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Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation

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  • 1 Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
  • | 2 NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 3 NOAA/NESDIS/STAR/Advanced Satellite Products Branch, Madison, Wisconsin
  • | 4 Institute of Atmospheric Sciences and Climate, National Research Council, Rome, Italy
  • | 5 University of Oklahoma, Norman, Oklahoma
  • | 6 National Oceanic and Atmospheric Administration, Norman, Oklahoma
  • | 7 Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, College Park, Maryland
  • | 8 Laboratoire de Météorologie Dynamique, École Polytechnique, CNRS, Palaiseau, France
  • | 9 National Aeronautics and Space Administration, Washington, D.C.
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Abstract

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.

© 2021 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: Lisa Milani, lisa.milani@nasa.gov

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

This article is included in the 12th International Precipitation Conference (IPC12) Special Collection.

Abstract

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.

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Corresponding author: Lisa Milani, lisa.milani@nasa.gov

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

This article is included in the 12th International Precipitation Conference (IPC12) Special Collection.

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