Evaluation of Seven Near-Real-Time Satellite-Based Precipitation Products for Wet Seasons in the Nierji Basin, China

Yanhong Dou,Huicheng Zhou,Lei Ye,Chi Zhang,Jiayan Zhang

doi:10.3390/rs13224552

Yanhong Dou, Huicheng Zhou + Show 3 more

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https://doi.org/10.3390/rs13224552

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Journal: Remote Sensing	Publication Date: Nov 12, 2021
Citations: 3	License type: CC BY 4.0

Affiliation: Dalian University of Technology

Abstract

This study evaluated and intercompared seven near-real-time (NRT) versions of satellite-based precipitation products (SPPs) with latencies of less than one day, including GSMaP-NRT, GSMaP-Gauge-NRT, GSMaP-NOW, IMERG-Early, IMERG-Late, TMPA 3B42RT, and PERSIANN-CCS for wet seasons from 2008 to 2019 in a typical middle–high latitude temperate monsoon climate basin, namely, the Nierji Basin in China, in four aspects: flood sub-seasons, rainfall intensities, precipitation events, and hydrological utility. Our evaluation shows that the cell-scale and area-scale intercomparison ranks of NRT SPPs are similar in these four aspects. The performances of SPPs at the areal scale, at the event scale, and with light magnitude are better than those at the cell scale, at the daily scale, and with heavy magnitude, respectively. Most SPPs are similar in terms of their Pearson Correlation Coefficient (CC). The main difference between SPPs is in terms of their root-mean-square error (RMSE). The worse performances of TMPA 3B42RT are mainly caused by the poor performances during main flood seasons. The worst performances of PERSIANN-CCS are primarily reflected by the lowest CC and the underestimation of precipitation. Though GSMaP-NOW has the highest RMSE and overestimates precipitation, it can reflect the precipitation variation, as indicated by the relatively high CC. The differences among SPPs are more significant in pre-flood seasons and less significant in post-flood seasons. These results can provide valuable guidelines for the selection, correction, and application of NRT SPPs and contribute to improved insight into NRT-SPP retrieval algorithms.

Highlights

Precipitation is an essential component of the hydrological circle [1,2]
Precipitation stations are sparse in most areas of the Nierji Basin and occasionally malfunction, so there is an urgent need for satellite-based precipitation products (SPPs)
The points in the Taylor diagrams of the pre- and post-flood seasons are more scattered and more centralized, respectively, than the points of the main flood season, meaning that the differences among SPPs are more significant in pre-flood seasons but slighter in post-flood seasons, whether at the cell scale or the areal scale

Summary

Introduction

Precipitation is an essential component of the hydrological circle [1,2]. The hydrological forecasting driven by precipitation plays a crucial role in water resources management [3] and flood warning and precautions [4], in wet seasons when precipitation occurs frequently. Ground-based rainfall observations, such as gauges [5,6] and radars [7,8], have been widely used in hydrological forecasting due to their fine temporal resolution and accurate estimates at regional scales. In the current rapid proliferation of meteorologic and hydrologic measuring techniques [9], indirect satellite-based precipitation products (SPPs) have made it possible to obtain global maps of precipitation at satisfactory spatial (finer than 0.25◦ ) and temporal (shorter than daily) scales. SPPs at a global scale are important because they are the supplements of ground-based rainfall observations, and because they can be used to improve the numerical weather prediction model’s initial conditions to accurately predict severe weather events such as tropical cyclones and Remote Sens.

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