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SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies
A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global produc...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10618238/ https://www.ncbi.nlm.nih.gov/pubmed/37907558 http://dx.doi.org/10.1038/s41597-023-02654-6 |
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author | Mosaffa, Hamidreza Filippucci, Paolo Massari, Christian Ciabatta, Luca Brocca, Luca |
author_facet | Mosaffa, Hamidreza Filippucci, Paolo Massari, Christian Ciabatta, Luca Brocca, Luca |
author_sort | Mosaffa, Hamidreza |
collection | PubMed |
description | A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global product that estimates rainfall from satellite soil moisture observations. Previous studies have demonstrated the SM2RAIN products’ high potential in estimating rainfall around the world. This manuscript describes the SM2RAIN-Climate rainfall product, which uses the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture v06.1 to provide monthly global rainfall for the 24-year period 1998–2021 at 1-degree spatial resolution. The assessment of the proposed rainfall dataset against different existing state-of-the-art rainfall products exhibits the robust performance of SM2RAIN-Climate in most regions of the world. This performance is indicated by correlation coefficients between SM2RAIN-Climate and state-of-the-art products, consistently exceeding 0.8. Moreover, evaluation results indicate the potential of SM2RAIN-Climate as an independent rainfall product from other satellite rainfall products in capturing the pattern of global rainfall trend. |
format | Online Article Text |
id | pubmed-10618238 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106182382023-11-02 SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies Mosaffa, Hamidreza Filippucci, Paolo Massari, Christian Ciabatta, Luca Brocca, Luca Sci Data Data Descriptor A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global product that estimates rainfall from satellite soil moisture observations. Previous studies have demonstrated the SM2RAIN products’ high potential in estimating rainfall around the world. This manuscript describes the SM2RAIN-Climate rainfall product, which uses the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture v06.1 to provide monthly global rainfall for the 24-year period 1998–2021 at 1-degree spatial resolution. The assessment of the proposed rainfall dataset against different existing state-of-the-art rainfall products exhibits the robust performance of SM2RAIN-Climate in most regions of the world. This performance is indicated by correlation coefficients between SM2RAIN-Climate and state-of-the-art products, consistently exceeding 0.8. Moreover, evaluation results indicate the potential of SM2RAIN-Climate as an independent rainfall product from other satellite rainfall products in capturing the pattern of global rainfall trend. Nature Publishing Group UK 2023-10-31 /pmc/articles/PMC10618238/ /pubmed/37907558 http://dx.doi.org/10.1038/s41597-023-02654-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Mosaffa, Hamidreza Filippucci, Paolo Massari, Christian Ciabatta, Luca Brocca, Luca SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies |
title | SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies |
title_full | SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies |
title_fullStr | SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies |
title_full_unstemmed | SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies |
title_short | SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies |
title_sort | sm2rain-climate, a monthly global long-term rainfall dataset for climatological studies |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10618238/ https://www.ncbi.nlm.nih.gov/pubmed/37907558 http://dx.doi.org/10.1038/s41597-023-02654-6 |
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