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


Breinl, Korbinian; Di Baldassarre, Giuliano; Girons Lopez, Marc; Hagenlocher, Michael; Vico, Giulia; Rutgersson, Anna (2017). Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity? Scientific Reports, 7(1):online.

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 di cult. To overcome these limitations, we investigated the performance of the reduced-complexity multi-site precipitation generator TripleM across three di erent 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 de cits are more marked at larger spatial scales due to the increasing heterogeneity of climatic conditions.

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 di cult. To overcome these limitations, we investigated the performance of the reduced-complexity multi-site precipitation generator TripleM across three di erent 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 de cits are more marked at larger spatial scales due to the increasing heterogeneity of climatic conditions.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2017
Deposited On:22 Sep 2017 11:42
Last Modified:09 Dec 2017 02:21
Publisher:Nature Publishing Group
ISSN:2045-2322
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41598-017-05822-y

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