Comprehensive Evaluation of High-Resolution Satellite-Based Precipitation Products over China

Hao Guo,Banghui Yang,Phillip Stepanian,Sheng Chen,Junjun Hu,Anming Bao

doi:10.3390/atmos7010006

Abstract

Characterizing the errors in satellite-based precipitation estimation products is crucial for understanding their effects in hydrological applications. Six precipitation products derived from three algorithms are comprehensively evaluated against gauge data over mainland China from December 2006 to November 2010. These products include three satellite-only estimates: the Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK), the Climate Prediction Center (CPC) MORPHing (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), as well as their gauge-corrected counterparts: the GSMaP Gauge-calibrated Product (GSMaP_Gauge), bias-corrected CMORPH (CMORPH_CRT), and PERSIANN Climate Data Record (PERSIANN-CDR). Overall, the bias-correction procedures largely reduce various errors for the three groups of satellite-based precipitation products. GSMaP_Gauge produces better fractional coverage with the highest correlation (0.95) and the lowest RMSE (0.53 mm/day) but also high RB (15.77%). In general, CMORPH_CRT amounts are closer to the gauge reference. CMORPH shows better performance than GSMaP_MVK and PERSIANN with the highest CC (0.82) and the lowest RMSE (0.93 mm/day), but also presents a relatively high RB (−19.60%). In winter, all six satellite precipitation estimates have comparatively poor capability, with the IR-based PERSIANN_CDR exhibiting the closest performance to the gauge reference. Both satellite-only and gauge-corrected satellite products show poor capability in detecting occurrence of precipitation with a low POD (<50%) and CSI (<35%) and a high FAR (>40%).

Highlights

Precipitation measurements provide useful information for evaluating the global water cycle and energy balance and supply crucial input for hydrological applications [1]
All satellite-based datasets can generally capture the spatial pattern of four-year mean daily precipitation (Figure 2b–h), but there are pronounced differences between the satellite-only precipitation products and bias-corrected products
GSMaP_Gauge, CMORPH_CRT, and PERSIANN_CDR, capture the spatial pattern of precipitation over China fairly well, while the satellite-only products perform much worse than the bias-corrected products

Summary

Introduction

Precipitation measurements provide useful information for evaluating the global water cycle and energy balance and supply crucial input for hydrological applications [1]. Obtaining reliable surface precipitation measurements is challenging due to the heterogeneity of precipitation at different spatiotemporal scales. Precipitation estimates obtained from rain gauges are generally. Atmosphere 2016, 7, 6 considered the most accurate estimates since they directly measure the precipitation. Rain gauges tend to be unevenly distributed and gauge coverage is often quite sparse over remote/rural areas. This sparsity constitutes a significant challenge because gauge-based rainfall estimates are often not representative of nearby rainfall rates in precipitation events containing large gradients of precipitation rates [2]. With the development of remote sensing technology, it is possible to obtain

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Results

Conclusion