Evaluation of Seasonal Differences among Three NOAA Climate Data Records of Precipitation

Author:

Prat Olivier P.1,Nelson Brian R.2

Affiliation:

1. a Cooperative Institute for Satellite Earth System Studies, North Carolina State University, Asheville, North Carolina

2. b Center for Weather and Climate, NOAA/NESDIS/NCEI, Asheville, North Carolina

Abstract

Abstract Three satellite precipitation datasets—CMORPH, PERSIANN-CDR, and GPCP—from the NOAA/Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007–18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products’ (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling–Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance—smaller bias, higher correlation, and a better KGE score—than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH’s performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 0°C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products.

Funder

NOAA

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference50 articles.

1. Adler, R. F., M. Sapiano, and J.-J. Wang, 2017a: Precipitation – Global Precipitation Climatology Project (GPCP) monthly (01B-34). Climate Algorithm Theoretical Basis Doc. (C-ATBD), revision 2, 33 pp., https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/Precipitation_GPCP-Monthly/AlgorithmDescription_01B-34.pdf.

2. Adler, R. F., M. Sapiano, and J.-J. Wang, 2017b: Global Precipitation Climatology Project (GPCP) daily analysis precipitation – GPCP daily CDR. Climate Algorithm Theoretical Basis Doc. (C-ATBD), 21 pp., https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/Precipitation_GPCP-Daily/AlgorithmDescription_01B-35.pdf.

3. The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation;Adler, R. F.,2018

4. Evaluation of satellite-retrieved extreme precipitation rates across the central United States;AghaKouchak, A.,2011

5. Systematic and random error components in satellite precipitation datasets;AghaKouchak, A.,2012

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