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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #381203

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 and error-component analysis of four satellite-based precipitation estimates against gauged rainfall over mainland China

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

Submitted to: Advances in Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/13/2022
Publication Date: 5/10/2022
Citation: Wei, G., Lu, H., Crow, W.T., Zhu, Y., Su, J. 2022. Comprehensive evaluation and error-component analysis of four satellite-based precipitation estimates against gauged rainfall over mainland China. Advances in Meteorology. 2022:9070970.

Interpretive Summary: Recent advances in remote-sensing technology have significantly improved the quality of short-term (e.g., daily) rainfall accumulation estimates available from satellite-based sensors. These advances have greatly increased the number and variety of potential satellite-based datasets available for agricultural applications. At the same time, guidance regarding the best choice of dataset is severely lacking. This paper describes the ground-based validation of several new state-of-the-art, satellite-based rainfall accumulation datasets and evaluates their accuracy in a mid-latitude, agricultural region. As such, it quantifies the uncertainty of accumulation estimates provided by state-of-theart products and provides guidance regarding the best-available dataset for applications. This research will be used to improve the quality of global agricultural drought monitoring conducted by the USDA and partnering federal agencies.

Technical Abstract: The Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) V06 product was released in May 2019. Despite that fact that quantitative information about errors and the source of the errors within IMERG are of paramount importance for both the data developers and end users, IMERG V06 has not, to the best of our knowledge, been systematically assessed over mainland China. To this end, the final-run gauge-calibrated IMERG V06 (V06C) and un-calibrated IMERG V06 (V06UC) products are comprehensively evaluated here against 2088 precipitation gauges acquired between March 2014 and June 2018 over China. Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)- Climate Data Record (CDR) and the Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. Continuous statistical indices and an error decomposition scheme are used to quantify their performance. Key results are as follows. (1) Except for V06UC’s relatively high underestimation over the Tibetan Plateau (TP) and high overestimation over Xinjiang (XJ), Northeastern China (DB) and Northern China (HB), and CDR’s severely overestimation over TP, all four satellite-based precipitation products (V06UC, V06C, CDR and BLD) can generally capture the spatial pattern of precipitation over China. Moreover, the satellite-based precipitation estimates agree better with gauge observations over humid regions than that over semi-humid, semi-arid and arid regions. (2) Across all four products, CDR performs the worst with lowest the correlation against gauge observations (R) regardless of region. It also exhibits the highest Root Mean Square Errors (RMSEs) except for XJ, DB and HB sub-regions and the highest Mean Absolute Errors (MAEs) except for XJ and DB, the largest RB over China, and with the worst critical success index (CSI) and false alarm ratio (FAR) results over each region. Conversely, BLD is the best precipitation product with the highest values of R and lowest MAE, RMSE and RBs over the whole China and most sub-regions (i.e., TP, Northwestern China (XB), DB, HB, Yangtze River (CJ) Plain and Southeastern China (HN)), with the highest values of probability of detection (POD) over China and most sub-regions (except for XJ and TP), and the best CSI over each region. Between the two IMERG products, V06C has improved V06UC’s precipitation estimate according to the relatively larger values of R across all regions, smaller RMSEs and MAEs over China and most sub-regions (XJ, XB, DB, HB and CJ), and improved POD and CSI scores for all regions (for POD, except for HB region). Results indicate that the gauge calibration algorithm (GCA) used in IMERG has active effect in terms of R, POD and CSI. (3) Among all sub-regions, all four satellite-based precipitation estimates demonstrate their worst performance over the arid XJ region which exhibits the highest FAR and lowest POD and CSI values across all regions. (4) In terms of intensity distribution, for summer over China, the four satellite-based precipitation estimates generally overestimate the frequency of light precipitation and moderate precipitation events (< 25 mm/day) and underestimate heavy precipitation events (> 42 mm/day). (5) The relative bias ratio (RBR) analysis shows that the contribution of missed precipitation tends to be lower over wetter regions. And for the same climate region, the contribution of missed precipitation is clearly lower in summer than in winter. In summer, false precipitation dominates the total error, whereas missed and false precipitation are the two leading error sources in winter. Future algorithm refinement efforts should focus on decreasing FAR in summer and winter and improving missed snow events during the winter.