Systematical Evaluation of Satellite Precipitation Estimates Over Central Asia Using an Improved Error‐Component Procedure

Abstract

Satellite precipitation estimates (SPEs) provide important alternative precipitation sources for various applications especially for regions where in situ observations are limited or unavailable, like central Asia. In this study, eight SPEs based on four different algorithms, namely, the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks are evaluated by using an improved evaluation system over central Asia with respect to their performance in capturing precipitation occurrence and magnitude. Both satellite‐only and gauge‐corrected versions are assessed against gauge‐gridded reference from June 2001 to May 2006. Main results show that all SPEs have difficulties in accurately estimating mountainous precipitation with great overestimation/underestimation in both winter and summer. In winter, CMORPH products fail to capture events over ice‐/snow‐covered region. In summer, large overestimations dominated by positive hit bias and missed precipitation are found for all products in northern central Asia. Interestingly, 3B42 and CMORPH products show great false alarm percentages (up to 90%) over lake region, which is more significant in summer than in winter. Significant elevation‐dependent errors exist in all products, especially for the high‐altitude regions (>3,000 m) with missed error and hit error being the two leading errors. Satellite‐only products have large systematic and random errors, while the gauge‐corrected products demonstrate significant improvements in reducing random errors. Generally, the gauge‐corrected GSMaP performs better than others with good skills in reducing various errors.

Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region

“…In terms of statistical metrics for SPPs (Table 2), the post-real-time IMERG-F and GSMaP-Gauge generally provided the best performance at different temporal scales among all the evaluated IMERG and GSMaP SPPs, respectively (Tables 3 and 5). Post-real-time SPPs showed better performance than their near-time-time versions in several regions [36,[50][51][52][53][54][55]. However, the offset effect of positive and negative precipitation errors might lead to distorted statistical metrics.…”

Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

“…Many previous studies [24,25,50,[72][73][74] found that post-real-time SPPs generally present higher quality than their near-real-time versions in many regions of the world because of gauge-based adjustment. In this study, IMERG-F evidently outperforms the near-real-time SPPs (IMERG-E and IMERG-L) in the Chindwin River basin.…”

Comparative Analysis of TMPA and IMERG Precipitation Datasets in the Arid Environment of El-Qaa Plain, Sinai

“…The PR complemented the results of the TMI and passive microwave sensors to provide measurements of radiance through precipitating clouds along the sensor view path. Radiance frequency reflects the properties of clouds and precipitation particles [57]. The active microwave sensors provided information about cloud height by measuring a backscatter delay [57].…”