Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia

Hao Guo,Sheng Chen,Xianwu Xue,Jujun Hu,Xinhua Zhang,Abebe Gebregiorgis,Anming Bao

doi:10.3390/rs70607181

Abstract

This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%).

Highlights

As a key exchange process within the hydrological cycle, precipitation represents the net heating from condensation in the atmosphere [1]
The primary objective of this paper is to identify the strengths and weaknesses of the currently most popular satellite-based high-resolution precipitation products (i.e., Tropical Rainfall Measuring Mission (TRMM), Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and PERSIANN) over Central Asia
United States, and the results showed that GSMaP gives comparable performance to other satellite-based precipitation products (i.e., CMORPH, PERSIANN, NRL, TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42) with slightly better probability of detection during summer

Summary

Introduction

As a key exchange process within the hydrological cycle, precipitation represents the net heating from condensation in the atmosphere [1]. Conventional rain gauge network can provide relatively accurate measurement of precipitation amount with high temporal frequency at specific location, but inhomogeneous distribution and small sampling area of rain gauges limit its use for applications at regional and global scale [6,7,8]. Ground-based weather radar networks provide continuous coverage with high spatial and temporal resolution at the regional scale. Despite the advantages of radar for rainfall retrievals, the use of such techniques does have limitations, e.g., over mountain regions, which includes beam blockage, ground clutter, cold weather and their interaction with vertical structure [1,9,10,11,12,13,14,15,16]. The high costs of radar usage limit its application especially for the developing countries

Objectives

Results

Conclusion