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Conditional statistical modeling of spatial extremes using non-homogeneous marked point processes

Marco Oesting and Martin Schlather, School of Business Informatics and Mathematics, Universität Mannheim


We will also develop novel spatial stochastic process models that allow for the simulation of sub-grid scale extreme weather, to generate multiple weather scenarios in ultra-high spatial resolution, conditionally on physically coherent, statistically postprocessed grid-scale ensemble forecasts of high-impact weather, as a further enhancement of the high resolution mesoscale forecasts. Currently available methods fail in this regard, because they are tailored to typical mean weather situations, rather than extremes.

Publication:

 

Oesting, M., and Schlather, M. (2013). Conditional sampling for max-stable processes with a mixed moving maxima representation. Extremes, doi: 10.1007/s10687-013-0178-1

Oesting, M., Schlather, M. and Friederichs, P. (2013). Conditional modelling of extreme wind gusts by bivariate Brown-Resnick processes. Available at http://arxiv.org/abs/1312.4584.

Oesting, M., Schlather, M. and Zhou, C. (2013). On the normalized spectral representation of max-stable proceses on a compact set. Submitted to Bernoulli. Available at http://arxiv.org/abs/1310.1813.

 

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