Evaluation of the GPM-IMERG V06 Final Run Products for Monthly/Annual Precipitation under the Complex Climatic and Topographic Conditions of China

Ying Zhang aCAS State Key Laboratory of Forest and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
bAgronomy College, Shenyang Agricultural University, Shenyang, China
cQingyuan Forest CERN, National Observation and Research Station, Liaoning Provinces, Shenyang, China
dKey Laboratory for Management of Non-Commercial Forests, Liaoning Province, Shenyang, China

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Xiao Zheng aCAS State Key Laboratory of Forest and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
cQingyuan Forest CERN, National Observation and Research Station, Liaoning Provinces, Shenyang, China
dKey Laboratory for Management of Non-Commercial Forests, Liaoning Province, Shenyang, China

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Xiufen Li aCAS State Key Laboratory of Forest and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
bAgronomy College, Shenyang Agricultural University, Shenyang, China
cQingyuan Forest CERN, National Observation and Research Station, Liaoning Provinces, Shenyang, China
dKey Laboratory for Management of Non-Commercial Forests, Liaoning Province, Shenyang, China

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Jiaxin Lyu aCAS State Key Laboratory of Forest and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
bAgronomy College, Shenyang Agricultural University, Shenyang, China
cQingyuan Forest CERN, National Observation and Research Station, Liaoning Provinces, Shenyang, China
dKey Laboratory for Management of Non-Commercial Forests, Liaoning Province, Shenyang, China

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Lanlin Zhao aCAS State Key Laboratory of Forest and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
cQingyuan Forest CERN, National Observation and Research Station, Liaoning Provinces, Shenyang, China
dKey Laboratory for Management of Non-Commercial Forests, Liaoning Province, Shenyang, China

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Abstract

The new-generation multisatellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG), version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPM-IMERG V06 Final Run products in different climatic and topographical regions of China for the 2014–20 period. This study showed that 1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient R >0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April–September) (RBias = 7.41%) than during the dry season (RBias = 13.65%). 2) GPM-IMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. 3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world.

Significance Statement

The purpose of this study was to better understand the capability of GPM-IMERG for precipitation estimation and the causes of errors. GPM-IMERG performed well when estimating precipitation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. Climate, followed by elevation, played a leading role in GPM-IMERG accuracy in China. Our results could provide a greater understanding of the accuracy of GPM-IMERG precipitation estimation in the different regions of China and can be applied to water resource management, afforestation (or reforestation) projects, and so on, in areas worldwide where meteorological stations are scarce.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiao Zheng, xiaozheng@iae.ac.cn

Abstract

The new-generation multisatellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG), version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPM-IMERG V06 Final Run products in different climatic and topographical regions of China for the 2014–20 period. This study showed that 1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient R >0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April–September) (RBias = 7.41%) than during the dry season (RBias = 13.65%). 2) GPM-IMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. 3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world.

Significance Statement

The purpose of this study was to better understand the capability of GPM-IMERG for precipitation estimation and the causes of errors. GPM-IMERG performed well when estimating precipitation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. Climate, followed by elevation, played a leading role in GPM-IMERG accuracy in China. Our results could provide a greater understanding of the accuracy of GPM-IMERG precipitation estimation in the different regions of China and can be applied to water resource management, afforestation (or reforestation) projects, and so on, in areas worldwide where meteorological stations are scarce.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiao Zheng, xiaozheng@iae.ac.cn
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