Evaluating IMERG V04 Final Run for Monitoring Three Heavy Rain Events Over Mainland China in 2016

Predicting and monitoring the spatiotemporal characteristics of heavy rain events are important to hazard preparedness, mitigation efforts, and local water resource management. Using three data sets, namely, the daily rain product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 04 Final Run, the daily output from European Centre for Medium-Range Weather Forecasts reanalysis data Interim version (ERA-Interim), and the high-quality gauge-satellite merged precipitation product, the spatiotemporal patterns of three heavy rain events are investigated for the first time over China in 2016, with the objective of assessing the capability of IMERG product for monitoring heavy rain events. It is found that the daily IMERG Final Run can better capture the spatial and temporal characteristics of heavy rain compared with that from ERA-Interim, but it significantly overestimates the amounts of the heaviest rainfalls by 11%–85% over the example regions. The comparison of regional averaged precipitation demonstrates that time series of precipitation retrieved by the IMERG algorithm agree well with that from gauge-satellite merged data set, with differences less than 10 mm on most days over each region. The statistic metrics demonstrate that the IMERG Final Run has a strong potential for detecting heavy rain events but with a relatively large error. This letter may provide useful feedback and insights for further improving the precipitation retrieving algorithm and the application of such data sets.

[1]  Y. Hong,et al.  Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales , 2015 .

[2]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[3]  Dim Coumou,et al.  Increased record-breaking precipitation events under global warming , 2015, Climatic Change.

[4]  N. Rebora,et al.  Analysis and hindcast simulations of an extreme rainfall event in the Mediterranean area: The Genoa 2011 case , 2014 .

[5]  Yang Hong,et al.  Statistical assessment and hydrological utility of the latest multi-satellite precipitation analysis IMERG in Ganjiang River basin , 2017 .

[6]  Yan Shen,et al.  A high spatiotemporal gauge‐satellite merged precipitation analysis over China , 2014 .

[7]  Hui Lu,et al.  Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high‐density rain gauge network , 2017 .

[8]  Zhong Liu,et al.  Comparison of Integrated Multisatellite Retrievals for GPM (IMERG) and TRMM Multisatellite Precipitation Analysis (TMPA) Monthly Precipitation Products: Initial Results , 2016 .

[9]  S. Sorooshian,et al.  Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks , 1997 .

[10]  G. Huffman,et al.  Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation , 2015 .

[11]  J. Francis,et al.  Extreme summer weather in northern mid-latitudes linked to a vanishing cryosphere , 2014 .

[12]  F. Joseph Turk,et al.  J1.2 VALIDATION OF AN OPERATIONAL GLOBAL PRECIPITATION ANALYSIS AT SHORT TIME SCALES , 2002 .

[13]  Emmanouil N. Anagnostou,et al.  Improving Radar-Based Estimation of Rainfall over Complex Terrain , 2002 .

[14]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[15]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[16]  Amir AghaKouchak,et al.  From TRMM to GPM: How well can heavy rainfall be detected from space? , 2016 .

[17]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[18]  P. Xie,et al.  Performance of high‐resolution satellite precipitation products over China , 2010 .

[19]  Venkat Lakshmi,et al.  A global assessment of the timing of extreme rainfall from TRMM and GPM for improving hydrologic design , 2016 .

[20]  Yang Hong,et al.  Evaluation of High-Resolution Precipitation Estimates from Satellites during July 2012 Beijing Flood Event Using Dense Rain Gauge Observations , 2014, PloS one.

[21]  Edward J. Zipser,et al.  The global distribution of largest, deepest, and most intense precipitation systems , 2015 .

[22]  Zhou Zi-jiang,et al.  Quality assessment of hourly merged precipitation product over China , 2013 .

[23]  Qiang Zhang,et al.  Summer extreme precipitation in eastern China: Mechanisms and impacts , 2017 .

[24]  Yang Hong,et al.  Precipitation Spectra Analysis Over China With High-Resolution Measurements From Optimally Merged Satellite/Gauge Observations—Part I: Spatial and Seasonal Analysis , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Pertti Nurmi,et al.  Recommendations on the verification of local weather forecasts , 2003 .

[26]  A. Hou,et al.  The Global Precipitation Measurement Mission , 2014 .