{"title":"Evaluation of Seasonal Differences Among Three NOAA Climate Data Records of Precipitation","authors":"O. Prat, B. Nelson","doi":"10.1175/jhm-d-22-0108.1","DOIUrl":null,"url":null,"abstract":"\nThree 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 2007-2018 over the conterminous United States. Data from the in-situ US 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 King-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.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"1 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrometeorology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/jhm-d-22-0108.1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
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 2007-2018 over the conterminous United States. Data from the in-situ US 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 King-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.
期刊介绍:
The Journal of Hydrometeorology (JHM) (ISSN: 1525-755X; eISSN: 1525-7541) publishes research on modeling, observing, and forecasting processes related to fluxes and storage of water and energy, including interactions with the boundary layer and lower atmosphere, and processes related to precipitation, radiation, and other meteorological inputs.