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A semiparametric multivariate and multisite weather generator
Author(s) -
Apipattanavis Somkiat,
Podestá Guillermo,
Rajagopalan Balaji,
Katz Richard W.
Publication year - 2007
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 - 10.1029/2006wr005714
Subject(s) - multivariate statistics , precipitation , spell , markov chain , environmental science , meteorology , statistics , climatology , watershed , econometrics , mathematics , computer science , geography , machine learning , geology , sociology , anthropology
We propose a semiparametric multivariate weather generator with greater ability to reproduce the historical statistics, especially the wet and dry spells. The proposed approach has two steps: (1) a Markov Chain for generating the precipitation state (i.e., no rain, rain, or heavy rain), and (2) a k ‐nearest neighbor ( k ‐NN) bootstrap resampler for generating the multivariate weather variables. The Markov Chain captures the spell statistics while the k ‐NN bootstrap captures the distributional and lag‐dependence statistics of the weather variables. Traditional k ‐NN generators tend to under‐simulate the wet and dry spells that are keys to watershed and agricultural modeling for water planning and management; hence the motivation for this research. We demonstrate the utility of the proposed approach and its improvement over the traditional k ‐NN approach through an application to daily weather data from Pergamino in the Pampas region of Argentina. We show the applicability of the proposed framework in simulating weather scenarios conditional on the seasonal climate forecast and also at multiple sites in the Pampas region.