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A multi-state weather generator for daily precipitation for the Torne River basin, northern Sweden/western Finland

Rayner, D.P. 1973 (author)
Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences
Achberger, Christine, 1968 (author)
Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences
Chen, Deliang, 1961 (author)
Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences
 (creator_code:org_t)
Elsevier BV, 2016
2016
English.
In: Advances in Climate Change Research. - : Elsevier BV. - 1674-9278. ; 7:1-2, s. 70-81
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • This paper describes a new weather generator e the 10-state empirical model e that combines a 10-state, first-order Markov chain with a non-parametric precipitation amounts model. Using a doubly-stochastic transition-matrix results in a weather generator for which the overall precipitation distribution (including both wet and dry days) and the temporal-correlation can be modified independently for climate change studies. This paper assesses the ability of the 10-state empirical model to simulate daily area-average precipitation in the Torne River catchment in northern Sweden/western Finland in the context of 3 other models: a 10-state model with a parametric (Gamma) amounts model; a wet/dry chain with the empirical amounts model; and a wet/dry chain with the parametric amounts model. The ability to accurately simulate the dis- tribution of multi-day precipitation in the catchment is the primary consideration. Results showed that the 10-state empirical model represented accumulated 2- to 14-day precipitation most realistically. Further, the dis- tribution of precipitation on wet days in the catchment is related to the placement of a wet day within a wet-spell, and the 10-state models represented this realistically, while the wet/dry models did not. Although all four models accurately reproduced the annual and monthly averages in the training data, all models underestimated inter-annual and inter-seasonal variance. Even so, the 10-state empirical model performed best. We conclude that the multi-state model is a promising candidate for hydrological applications, as it simulates multi-day precipitation well, but that further development is required to improve the simulation of interannual variation.

Subject headings

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Meteorologi och atmosfärforskning (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Meteorology and Atmospheric Sciences (hsv//eng)

Keyword

Weather generator; Multi-state; Torne River; Precipitation

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University of Gothenburg

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