Performance of IMERG as a Function of Spatiotemporal Scale.

The Integrated Multi-satellitE Retrievals for GPM (IMERG), a global high-resolution gridded precipitation data set, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scale and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final run of MERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5 ) and accumulation periods (from 0.5 h to 24 h) over a region in the southeastern US. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multi satellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.

[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]  Y. Hong,et al.  Understanding Overland Multisensor Satellite Precipitation Error in TMPA-RT Products , 2017 .

[3]  Yudong Tian,et al.  Systematic anomalies over inland water bodies in satellite‐based precipitation estimates , 2007 .

[4]  Jian Zhang,et al.  Evaluation and Uncertainty Estimation of NOAA/NSSL Next-Generation National Mosaic Quantitative Precipitation Estimation Product (Q2) over the Continental United States , 2013 .

[5]  W. Briggs Statistical Methods in the Atmospheric Sciences , 2007 .

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

[7]  Yang Hong,et al.  Probabilistic precipitation rate estimates with ground‐based radar networks , 2015 .

[8]  Robert F. Adler,et al.  Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA , 2009 .

[9]  Yudong Tian,et al.  An improved procedure for the validation of satellite-based precipitation estimates , 2015 .

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

[11]  Y. Hong,et al.  Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China , 2013 .

[12]  Kuolin Hsu,et al.  Precipitation Estimation from Remotely Sensed Data Using Deep Neural Network , 2015 .

[13]  Ali Tokay,et al.  A Novel Approach to Identify Sources of Errors in IMERG for GPM Ground Validation , 2016 .

[14]  Yudong Tian,et al.  Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications , 2007 .

[15]  Emmanouil N. Anagnostou,et al.  Evaluation of Global Satellite Rainfall Products over Continental Europe , 2012 .

[16]  R. Roca,et al.  Comparing Satellite and Surface Rainfall Products over West Africa at Meteorologically Relevant Scales during the AMMA Campaign Using Error Estimates , 2010 .

[17]  Yang Hong,et al.  Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? , 2013 .

[18]  Yudong Tian,et al.  Performance Metrics, Error Modeling, and Uncertainty Quantification , 2016 .

[19]  Yudong Tian,et al.  Modeling errors in daily precipitation measurements: Additive or multiplicative? , 2013 .

[20]  Yudong Tian,et al.  Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates , 2012 .

[21]  S. Sarachi,et al.  A Statistical Model for the Uncertainty Analysis of Satellite Precipitation Products , 2015 .

[22]  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 .

[23]  F. Hossain,et al.  Investigating Error Metrics for Satellite Rainfall Data at Hydrologically Relevant Scales , 2008 .

[24]  Matthew Rodell,et al.  Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System Land Surface States , 2005 .

[25]  Yang Hong,et al.  Intercomparison of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations for a Season of Record Rainfall , 2010 .

[26]  Y. Hong,et al.  Impact of sub‐pixel rainfall variability on spaceborne precipitation estimation: evaluating the TRMM 2A25 product , 2015 .

[27]  Jian Zhang,et al.  National mosaic and multi-sensor QPE (NMQ) system description, results, and future plans , 2011 .

[28]  Yang Hong,et al.  Statistical and Hydrological Comparisons between TRMM and GPM Level-3 Products over a Midlatitude Basin: Is Day-1 IMERG a Good Successor for TMPA 3B42V7? , 2016 .

[29]  Yudong Tian,et al.  An Error Model for Uncertainty Quantification in High-Time-Resolution Precipitation Products , 2014 .

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

[31]  Yang Hong,et al.  Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar–Based National Mosaic QPE , 2012 .

[32]  E. Anagnostou,et al.  Error Analysis of Satellite Precipitation Products in Mountainous Basins , 2014 .

[33]  Y. Hong,et al.  Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System , 2004 .

[34]  P. Xie,et al.  Kalman Filter–Based CMORPH , 2011 .

[35]  Misako Kachi,et al.  Verification of High-Resolution Satellite-Based Rainfall Estimates around Japan Using a Gauge-Calibrated Ground-Radar Dataset , 2009 .

[36]  Yang Hong,et al.  Early assessment of Integrated Multi-satellite Retrievals for Global Precipitation Measurement over China , 2016 .

[37]  Javier Tomasella,et al.  Propagation of satellite precipitation uncertainties through a distributed hydrologic model: A case study in the Tocantins–Araguaia basin in Brazil , 2015 .

[38]  J. Janowiak,et al.  COMPARISON OF NEAR-REAL-TIME PRECIPITATION ESTIMATES FROM SATELLITE OBSERVATIONS AND NUMERICAL MODELS , 2007 .