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Sci Rep. 2017 Jul 14;7(1):5449. doi: 10.1038/s41598-017-05822-y.

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

Author information

1
Department of Earth Sciences, Uppsala University, Villavägen 16, 75236, Uppsala, Sweden. korbinian.breinl@geo.uu.se.
2
Department of Earth Sciences, Uppsala University, Villavägen 16, 75236, Uppsala, Sweden.
3
Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland.
4
Institute for Environment and Human Security, United Nations University (UNU-EHS), UN Campus, Platz der Vereinten Nationen 1, 53113, Bonn, Germany.
5
Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Ulls väg 16, 75007, Uppsala, Sweden.

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.

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