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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...

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Autores principales: Mosaffa, Hamidreza, Filippucci, Paolo, Massari, Christian, Ciabatta, Luca, Brocca, Luca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
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.
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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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