Cross Validation of GOES-16 and NOAA Multi-Radar Multi-Sensor (MRMS) QPE over the Continental United States

Luyao Sun,Haonan Chen,Lei Han,Zhe Li

doi:10.3390/rs13204030

Abstract

The Geostationary Operational Environmental Satellite-R (GOES-R) series provides new opportunities for continuous observation of precipitation at large scales with a high resolution. An operational quantitative precipitation estimation (QPE) product has been produced based on multi-channel measurements from the Advanced Baseline Imager (ABI) aboard the GOES-16 (formerly known as GOES-R). This paper presents a comprehensive evaluation of this GOES-16 QPE product against a ground reference QPE product from the National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor (MRMS) system over the continental United States (CONUS) during the warm seasons of 2018 and 2019. For the first time, the accuracy of GOES-16 QPE product was quantified using the gauge-corrected MRMS (GC-MRMS) QPE product, and a number of evaluation metrics were applied to adequately resolve the associated errors. The results indicated that precipitation occurrence and intensity estimated by the GOES-16 QPE agreed with GC-MRMS fairly well over the eastern United States (e.g., the probability of detection was close to 1.0, and the Pearson’s correlation coefficient was 0.80 during September 2019), while the discrepancies were noticeable over the western United States (e.g., the Pearson’s correlation coefficient was 0.64 for the same month). The performance of GOES-16 QPE was downgraded over the western United States, in part due to the limitations of the GOES-16 rainfall retrieval algorithm over complex terrains, and in part because of the poor radar coverage analyzed by the MRMS system. In addition, it was found that the GOES-16 QPE product significantly overestimated rainfall induced by the mesoscale convective systems in the midwestern United States, which must be addressed in the future development of GOES satellite rainfall retrieval algorithms.

Highlights

Knowledge of precipitation is critical to advancing our understanding of the water cycle and many other coupled cycles of the Earth’s system
Remains unclear due to the limited high-quality ground reference and comprehensive evaluation works. To address this critical problem, this study aims to provide a thorough assessment of the Geostationary Operational Environmental Satellite-16 (GOES-16) quantitative precipitation estimation (QPE) product by leveraging the high-quality ground-based QPE from the Multi-Radar Multi-Sensor (MRMS) system over continental United States (CONUS)
The GOES-16 QPE tended to underestimate warm-season total rainfall when it was below 800 mm, and to overestimate it for rainfall greater than 800 mm

Summary

Introduction

Knowledge of precipitation is critical to advancing our understanding of the water cycle and many other coupled (e.g., carbon, nitrogen) cycles of the Earth’s system. Reliable precipitation measurements and estimates are vital for supporting and verifying climate, weather, and hydrological models, as well as the key components of other Earth system models. A wide range of instruments with varied coverage, resolution, and accuracy have been developed to measure precipitation. Compared with ground-based sensors such as rain gauges and weather radar, satellites have unique advantages in monitoring precipitation at regional to global scales, in data-sparse areas such as the ocean, mountainous regions, and developing countries. A large number of geostationary (GEO) and low earth orbit (LEO) satellites have been launched for monitoring a wide range of hydrometeorological variables, including precipitation [1,2,3]. Different from LEO satellites, GEO platforms can provide very a high sampling frequency with much more detailed information, as they constantly focus on the

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