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

2004/10/08 by Stephen Jewson, Jewson, Stephen
Earth and Planetary Sciences · Environmental Science · Physics and Astronomy · #Climate variability and models #Meteorological Phenomena and Simulations #Plant Water Relations and Carbon Dynamics #physics.ao-ph

paper · pdf · doi:10.48550/arxiv.physics/0410053

arxiv created 2004/10/08 · arxiv updated 2009/12/01

Abstract

Spread 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.

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