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

• Published in: Advances in Climate Change Research
• Main Authors: David Rayner, Christine Achberger, Deliang Chen
• Format: Article in Journal/Newspaper
• Language: English
• Published: KeAi Communications Co., Ltd. 2016
• Subjects: Weather generator; Multi-state; Torne River; Precipitation; Meteorology. Climatology; QC851-999; Social sciences (General); H1-99; Northern Sweden
• Online Access: https://doi.org/10.1016/j.accre.2016.06.006; https://doaj.org/article/0074a852e4c04f459b8f26ebfd1cd62e

This paper describes a new weather generator – the 10-state empirical model – 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 distribution 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 distribution 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.