Spatially Adaptive Post-Processing of Ensemble Forecasts for Temperature

抄録

<jats:title>Summary</jats:title><jats:p>We propose a statistical post-processing method that yields locally calibrated probabilistic forecasts of temperature, based on the output of an ensemble prediction system. It represents the mean of the predictive distributions as a sum of short-term averages of local temperatures and ensemble prediction system driven terms. For the spatial interpolation of temperature averages and local forecast uncertainty parameters we use an intrinsic Gaussian random-field model with a location-dependent nugget effect that accounts for small-scale variability. Applied to the COSMO-DE ensemble, our method yields locally calibrated and sharp probabilistic forecasts and compares favourably with other approaches.</jats:p>

収録刊行物

被引用文献 (1)*注記

もっと見る

問題の指摘

ページトップへ