Probabilistic temperature forecasting: a comparison of four spread-regression models

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dc.creatorJewson, Stephen
dc.date2004-10-08
dc.date.accessioned2026-07-07T05:52:50Z
dc.date.available2026-07-07T05:52:50Z
dc.descriptionSpread regression is an extension of linear regression that allows for the inclusion of a predictor that contains information about the variance. It can be used to take the information from a weather forecast ensemble and produce a probabilistic prediction of future temperatures. There are a number of ways that spread regression can be formulated in detail. We perform an empirical comparison of four of the most obvious methods applied to the calibration of a year of ECMWF temperature forecasts for London Heathrow.
dc.identifierhttps://arxiv.org/abs/physics/0410053
dc.identifierhttp://arxiv.org/abs/physics/0410053
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/86450
dc.subjectAtmospheric and Oceanic Physics
dc.titleProbabilistic temperature forecasting: a comparison of four spread-regression models
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