An Assessment of VAS-Derived Retrievals and Parameters Used in Thunderstorm Forecasting

Fuelberg, Henry E.; Olson, Steven R.

doi:10.1175/1520-0493(1991)119<0795:aaovdr>2.0.co;2

Cited by 10 publications

(9 citation statements)

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“…The SGP site was selected as the first ARM field measurement site because of its high variability of weather conditions, large seasonal variability of temperature, moisture and precipitation, spatially homogeneous terrain, and easy accessibility. Raob temperature/water vapor profiles have been widely used to evaluate remotely sensed profiles (Fuelberg and Olson 1991;Rao and Fuelberg 1998;Jin et al 2008). Recently, the ARM CART site has been selected as a site in the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) to provide long-term, highly accurate measurements of the atmospheric profiles.…”

Section: ) the Measurements From The Arm Cart Sitementioning

confidence: 99%

Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder

Lee

et al. 2014

Journal of Atmospheric and Oceanic Technology

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A physical retrieval algorithm has been developed for deriving the legacy atmospheric profile (LAP) product from infrared radiances of the Advanced Baseline Imager (ABI) on board the next-generation Geostationary Operational Environmental Satellite (GOES-R) series. In this study, the GOES-R ABI LAP retrieval algorithm is applied to the GOES-13 sounder radiance measurements (termed the GOES-13 LAP retrieval algorithm in this study) for its validation as well as for potential transition of the GOES-13 LAP retrieval algorithm for the operational processing of GOES sounder data. The GOES-13 LAP retrievals are compared with five different truth measurements: radiosonde observation (raob) and microwave radiometer-measured total precipitable water (TPW) at the Atmospheric Radiation Measurement Cloud and Radiation Testbed site, conventional raob, TPW measurements from the global positioning systemintegrated precipitable water NOAA network, and TPW measurements from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The results show that with the GOES-R ABI LAP retrieval algorithm, the GOES-13 sounder provides better water vapor profiles than the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast fields at the levels between 300 and 700 hPa. The root-mean-square error (RMSE) and standard deviation (STD) of the GOES-13 sounder TPW are consistently reduced from those of the GFS forecast no matter which measurements are used as the truth. These substantial improvements indicate that the GOES-R ABI LAP retrieval algorithm is well prepared to provide continuity of quality to some of the current GOES sounder products, and the algorithm can be transferred to process the current GOES sounder measurements for operational product generation.

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Section: ) the Measurements From The Arm Cart Sitementioning

confidence: 99%

Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder

Lee

et al. 2014

Journal of Atmospheric and Oceanic Technology

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“…The collocated points of the radiosonde and GOES-10 retrievals were determined using a maximum distance of 50 km, following the methodology proposed by Fuelberg and Olson (1991) and Rao and Fuelberg (1998). It is important to clarify that the validations were performed at the second sweep sector of GOES-10, as shown in figure 1(b).…”

Section: Data and Methodologiesmentioning

confidence: 99%

“…This is possible because the radiosonde value, which is used as reference, represents temperature and humidity at a specific atmospheric pressure level (local value), while the satellite value is a volumetric observation. Therefore, it is expected that integrated values in the column, such as precipitable water, are more consistent to be compared with local observations (Fuelberg and Olson 1991). Additionally, TPW is one of the most important parameters widely used to assess the amount of water vapour in the atmosphere.…”

Section: Total Precipitable Watermentioning

confidence: 98%

Regionalization of the GOES-10 retrieval algorithm for tropical South America

Gonçalves

Costa

Angelis

et al. 2012

International Journal of Remote Sensing

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“…As noted earlier, the days 17-18 June 1986 contained large horizontal and vertical humidity gradients. Fuelberg et al (1991) present a detailed synoptic discussion of the case. The brief description presented here utilizes the LAMPS model run.…”

Section: Case Studymentioning

confidence: 99%

“…Parameters such as precipitable water, lifted index, and potential temperature could then be calculated from these profiles. Major limitations of their algorithm are the necessity of a first guess profile and the fact that the final retrieval tends to retain features of this first guess (Hayden 1988;Fuelberg and Olson 1991). Split-window techniques attempt to exploit the differential absorption of water vapor in two adjacent bandpasses.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing Convective Instability fromGOES-8Radiances

Rao

Fuelberg

1997

J. Appl. Meteor.

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Statistical algorithms are developed to diagnose the vertical change in equivalent potential temperature (⌬⌰ e ) between 920 and 620 hPa from GOES-8 radiance data. The models are prepared using a training dataset of radiosonde releases from 10 United States cities. Simulated GOES-8 channel brightness temperatures are calculated from these soundings. The training data are stratified into several subsets (depending on time and location). Models trained only on 0000 or 1200 UTC data explain approximately 7% more of the variance in observed ⌬⌰ e than those trained on both 0000 and 1200 UTC. Values of R 2 from models using training data from only one are superior to those trained on multiple stations. Inclusion of the imager channels adds little information to the algorithms.These models then are applied to data from the Limited Area Mesoscale Prediction System model to see which performs consistently better over diurnally varying conditions. Models trained only with 0000 UTC data give the best results, explaining between 63% and 81% of the variance in the independent data. The model that performed best is studied further. Biases are present when this model is applied to times other than 0000 UTC. These biases are caused by temperature differences between the 0000 UTC training data and those at the times being examined. Strong regional biases also occur when a model trained on only one location is applied to a large area. A second model is incorporated into the procedure to reduce this bias. The two-model algorithm explains more variance than the initial one-model version (93% vs 77%), and the area of strong regional bias is greatly reduced.This statistical procedure for ⌬⌰ e is then tested on observed GOES-8 data. A new statistical model is formed using the observed GOES-8 brightness temperatures and ⌬⌰ e 's calculated from collocated radiosonde observations. The new model yields an R 2 of 67% when applied to an independent dataset. This value is smaller than those from the models using simulated data, most likely due to several additional sources of discrepancy. Finally, simulated GOES-7 Visible Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) radiances are used to prepare a ⌬⌰ e algorithm. The VAS model explains approximately 5% less variance than its GOES-8 counterpart, due to the reduced vertical resolution available on VAS.These analyses show that regionally trained regression models can accurately diagnose convective instability while using relatively little computational time.

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An Assessment of VAS-Derived Retrievals and Parameters Used in Thunderstorm Forecasting

Cited by 10 publications

References 0 publications

Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder

Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder

Regionalization of the GOES-10 retrieval algorithm for tropical South America

Diagnosing Convective Instability fromGOES-8Radiances

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