Editorial Type:
Article
Article Type:
Research Article

Improving the Quality of Heavy Precipitation Estimates from Satellite Passive Microwave Rainfall Retrievals

Veljko Petković Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Christian D. Kummerow Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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David L. Randel Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Jeffrey R. Pierce Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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John K. Kodros Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Print Publication:
01 Jan 2018
Collections:
Global Precipitation Measurement (GPM): Science and Applications
DOI:
https://doi.org/10.1175/JHM-D-17-0069.1
Page(s):
69–85
Restricted access

Abstract

Prominent achievements made in addressing global precipitation using satellite passive microwave retrievals are often overshadowed by their performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate precipitation measurements. This is especially true over land, with precipitation estimates being based on an observed mean relationship between high-frequency (e.g., 89 GHz) brightness temperature depression (i.e., the ice-scattering signature) and surface precipitation rate. This indirect relationship between the observed (brightness temperatures) and state (precipitation) vectors often leads to inaccurate estimates, with more pronounced biases (e.g., −30% over the United States) observed during extreme events. This study seeks to mitigate these errors by employing previously established relationships between cloud structures and large-scale environments such as CAPE, wind shear, humidity distribution, and aerosol concentrations to form a stronger relationship between precipitation and the scattering signal. The GPM passive microwave operational precipitation retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. The modified algorithm is allowed to use the large-scale environment to filter out a priori states that do not match the general synoptic condition relevant to the observation and thus reduces the difference between the assumed and observed variability in the ice-to-rain ratio. Using the ground Multi-Radar Multi-Sensor (MRMS) network over the United States, the results demonstrate outstanding potential in improving the accuracy of heavy precipitation over land. It is found that individual synoptic parameters can remove 20%–30% of existing bias and up to 50% when combined, while preserving the overall performance of the algorithm.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Veljko Petković, veljko@atmos.colostate.edu

This article is included in the Global Precipitation Measurement (GPM) special collection.

Abstract

Prominent achievements made in addressing global precipitation using satellite passive microwave retrievals are often overshadowed by their performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate precipitation measurements. This is especially true over land, with precipitation estimates being based on an observed mean relationship between high-frequency (e.g., 89 GHz) brightness temperature depression (i.e., the ice-scattering signature) and surface precipitation rate. This indirect relationship between the observed (brightness temperatures) and state (precipitation) vectors often leads to inaccurate estimates, with more pronounced biases (e.g., −30% over the United States) observed during extreme events. This study seeks to mitigate these errors by employing previously established relationships between cloud structures and large-scale environments such as CAPE, wind shear, humidity distribution, and aerosol concentrations to form a stronger relationship between precipitation and the scattering signal. The GPM passive microwave operational precipitation retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. The modified algorithm is allowed to use the large-scale environment to filter out a priori states that do not match the general synoptic condition relevant to the observation and thus reduces the difference between the assumed and observed variability in the ice-to-rain ratio. Using the ground Multi-Radar Multi-Sensor (MRMS) network over the United States, the results demonstrate outstanding potential in improving the accuracy of heavy precipitation over land. It is found that individual synoptic parameters can remove 20%–30% of existing bias and up to 50% when combined, while preserving the overall performance of the algorithm.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Veljko Petković, veljko@atmos.colostate.edu

This article is included in the Global Precipitation Measurement (GPM) special collection.

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  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621–638, doi:10.1175/BAMS-D-14-00174.1.

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