A Framework for Spatiotemporal Stochastic Weather Simulation
William Kleiber
Applied Mathematics,Ìý
Date and time:Â
Friday, November 16, 2012 - 3:30pm
´¡²ú²õ³Ù°ù²¹³¦³Ù:Ìý
Gridded daily weather simulations are key features of downscaling,hydrological and agricultural models, as well as climate impact studies. Typically, spatially consistent weather simulations are required across a domain at locations without observational data. We introduce an approach to daily weather simulation relying on multivariate latent and transformed
Gaussian processes. In particular, we simulate daily maximum temperature, minimum temperature and precipitation, and discuss modeling approaches that allow the relationship between variables to vary across the simulation domain. A two-part model ensures locally accurate, as well as spatially and temporally correlated, simulations. The method is illustrated on a sparse observation network over Iowa, and a denser network over Colorado.