Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events

Abstract The National Aeronautics and Space Administration‐Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) mission consists of a multisatellite constellation that provides real‐time or near‐real‐time global observations of rain and snow. In this study, GPM Level 3 Integrate...

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Main Authors: Xuanli Li, John R. Mecikalski, Jayanthi Srikishen, Bradley Zavodsky, Walter A. Petersen
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2020-05-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
Online Access:https://doi.org/10.1029/2019MS001618
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spelling doaj-fed1de2d120b48cb82daabe15ca5fcf22020-11-25T03:45:57ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662020-05-01125n/an/a10.1029/2019MS001618Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation EventsXuanli Li0John R. Mecikalski1Jayanthi Srikishen2Bradley Zavodsky3Walter A. Petersen4Earth System Science Center University of Alabama in Huntsville Huntsville AL USADepartment of Atmospheric and Earth Science University of Alabama in Huntsville Huntsville AL USAUniversities Space Research Association Huntsville AL USANASA Marshall Space Flight Center Huntsville AL USANASA Marshall Space Flight Center Huntsville AL USAAbstract The National Aeronautics and Space Administration‐Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) mission consists of a multisatellite constellation that provides real‐time or near‐real‐time global observations of rain and snow. In this study, GPM Level 3 Integrated Multi‐satellitE Retrievals for GPM (IMERG) and Level 2 GPM Microwave Imager Goddard Profiling rainfall products have been assimilated into the Weather Research and Forecasting model using the community Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments have been conducted and compared to demonstrate the impact of rain rate data assimilation on forecasts of heavy rainfall related to Hurricane Harvey (2017) and moderate to light rainfall observed during the GPM Integrated Precipitation and Hydrology Experiment field campaign. The results indicate that both GPM Microwave Imager Goddard Profiling and IMERG data could generate apparent increments in moisture, temperature, wind, and pressure fields for Hurricane Harvey, which led to significant improvement in the precipitation forecast. Frequent (every 3 hr) assimilation of IMERG data also positively impacted the short‐term precipitation forecast skill for the Integrated Precipitation and Hydrology Experiment moderate to light rain events. However, results also indicate that the impact of rain data assimilation was limited for a system that had a small horizontal dimension with low rain rates and within a relatively stable synoptic environment.https://doi.org/10.1029/2019MS001618data assimilationquantitative precipitation forecastnumerical modeling
collection DOAJ
language English
format Article
sources DOAJ
author Xuanli Li
John R. Mecikalski
Jayanthi Srikishen
Bradley Zavodsky
Walter A. Petersen
spellingShingle Xuanli Li
John R. Mecikalski
Jayanthi Srikishen
Bradley Zavodsky
Walter A. Petersen
Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events
Journal of Advances in Modeling Earth Systems
data assimilation
quantitative precipitation forecast
numerical modeling
author_facet Xuanli Li
John R. Mecikalski
Jayanthi Srikishen
Bradley Zavodsky
Walter A. Petersen
author_sort Xuanli Li
title Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events
title_short Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events
title_full Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events
title_fullStr Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events
title_full_unstemmed Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events
title_sort assimilation of gpm rain rate products with gsi data assimilation system for heavy and light precipitation events
publisher American Geophysical Union (AGU)
series Journal of Advances in Modeling Earth Systems
issn 1942-2466
publishDate 2020-05-01
description Abstract The National Aeronautics and Space Administration‐Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) mission consists of a multisatellite constellation that provides real‐time or near‐real‐time global observations of rain and snow. In this study, GPM Level 3 Integrated Multi‐satellitE Retrievals for GPM (IMERG) and Level 2 GPM Microwave Imager Goddard Profiling rainfall products have been assimilated into the Weather Research and Forecasting model using the community Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments have been conducted and compared to demonstrate the impact of rain rate data assimilation on forecasts of heavy rainfall related to Hurricane Harvey (2017) and moderate to light rainfall observed during the GPM Integrated Precipitation and Hydrology Experiment field campaign. The results indicate that both GPM Microwave Imager Goddard Profiling and IMERG data could generate apparent increments in moisture, temperature, wind, and pressure fields for Hurricane Harvey, which led to significant improvement in the precipitation forecast. Frequent (every 3 hr) assimilation of IMERG data also positively impacted the short‐term precipitation forecast skill for the Integrated Precipitation and Hydrology Experiment moderate to light rain events. However, results also indicate that the impact of rain data assimilation was limited for a system that had a small horizontal dimension with low rain rates and within a relatively stable synoptic environment.
topic data assimilation
quantitative precipitation forecast
numerical modeling
url https://doi.org/10.1029/2019MS001618
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