Publication: Extension of Geostatisical Output Perturbation (GOP) Method for Probabilistic Weather Forecasting of Surface Temperature

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Title	Extension of Geostatisical Output Perturbation (GOP) Method for Probabilistic Weather Forecasting of Surface Temperature
Authors/Editors*	Erika Kramer and Yulia Gel
Where published*	The 1st TIES North American Regional Meeting, Seattle, WA, USA. (TIES stands for The International Environmetrics Society.)
How published*	Proceedings
Year*	2007
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Keywords	spatio-temporal modelling, probabilistic weather forecasting, CART
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Abstract	Our project focuses on further development of Geostatistical Output Perturbation (GOP) method for probabilistic mesoscale weather forecasting of surface temperature. In particular, in order to more accurately capture spatio-temporal non-stationarity of surface temperature, we apply GOP to local subdomains in the US Pacific North-West. The subdomains are selected by Classification and Regression Trees (CART) for different seasons. The subdomain selection remains consistent through different periods of time. We develop a hierarchical GOP approach by modeling sills of variograms as a spatio-temporal random process rather than a deterministic quantity and then generating GOP ensembles with a randomly selected sill. The resulting prediction intervals appear to be calibrated and shorter than from the usual GOP.