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

Artikel i vetenskaplig tidskrift
Författare D.P. Rayner
Christine Achberger
Deliang Chen
Publicerad i Advances in Climate Change Research
Volym 7
Nummer/häfte 1-2
Sidor 70-81
ISSN 1674-9278
Publiceringsår 2016
Publicerad vid Institutionen för geovetenskaper
Sidor 70-81
Språk en
Länkar dx.doi.org/10.1016/j.accre.2016.06....
Ämnesord Weather generator; Multi-state; Torne River; Precipitation
Ämneskategorier Meteorologi och atmosfärforskning

Sammanfattning

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.

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