Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?

Abstract Stochastic weather generators can generate very long time series of weather patterns, which are indispensable in earth sciences, ecology and climate research. Yet, both their potential and limitations remain largely unclear because past research has typically focused on eclectic case studie...

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Autores principales:	Korbinian Breinl, Giuliano Di Baldassarre, Marc Girons Lopez, Michael Hagenlocher, Giulia Vico, Anna Rutgersson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e9f40a31917c41da951d4713a2ae69cc
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spelling	oai:doaj.org-article:e9f40a31917c41da951d4713a2ae69cc2021-12-02T15:05:38ZCan weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?10.1038/s41598-017-05822-y2045-2322https://doaj.org/article/e9f40a31917c41da951d4713a2ae69cc2017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05822-yhttps://doaj.org/toc/2045-2322Abstract Stochastic weather generators can generate very long time series of weather patterns, which are indispensable in earth sciences, ecology and climate research. Yet, both their potential and limitations remain largely unclear because past research has typically focused on eclectic case studies at small spatial scales in temperate climates. In addition, stochastic multi-site algorithms are usually not publicly available, making the reproducibility of results difficult. To overcome these limitations, we investigated the performance of the reduced-complexity multi-site precipitation generator TripleM across three different climatic regions in the United States. By resampling observations, we investigated for the first time the performance of a multi-site precipitation generator as a function of the extent of the gauge network and the network density. The definition of the role of the network density provides new insights into the applicability in data-poor contexts. The performance was assessed using nine different statistical metrics with main focus on the inter-annual variability of precipitation and the lengths of dry and wet spells. Among our study regions, our results indicate a more accurate performance in wet temperate climates compared to drier climates. Performance deficits are more marked at larger spatial scales due to the increasing heterogeneity of climatic conditions.Korbinian BreinlGiuliano Di BaldassarreMarc Girons LopezMichael HagenlocherGiulia VicoAnna RutgerssonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Korbinian Breinl Giuliano Di Baldassarre Marc Girons Lopez Michael Hagenlocher Giulia Vico Anna Rutgersson Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?
description	Abstract Stochastic weather generators can generate very long time series of weather patterns, which are indispensable in earth sciences, ecology and climate research. Yet, both their potential and limitations remain largely unclear because past research has typically focused on eclectic case studies at small spatial scales in temperate climates. In addition, stochastic multi-site algorithms are usually not publicly available, making the reproducibility of results difficult. To overcome these limitations, we investigated the performance of the reduced-complexity multi-site precipitation generator TripleM across three different climatic regions in the United States. By resampling observations, we investigated for the first time the performance of a multi-site precipitation generator as a function of the extent of the gauge network and the network density. The definition of the role of the network density provides new insights into the applicability in data-poor contexts. The performance was assessed using nine different statistical metrics with main focus on the inter-annual variability of precipitation and the lengths of dry and wet spells. Among our study regions, our results indicate a more accurate performance in wet temperate climates compared to drier climates. Performance deficits are more marked at larger spatial scales due to the increasing heterogeneity of climatic conditions.
format	article
author	Korbinian Breinl Giuliano Di Baldassarre Marc Girons Lopez Michael Hagenlocher Giulia Vico Anna Rutgersson
author_facet	Korbinian Breinl Giuliano Di Baldassarre Marc Girons Lopez Michael Hagenlocher Giulia Vico Anna Rutgersson
author_sort	Korbinian Breinl
title	Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?
title_short	Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?
title_full	Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?
title_fullStr	Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?
title_full_unstemmed	Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?
title_sort	can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e9f40a31917c41da951d4713a2ae69cc
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Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?

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