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Space‐time model for daily rainfall using atmospheric circulation patterns
Author(s) -
Bardossy Andras,
Plate Erich J.
Publication year - 1992
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/91wr02589
Subject(s) - atmospheric circulation , precipitation , environmental science , autoregressive model , climatology , covariance , meteorology , conditional probability distribution , stochastic modelling , atmospheric sciences , mathematics , statistics , geography , geology
A multidimensional stochastic model is developed for the space‐time distribution of daily precipitation. The rainfall is linked to the atmospheric circulation patterns using conditional distributions and conditional spatial covariance functions. The model is a transformed conditional multivariate autoregressive AR(I) model, with parameters depending on the atmospheric circulation pattern. The model reproduces both the local rainfall occurrence probabilities and the distribution of the rainfall amounts at given locations, and the spatial dependence described with the help of cross‐covariances of the transformed series. Parameter estimation methods based on the moments of the observed data are developed. A simulation procedure for the model is also presented. Its link to atmospheric circulation patterns makes it suitable for local precipitation simulation under stationary and nonstationary arrivals of atmospheric circulation patterns such as climate change. The model is applied using the classification scheme of the German Weather Service which is available for the time period 1881–1990. Precipitation data measured at 44 different stations for the time period 1977–1990 in the catchment of the river Ruhr (Germany) are used to demonstrate the model.