOES-16 cloud mask data product. Using this method, a new ShCu dataset can be generated to bridge the observational gap in detecting ShCu, which may transition into deep precipitating clouds, and to facilitate further studies on ShCu development over heterogenous land surface.

Carbon dioxide and water vapor fluxes of multi-purpose winter wheat production systems in the U.S. Southern Great Plains

2021 ◽  
Vol 310 ◽  
pp. 108631
Author(s):  
Pradeep Wagle ◽  
Prasanna H. Gowda ◽  
Brian K. Northup ◽  
James P.S. Neel ◽  
Patrick J. Starks ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Water Vapor ◽  
Winter Wheat ◽  
Great Plains ◽  
Production Systems ◽  
Wheat Production ◽  
Southern Great Plains ◽  
The U.S

scholarly journals Quantifying the Land–Atmosphere Coupling Behavior in Modern Reanalysis Products over the U.S. Southern Great Plains

2015 ◽  
Vol 28 (14) ◽  
pp. 5813-5829 ◽  
Author(s):  
Joseph A. Santanello ◽  
Joshua Roundy ◽  
Paul A. Dirmeyer
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Southern Great Plains ◽  
Diurnal Cycles ◽  
The North ◽  
Energy Cycle ◽  
Coupling Behavior ◽  
Regional Reanalysis ◽  
Modern Era ◽  
The U.S

Abstract The coupling of the land with the planetary boundary layer (PBL) on diurnal time scales is critical to regulating the strength of the connection between soil moisture and precipitation. To improve understanding of land–atmosphere (L–A) interactions, recent studies have focused on the development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, the authors apply a suite of local land–atmosphere coupling (LoCo) metrics to modern reanalysis (RA) products and observations during a 17-yr period over the U.S. southern Great Plains. Specifically, a range of diagnostics exploring the links between soil moisture, evaporation, PBL height, temperature, humidity, and precipitation is applied to the summertime monthly mean diurnal cycles of the North American Regional Reanalysis (NARR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR). Results show that CFSR is the driest and MERRA the wettest of the three RAs in terms of overall surface–PBL coupling. When compared against observations, CFSR has a significant dry bias that impacts all components of the land–PBL system. CFSR and NARR are more similar in terms of PBL dynamics and response to dry and wet extremes, while MERRA is more constrained in terms of evaporation and PBL variability. Each RA has a unique land–PBL coupling that has implications for downstream impacts on the diurnal cycle of PBL evolution, clouds, convection, and precipitation as well as representation of extremes and drought. As a result, caution should be used when treating RAs as truth in terms of their water and energy cycle processes.

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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