Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Ziqiang Ma,Yu He,Xiao Tan,Yang Hong,Weizhen Fang,Jintao Xu,Kang He

doi:10.3390/rs10121883

Abstract

Accurate precipitation data is crucial in many applications such as hydrology, meteorology, and ecology. Compared with ground observations, satellite-based precipitation estimates can provide much more spatial information to characterize precipitation. In this study, the satellite-based precipitation products of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were firstly evaluated over the Tibetan Plateau (TP) in 2015 against ground observations at both annual and monthly scales. Secondly, random forest algorithm was used to obtain the annual downscaled results (~1 km) based on IMERG and TMPA data and the downscaled results were examined against rain gauge data. Thirdly, a disaggregation algorithm was used to obtain the monthly downscaled results based on those at annual scale. The results indicated that (1) IMERG performed better than TMPA at both annual and monthly scales; (2) IMERG had few anomalies while TMPA displayed significant numbers of outliers in central and western parts of the TP; (3) random forest was a promising algorithm in acquiring high resolution precipitation data with improved accuracy; (4) the downscaled results based on IMERG had better performances than those based on TMPA.

Highlights

Precipitation plays a significant role in global water cycles and energy exchanges [1]
The annual downscaled results at 1 km resolution using random forest models based on TRMM Multisatellite Precipitation Analysis (TMPA) and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) data were presented in Figure 9b,c, respectively
Both the downscaled results based on TMPA and IMERG captured the spatial distribution of annual precipitation compared to the original TMRePmAot,e Soernisg. i2n01a8l, 1I0M, xEFROGR PaEnEdR RgEaVuIEgWe precipitation, showing a decreasing trend from south t1o2 onfo2r1th, an3d.3f.roDmowwnsecsatletod Reaessut.ltIst awnadsVnaolitdaabtiloentshUatstinhge GdorowunndscOalbesderrveastuiolntss based TMPA data shown contiguous precipitation map with anomalies removed which appeared in the original TMPA data, in western and noTrthheerannnpuaartlsdoofwthnescTaPle.dThreesudlotws ants1caklmedrreessoululttsiobnasuesdinognraIMndEoRmGfdoraetsatsmtilolddeilsspblaayseedd ocnonTtMinPuAous vaarniadtioInMaEnRdGgrdaadtuaalwtererendpwreistehnotuetdouintliFerigs.ure 9b,c, respectively

Summary

Introduction

Precipitation plays a significant role in global water cycles and energy exchanges [1]. Accurate precipitation information is highly desirable in various scientific and application fields such as water resource management, weather prediction, as well as disaster monitoring and control [2]. There are three kinds of independent instruments to obtaining precipitation data, including gauges, weather radar and satellite-based sensors [3]. Though conventional point-based measurements from rain gauges could provide relatively accurate rainfall values at the point scale, they are not suitable for providing continuous spatial precipitation distributions [4]. Weather radar can obtain rainfall data with finer spatiotemporal resolutions. While satellite-based remote sensing has great potentials to provide comprehensive estimates of precipitation globally with reasonable spatiotemporal resolutions and accuracy

Objectives

Methods

Results

Conclusion