{"title":"伊朗上空月度、季节和年度时间尺度卫星降水产品评估","authors":"Nazanin Nozarpour, Emad Mahjoobi, Saeed Golian","doi":"10.1007/s41742-024-00619-0","DOIUrl":null,"url":null,"abstract":"<p>Understanding the spatial and temporal distribution of precipitation globally is advantageous for advancing climate knowledge and improving weather and climate forecasting models. Despite the complexity of determining precipitation distribution, numerous satellite-based precipitation products (SPPs) have been developed in recent decades to estimate precipitation with sufficient coverage and accuracy. This study evaluates the performance of four SPPs, namely Integrated Multi-satellite Retrievals for GPM (IMERG-FRV6), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Tropical Rainfall Measuring Mission (TRMM-3B43V7), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record (PERSIANN-CDR) on monthly, seasonal, and annual scales in Iran, and aimed to enhance the accuracy of the evaluation by extending the statistical period and selecting evaluation indicators based on error, efficiency, and correlation. Measured rainfall data from 81 synoptic stations across Iran from 2008 to 2019 were used for this evaluation. To accurately assess the selected SPPs, several statistical indices including Correlation Coefficient (CC), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), and Bias were calculated and analyzed at all synoptic stations. The results demonstrate that MSWEP has a significant advantage over other products at all time scales. The performance of all four products in areas with high monthly rainfall is associated with more errors. PERSIANN-CDR exhibited the highest monthly RMSE, while TRMM-3B43V7 performed better in drier regions with low to moderate precipitation. MSWEP showed the closest average precipitation to observational data in spring, summer, and winter, while IMERG-FRV6 overestimated precipitation in all seasons.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\n","PeriodicalId":14121,"journal":{"name":"International Journal of Environmental Research","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of Satellite-based Precipitation Products in Monthly, Seasonal, and Annual Time-Scale over Iran\",\"authors\":\"Nazanin Nozarpour, Emad Mahjoobi, Saeed Golian\",\"doi\":\"10.1007/s41742-024-00619-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Understanding the spatial and temporal distribution of precipitation globally is advantageous for advancing climate knowledge and improving weather and climate forecasting models. Despite the complexity of determining precipitation distribution, numerous satellite-based precipitation products (SPPs) have been developed in recent decades to estimate precipitation with sufficient coverage and accuracy. This study evaluates the performance of four SPPs, namely Integrated Multi-satellite Retrievals for GPM (IMERG-FRV6), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Tropical Rainfall Measuring Mission (TRMM-3B43V7), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record (PERSIANN-CDR) on monthly, seasonal, and annual scales in Iran, and aimed to enhance the accuracy of the evaluation by extending the statistical period and selecting evaluation indicators based on error, efficiency, and correlation. Measured rainfall data from 81 synoptic stations across Iran from 2008 to 2019 were used for this evaluation. To accurately assess the selected SPPs, several statistical indices including Correlation Coefficient (CC), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), and Bias were calculated and analyzed at all synoptic stations. The results demonstrate that MSWEP has a significant advantage over other products at all time scales. The performance of all four products in areas with high monthly rainfall is associated with more errors. PERSIANN-CDR exhibited the highest monthly RMSE, while TRMM-3B43V7 performed better in drier regions with low to moderate precipitation. MSWEP showed the closest average precipitation to observational data in spring, summer, and winter, while IMERG-FRV6 overestimated precipitation in all seasons.</p><h3 data-test=\\\"abstract-sub-heading\\\">Graphical Abstract</h3>\\n\",\"PeriodicalId\":14121,\"journal\":{\"name\":\"International Journal of Environmental Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Environmental Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1007/s41742-024-00619-0\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Environmental Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s41742-024-00619-0","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Assessment of Satellite-based Precipitation Products in Monthly, Seasonal, and Annual Time-Scale over Iran
Understanding the spatial and temporal distribution of precipitation globally is advantageous for advancing climate knowledge and improving weather and climate forecasting models. Despite the complexity of determining precipitation distribution, numerous satellite-based precipitation products (SPPs) have been developed in recent decades to estimate precipitation with sufficient coverage and accuracy. This study evaluates the performance of four SPPs, namely Integrated Multi-satellite Retrievals for GPM (IMERG-FRV6), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Tropical Rainfall Measuring Mission (TRMM-3B43V7), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record (PERSIANN-CDR) on monthly, seasonal, and annual scales in Iran, and aimed to enhance the accuracy of the evaluation by extending the statistical period and selecting evaluation indicators based on error, efficiency, and correlation. Measured rainfall data from 81 synoptic stations across Iran from 2008 to 2019 were used for this evaluation. To accurately assess the selected SPPs, several statistical indices including Correlation Coefficient (CC), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), and Bias were calculated and analyzed at all synoptic stations. The results demonstrate that MSWEP has a significant advantage over other products at all time scales. The performance of all four products in areas with high monthly rainfall is associated with more errors. PERSIANN-CDR exhibited the highest monthly RMSE, while TRMM-3B43V7 performed better in drier regions with low to moderate precipitation. MSWEP showed the closest average precipitation to observational data in spring, summer, and winter, while IMERG-FRV6 overestimated precipitation in all seasons.
期刊介绍:
International Journal of Environmental Research is a multidisciplinary journal concerned with all aspects of environment. In pursuit of these, environmentalist disciplines are invited to contribute their knowledge and experience. International Journal of Environmental Research publishes original research papers, research notes and reviews across the broad field of environment. These include but are not limited to environmental science, environmental engineering, environmental management and planning and environmental design, urban and regional landscape design and natural disaster management. Thus high quality research papers or reviews dealing with any aspect of environment are welcomed. Papers may be theoretical, interpretative or experimental.