Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #357403

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Comprehensive evaluation of GPM-IMERG, CMORPH and TMPA precipitation products with gauged rainfall over mainland China

item WEI, G. - Hohai University
item LU, H. - Hohai University
item Crow, Wade
item ZHU, Y. - Hohai University
item WANG, J. - Hohai University
item SU, J. - Hohai University

Submitted to: Advances in Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2018
Publication Date: 12/15/2018
Citation: Wei, G., Lu, H., Crow, W.T., Zhu, Y., Wang, J., Su, J. 2018. Comprehensive evaluation of GPM-IMERG, CMORPH and TMPA precipitation products with gauged rainfall over mainland China. Advances in Meteorology. 2018:3024190.

Interpretive Summary: Remotely-sensed estimates of rainfall accumulation are valuable for agricultural drought monitoring and water resource assessment within regions of the world lacking adequate ground rain gauge coverage. However, before these estimates can be used with confidence, they must first be objectively evaluated for a wide range of land cover and climate conditions. This manuscript describes the evaluation of multiple state-of-the-art satellite-based rainfall accumulation products against ground rain-gauge observations obtained within mainland China. These comparisons yielded valuable insights regarding the relative performance of each product and identified specific instances in which the performance of a particular product was unacceptably poor. Results from this paper will be used to improve future versions of these rainfall products and help users select the best product for their specific application.

Technical Abstract: The accurate assessment of newly released precipitation data sets is important for benchmarking a product’s continued improvement and future development. Here, four satellite-based precipitation products including the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) V05B, IMERG V04A, the Tropical Rainfall Measuring Mission (TRMM) 3B42 and the Climate Prediction Center Morphing technique (CMORPH)-CRT are systematically evaluated against gauge precipitation observations at multiple spatiotemporal scales from March 2014 to February 2017 over China. Categorical verification techniques and statistical methods are used to quantize their performance. Results illustrate that: (1) except for IMERG V04A’s severe underestimation over the Tibetan Plateau (TP) and Xinjiang (XJ) with high negative relative biases (RBs) and CMORPH-CRT’s overestimation over XJ with high positive RB, the four satellite-based precipitation products generally capture the same spatial patterns of precipitation over China. (2) At the annual scale over China, the IMERG products does not show an advantage over its predecessor (TRMM 3B42) in terms of RMSEs, RRMSEs, and Rs; meanwhile, the performance of IMERG products is worse than TRMM 3B42 in spring and summer according to the RMSE, RRMSE and R metrics. Between the two IMERG products, IMERG V05B shows the anticipated improvement (over IMERG V04A) with a decrease in RMSE from 0.4496 to 0.4097 mm/day, a decrease of RRMSE from 16.95% to 15.44%, and an increase of R from 0.9689 to 0.9759 during annual daily average precipitation analysis. Similar results are obtained at the seasonal scale. Among the four satellite products, CMORPH-CRT shows the worst seasonal performance with the highest RMSE (0.6247 mm/day), RRMSE (23.55%) and lowest R (0.9343) over China. (3) Over XJ and TP, IMERG V05B clearly improve the strong underestimation of precipitation in IMERG V04A with the RBs of 5.2% vs. -21.8% over XJ, and 2.78% vs. -46% over TP. Results at the annual scale are similar to those obtained at the seasonal scale, except for summer results over XJ. While, over the remaining sub-regions, the two IMERG products have a close performance, meanwhile, IMERG V04A slightly improves IMERG V05B’s overestimation according to RBs (except for HN) at the annual scale. However, all four products are unreliable over XJ at both an annual and seasonal scale. (4) Across all products, TRMM 3B42 best reproduces the probability density function (PDF) of daily precipitation intensity. (5) According to the categorical verification technique in this study, both IMERG products yield better results for the detection of precipitation events on the basis of probability of detection (POD) and critical success index (CSI) categorical evaluations compared to TRMM 3B42 and CMORPH-CRT over China and across most of the sub-regions. However, all four products have room to further improvement, especially in high-latitude and dry climate regions. These findings provide valuable feedback for both IMERG algorithm developers and data set users.