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Incorporating NASA Spaceborne Radar Data into NOAA National Mosaic QPE System for Improved Precipitation Measurement: A Physically Based VPR Identification and Enhancement Method

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  • 1 * School of Meteorology, and Advanced Radar Research Center, University of Oklahoma, and Hydrometeorology and Remote Sensing Laboratory, National Weather Center, Norman, Oklahoma
  • | 2 Advanced Radar Research Center, University of Oklahoma, and Hydrometeorology and Remote Sensing Laboratory, National Weather Center, Norman, Oklahoma
  • | 3 Advanced Radar Research Center, University of Oklahoma, and Hydrometeorology and Remote Sensing Laboratory, National Weather Center, and NOAA/National Severe Storms Laboratory, Norman, Oklahoma
  • | 4 Advanced Radar Research Center, and School of Civil Engineering and Environmental Sciences, University of Oklahoma, and Hydrometeorology and Remote Sensing Laboratory, National Weather Center, Norman, Oklahoma
  • | 5 NOAA/National Severe Storms Laboratory, Norman, Oklahoma
  • | 6 ** School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 7 Advanced Radar Research Center, University of Oklahoma, and Hydrometeorology and Remote Sensing Laboratory, National Weather Center, Norman, Oklahoma, and State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China
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Abstract

This study proposes an approach that identifies and corrects for the vertical profile of reflectivity (VPR) by using Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) measurements in the region of Arizona and southern California, where the ground-based Next Generation Weather Radar (NEXRAD) finds difficulties in making reliable estimations of surface precipitation amounts because of complex terrain and limited radar coverage. A VPR identification and enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically based parameterization is employed to obtain a representative VPR at S band from the TRMM PR measurement at Ku band. Then the representative VPR is convolved with ground radar beam sampling properties to compute apparent VPRs for enhancing NEXRAD quantitative precipitation estimation (QPE). The VPR-IE methodology is evaluated with several stratiform precipitation events during the cold season and is compared to two other statistically based correction methods, that is, the TRMM PR–based rainfall calibration and a range ring–based adjustment scheme. The results show that the VPR-IE has the best overall performance and provides much more accurate surface rainfall estimates than the original ground-based radar QPE. The potential of the VPR-IE method could be further exploited and better utilized when the Global Precipitation Measurement Mission's dual-frequency PR is launched in 2014, with anticipated accuracy improvements and expanded latitude coverage.

Corresponding author address: Yang Hong, School of Civil Engineering and Environmental Sciences, University of Oklahoma, Atmospheric Radar Research Center at the National Weather Center, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: yanghong@ou.edu

Abstract

This study proposes an approach that identifies and corrects for the vertical profile of reflectivity (VPR) by using Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) measurements in the region of Arizona and southern California, where the ground-based Next Generation Weather Radar (NEXRAD) finds difficulties in making reliable estimations of surface precipitation amounts because of complex terrain and limited radar coverage. A VPR identification and enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically based parameterization is employed to obtain a representative VPR at S band from the TRMM PR measurement at Ku band. Then the representative VPR is convolved with ground radar beam sampling properties to compute apparent VPRs for enhancing NEXRAD quantitative precipitation estimation (QPE). The VPR-IE methodology is evaluated with several stratiform precipitation events during the cold season and is compared to two other statistically based correction methods, that is, the TRMM PR–based rainfall calibration and a range ring–based adjustment scheme. The results show that the VPR-IE has the best overall performance and provides much more accurate surface rainfall estimates than the original ground-based radar QPE. The potential of the VPR-IE method could be further exploited and better utilized when the Global Precipitation Measurement Mission's dual-frequency PR is launched in 2014, with anticipated accuracy improvements and expanded latitude coverage.

Corresponding author address: Yang Hong, School of Civil Engineering and Environmental Sciences, University of Oklahoma, Atmospheric Radar Research Center at the National Weather Center, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: yanghong@ou.edu
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