Assessment of Rainfall Frequencies from Global Precipitation Datasets
Rainfall is of vital importance to terrestrial ecosystems and its intermittent characteristics have a profound impact on plant growth, soil biogeochemical cycles, and water resource management. Rainfall frequency, one of the key statistics of rainfall intermittency, has received relatively little re...
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MDPI AG
2025-01-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/1/66 |
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| author | Xueyi Yin Ziyang Zhang Zhi Lin Jun Yin |
| author_facet | Xueyi Yin Ziyang Zhang Zhi Lin Jun Yin |
| author_sort | Xueyi Yin |
| collection | DOAJ |
| description | Rainfall is of vital importance to terrestrial ecosystems and its intermittent characteristics have a profound impact on plant growth, soil biogeochemical cycles, and water resource management. Rainfall frequency, one of the key statistics of rainfall intermittency, has received relatively little research attention. Leveraging scale-dependent relationships in rainfall frequencies and using various global precipitation datasets, we found most grid-scale rainfall frequencies are relatively large and do not converge to the field-scale frequencies as grid size decreases. Specifically, these differences are as high as 41.8% for the Global Precipitation Climatology Project (GPCP) and 74.8% for the fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), which are much larger than the differences in mean rainfall rates but can be partially corrected by redefining wet days with higher rainfall thresholds. These differences across most regions of the world should be interpreted as the inherent biases associated with the model structure or algorithms used for deriving precipitation data and cannot be reduced simply by increasing the data resolutions. Such biases could propagate into the hydrological process and influence the calibration of the rainfall-runoff process, one of the key nonlinear relationships in land surface modeling. We, therefore, call for urgent research into this topic to avoid misunderstandings of rainfall intermittency and ensure its proper application in various fields. |
| format | Article |
| id | doaj-art-6b2948fddbf640438f6c0c58809cff50 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-6b2948fddbf640438f6c0c58809cff502025-01-24T13:21:54ZengMDPI AGAtmosphere2073-44332025-01-011616610.3390/atmos16010066Assessment of Rainfall Frequencies from Global Precipitation DatasetsXueyi Yin0Ziyang Zhang1Zhi Lin2Jun Yin3Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaRainfall is of vital importance to terrestrial ecosystems and its intermittent characteristics have a profound impact on plant growth, soil biogeochemical cycles, and water resource management. Rainfall frequency, one of the key statistics of rainfall intermittency, has received relatively little research attention. Leveraging scale-dependent relationships in rainfall frequencies and using various global precipitation datasets, we found most grid-scale rainfall frequencies are relatively large and do not converge to the field-scale frequencies as grid size decreases. Specifically, these differences are as high as 41.8% for the Global Precipitation Climatology Project (GPCP) and 74.8% for the fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), which are much larger than the differences in mean rainfall rates but can be partially corrected by redefining wet days with higher rainfall thresholds. These differences across most regions of the world should be interpreted as the inherent biases associated with the model structure or algorithms used for deriving precipitation data and cannot be reduced simply by increasing the data resolutions. Such biases could propagate into the hydrological process and influence the calibration of the rainfall-runoff process, one of the key nonlinear relationships in land surface modeling. We, therefore, call for urgent research into this topic to avoid misunderstandings of rainfall intermittency and ensure its proper application in various fields.https://www.mdpi.com/2073-4433/16/1/66rainfall frequencyrainfall intermittencygrid scalefield scaleprecipitation |
| spellingShingle | Xueyi Yin Ziyang Zhang Zhi Lin Jun Yin Assessment of Rainfall Frequencies from Global Precipitation Datasets Atmosphere rainfall frequency rainfall intermittency grid scale field scale precipitation |
| title | Assessment of Rainfall Frequencies from Global Precipitation Datasets |
| title_full | Assessment of Rainfall Frequencies from Global Precipitation Datasets |
| title_fullStr | Assessment of Rainfall Frequencies from Global Precipitation Datasets |
| title_full_unstemmed | Assessment of Rainfall Frequencies from Global Precipitation Datasets |
| title_short | Assessment of Rainfall Frequencies from Global Precipitation Datasets |
| title_sort | assessment of rainfall frequencies from global precipitation datasets |
| topic | rainfall frequency rainfall intermittency grid scale field scale precipitation |
| url | https://www.mdpi.com/2073-4433/16/1/66 |
| work_keys_str_mv | AT xueyiyin assessmentofrainfallfrequenciesfromglobalprecipitationdatasets AT ziyangzhang assessmentofrainfallfrequenciesfromglobalprecipitationdatasets AT zhilin assessmentofrainfallfrequenciesfromglobalprecipitationdatasets AT junyin assessmentofrainfallfrequenciesfromglobalprecipitationdatasets |