Using High-Density Rain Gauges to Validate the Accuracy of Satellite Precipitation Products over Complex Terrains

Linfei Yu,Yonghui Yang,Yongqiang Zhang

doi:10.3390/atmos11060633

Abstract

Topography and precipitation intensity are important factors that affect the quality of satellite precipitation products (SPPs). A clear understanding of the accuracy performance of SPPs over complex terrains and its relationship with topography is valuable for further improvement of product algorithms. The objective of this study is to evaluate three SPPs—the Climate Prediction Center morphing method bias corrected product (CMORPH CRT), Global Precipitation Measurement Integrated MultisatellitE Retrievals (IMERG), and Tropical Rainfall Measuring Mission 3B42V7 (TRMM 3B42V7) against a high-density network of 104 rain gauges over the Taihang Mountains from 1 January 2016 to 31 December 2017, with special focus on the reliability of products’ performance at different elevation and precipitation intensity. The results show that three SPPs slightly overestimate daily precipitation, compared to rain gauge observations, with bias ratios (β) from 1.02 to 1.06 over the entire regions. In terms of accuracy, 3B42 slightly outperforms CRT and IMERG over the Taihang Mountains. As for different elevation ranges, three SPPs show better performance in terms of accuracy in low and moderate elevation (0–500 m) regions. Similar performances of precipitation detection capability can be found for three products over the whole areas, with detection scores ranging from 0.53 to 0.58. Better precipitation detecting performance of three SPPs was discovered in high-elevation (>1000 m) regions. We adopted a linear regression (LR) model and Locally Weighted Regression (LWR) model in an attempt to discover the linear/non-linear relationships between SPPs’ performances and topographic variations. In the accuracy statistical metrics, the errors of 3B42 and CRT showed significantly positive correlations (p < 0.01) with elevation variations. The critical success index for three products gradually increased with elevation variation based on the LR model. The correlation coefficient and probability of detection for three products showed significant non-linear trends in the LWR model. The probability distribution function for the three products in different elevation regions is similar to that over the entire regions. Three SPPs slightly overestimated the frequency of heavy rain events (6.9 < precipitation intensity (PI) ≤ 19.6 mm/d); CRT and 3B42 tended to underestimate the frequency of no rain events (PI < 0.1 mm/d), while IMERG generally overestimated the frequency of no rain events. Our results not only give a detailed assessment of mainly current SPPs over the Taihang Mountains, but also recommend that further improvement on retrieval algorithm is needed by considering topographical impacts for SPPs in the future.

Highlights

Precipitation is an important variable in global water and energy circulation systems [1,2], and accurate precipitation measurements have direct benefits for water resources related applications such as disaster prevention, agricultural water usage, water resource management, and weather prediction [3,4,5]
Three Satellite precipitation products (SPPs) slightly overestimated the frequency of heavy rain events (6.9 < precipitation intensity (PI) ≤ 19.6 mm/d); CRT and 3B42 tended to underestimate the frequency of no rain events (PI < 0.1 mm/d), while IMERG generally overestimated the frequency of no rain events
The performance of the three SPPs can be evaluated in terms of a ground-based dataset in different elevation categories

Summary

Introduction

Precipitation is an important variable in global water and energy circulation systems [1,2], and accurate precipitation measurements have direct benefits for water resources related applications such as disaster prevention, agricultural water usage, water resource management, and weather prediction [3,4,5]. Methods of measuring precipitation traditionally included ground-based rain gauges, weather radars, and remote sensing [6,7]. By far the most accurate ground-based measurement via its “point-scale” observations are often limited due to an insufficient number of gauges and sparse network in some remote regions such as less accessible mountainous and oceanic regions [8,9]. One alternative source for overcoming such a limitation in ground-based measurement using point scale is space-based assessment and observation of precipitation [10,11]. Continued improvement in temporal and spatial resolutions of satellite-based products has been achieved through recent updates in sensor and technology methods via the merging of various data sources such as radar data, thermal infrared data, active/passive microwave data, and information from the Global Telecommunication System [15,16]

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