Details:

Journal Title:
Remote Sensing
Personal Author:
Sun, Luyao ; Chen, Haonan ; Li, Zhe ; Han, Lei
Sun, Luyao ; Chen, Haonan ; Li, Zhe ; Han, Lei Less -
NOAA Program & Office:
CIRA (Cooperative Institute for Research in the Atmosphere)
Description:
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.
Keywords:
[+]
General Earth and Planetary Sciences
Source:
Remote Sensing, 13(20), 4030
DOI:
https://doi.org/10.3390/rs13204030
ISSN:
2072-4292
Format:
PDF
Publisher:
MDPI AG
Document Type:
Journal Article
Funding:
Grant no. NA19OAR4320073
License:
https://creativecommons.org/licenses/by/4.0/
Rights Information:
CC BY
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:2e10c4809163aa63a0686362959a504c1a153cdbce2cb1e88791a30d74a17285
Download URL:
https://repository.library.noaa.gov/view/noaa/54798/noaa_54798_DS1.pdf
File Type:

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