Data-driven analysis of annual rain distributions
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929353066020864 |
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| author | Ashkenazy, Yosef Smith, Naftali R. |
| author_facet | Ashkenazy, Yosef Smith, Naftali R. |
| contents | Rainfall is an important component of the climate system and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03861 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-driven analysis of annual rain distributions Ashkenazy, Yosef Smith, Naftali R. Atmospheric and Oceanic Physics Statistical Mechanics Data Analysis, Statistics and Probability Rainfall is an important component of the climate system and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well. |
| title | Data-driven analysis of annual rain distributions |
| topic | Atmospheric and Oceanic Physics Statistical Mechanics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2312.03861 |