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X-WR-CALNAME;VALUE=TEXT:StatClimatol: Doug Nychka
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UID:event_1121846_0
SUMMARY:StatClimatol: Doug Nychka
DESCRIPTION:Statistical Models for Spatial Structure in Climate Simulations and DataThe interest in the regional effects of climate change has motivated the analysis of large spatial and space-time data that are the result of numerical models. Typically the model output involves grids of several thousand points and standard methods of spatial statistics break when applied to these large data sets. This talk will present a flexible spatial model based on fixed rank Kriging that can handle a large number of spatial locations and also include nonstationary spatial dependence. This is feasible using compactly supported basis functions and spatial dependence based on Markov random fields. Using this method we estimate the change in the seasonal cycle of temperature over the US from climate simulations from the North American Regional Climate Change and Assessment Program (NARCCAP). Part of this analysis is to account for topography and other covariates and to determine the effect of specific pairings of global and regional models on the results.
LOCATION:HUCE Seminar Room (3rd floor Geological Museum)
STATUS:CONFIRMED
DTSTART:20140204T200000Z
DTEND:20140204T210000Z
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