A Preliminary Assessment of the Gauge-Adjusted Near-Real-Time GSMaP Precipitation Estimate over Mainland China

Dekai Lu,Bin Yong

doi:10.3390/rs12010141

Abstract

The near-real-time satellite-derived precipitation estimates are attractive for a wide range of applications like extreme precipitation monitoring and natural hazard warning. Recently, a gauge-adjusted near-real-time GSMaP precipitation estimate (GSMaP_Gauge_NRT) was produced to improve the quality of the original GSMaP_NRT. In this study, efforts were taken to investigate and validate the performance of the GSMaP_Gauge_NRT using gauge observations over Mainland China. The analyses indicated that GSMaP_NRT generally overestimated the gauge precipitation in China. After calibration, the GSMaP_Gauge_NRT effectively reduced this bias and was more consistent with gauge observations. Results also showed that the correction scheme of GSMaP_Gauge_NRT mainly acted on hit events and could hardly make up the miss events of the satellite precipitation estimates. Finally, we extended the evaluation to the global scale for a broader view of GSMaP_Gauge_NRT. The global comparisons exhibited that the GSMaP_Gauge_NRT was in good agreement with the GSMaP_Gauge product. In conclusion, the GSMaP_Gauge_NRT had better performance than the GSMaP_NRT and was a more reliable near-real-time satellite precipitation product.

Highlights

Reliable precipitation estimates are crucial because of their role in flood monitoring, crop yield, and water resource management [1,2,3]
The remote sensing of precipitation combines the advantage of the frequency sampling of infrared (IR) sensors derived from geostationary (GEO) satellites and the superior accuracy of passive microwave (PMW) sensors carried onboard the low earth orbiting (LEO) satellites, in an effort to produce precipitation data with extensive spatial coverage and fine resolutions [7,8,9]
Various satellite precipitation missions have been implemented and their products have been made available to the public

Summary

Introduction

Reliable precipitation estimates are crucial because of their role in flood monitoring, crop yield, and water resource management [1,2,3]. Previous satellite precipitation missions include the NASA’s Tropical Rainfall Measuring Mission (TRMM [10]), NOAA’s Climate Prediction Center (CPC) morphing technique (CMORPH [11]), JAXA’s Global Satellite Mapping of Precipitation (GSMaP [12]), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN [13]), the Climate Hazard Group InfraRed Precipitation (CHIRP [14]), and the successor of TRMM: Global Precipitation Measurement (GPM [3]) These satellite precipitation missions and products have benefitted the hydrology and meteorology community in relevant researches and applications. If someone want to use PERSIANN-CDR or GSMaP_Gauge data, they must wait ~3 months or ~3 days after observation, respectively [21,30]

Methods

Results

Conclusion