Integration and Ocean-Based Prelaunch Validation of GOES-R Advanced Baseline Imager Legacy Atmospheric Products

Hua Xie * I.M. Systems Group, Inc., NOAA/NESDIS/STAR, College Park, Maryland

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Nicholas R. Nalli * I.M. Systems Group, Inc., NOAA/NESDIS/STAR, College Park, Maryland

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Shanna Sampson Riverside Technology, Inc., and I.M. Systems Group, Inc., NOAA/NESDIS/STAR, College Park, Maryland

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Walter W. Wolf NOAA/NESDIS/STAR, College Park, Maryland

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Jun Li Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Timothy J. Schmit NOAA/NESDIS/STAR, Madison, Wisconsin

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Christopher D. Barnet NOAA/NESDIS/STAR, College Park, Maryland

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Everette Joseph ** NOAA Center for Atmospheric Sciences, Howard University, Washington, D.C.

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Vernon R. Morris ** NOAA Center for Atmospheric Sciences, Howard University, Washington, D.C.

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Fanglin Yang * I.M. Systems Group, Inc., NOAA/NESDIS/STAR, College Park, Maryland

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Abstract

An ocean-based prelaunch evaluation of the Geostationary Operational Environmental Satellite (GOES)-R series Advanced Baseline Imager (ABI) legacy atmospheric profile (LAP) products is conducted using proxy data based upon the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation satellite. SEVIRI-based LAP temperature and moisture profile retrievals are validated against in situ correlative data obtained over the open ocean from multiple years of the National Oceanic and Atmospheric Administration (NOAA) Aerosols and Ocean Science Expeditions (AEROSE). The NOAA AEROSE data include dedicated radiosonde observations (RAOBs) launched from the NOAA ship Ronald H. Brown over the tropical Atlantic: a region optimally situated within the full-disk scanning range of SEVIRI and one of great meteorological importance as the main development area of Atlantic hurricanes. The most recent versions of the GOES-R Algorithm Working Group team algorithms (e.g., cloud mask, aerosol detection products, and LAP) implemented within the algorithms integration team framework (the NOAA operational system that will host these operational product algorithms) are used in the analyses. Forecasts from the National Centers for Environmental Prediction Global Forecasting System (NCEP GFS) are used for the LAP regression and direct comparisons. The GOES-R LAP retrievals are found to agree reasonably with the AEROSE RAOB observations, and overall retrievals improve both temperature and moisture against computer model NCEP GFS outputs. The validation results are then interpreted within the context of a difficult meteorological regime (e.g., Saharan air layers and dust) coupled with the difficulty of using a narrowband imager for the purpose of atmospheric sounding.

Corresponding author address: Hua Xie, I.M. Systems Group, Inc., NOAA/NESDIS/STAR, NOAA Center for Weather and Climate Prediction (NCWCP), 5830 University Research Ct., College Park, MD 20740. E-mail: hua.xie@noaa.gov

Abstract

An ocean-based prelaunch evaluation of the Geostationary Operational Environmental Satellite (GOES)-R series Advanced Baseline Imager (ABI) legacy atmospheric profile (LAP) products is conducted using proxy data based upon the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation satellite. SEVIRI-based LAP temperature and moisture profile retrievals are validated against in situ correlative data obtained over the open ocean from multiple years of the National Oceanic and Atmospheric Administration (NOAA) Aerosols and Ocean Science Expeditions (AEROSE). The NOAA AEROSE data include dedicated radiosonde observations (RAOBs) launched from the NOAA ship Ronald H. Brown over the tropical Atlantic: a region optimally situated within the full-disk scanning range of SEVIRI and one of great meteorological importance as the main development area of Atlantic hurricanes. The most recent versions of the GOES-R Algorithm Working Group team algorithms (e.g., cloud mask, aerosol detection products, and LAP) implemented within the algorithms integration team framework (the NOAA operational system that will host these operational product algorithms) are used in the analyses. Forecasts from the National Centers for Environmental Prediction Global Forecasting System (NCEP GFS) are used for the LAP regression and direct comparisons. The GOES-R LAP retrievals are found to agree reasonably with the AEROSE RAOB observations, and overall retrievals improve both temperature and moisture against computer model NCEP GFS outputs. The validation results are then interpreted within the context of a difficult meteorological regime (e.g., Saharan air layers and dust) coupled with the difficulty of using a narrowband imager for the purpose of atmospheric sounding.

Corresponding author address: Hua Xie, I.M. Systems Group, Inc., NOAA/NESDIS/STAR, NOAA Center for Weather and Climate Prediction (NCWCP), 5830 University Research Ct., College Park, MD 20740. E-mail: hua.xie@noaa.gov
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