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Extended logistic regression is a recent ensemble calibration method that extends logistic regression to provide full continuous probability distribution forecasts. It assumes conditional logistic distributions for the (transformed) predictand and fits these using selected predictand category...
Persistent link: https://www.econbiz.de/10010197616
To achieve well calibrated probabilistic forecasts, ensemble forecasts often need to be statistically post-processed. One recent ensemble-calibration method is extended logistic regression which extends the popular logistic regression to yield full probability distribution forecasts. Although...
Persistent link: https://www.econbiz.de/10009787084
Raw ensemble forecasts display large errors in predicting precipitation amounts and its forecast uncertainty, especially in mountainous regions where local e.ects are often not captured. Therefore, statistical post-processing is typically applied to obtain automatically corrected weather...
Persistent link: https://www.econbiz.de/10011542308
To post-process ensemble predictions to a particular location, often statistical methods are used, especially in complex terrain such as the Alps. When expanded to several stations, the post-processing has to be repeated at every station individually thus losing information about spatial...
Persistent link: https://www.econbiz.de/10011449375
Non-homogeneous regression is often used to statistically post-process ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input but other potentially useful information sources are ignored. Although it is straightforward to add further input variables,...
Persistent link: https://www.econbiz.de/10011434081
Statistical post-processing of ensemble predictions is usually adjusted to a particular lead time so that several models must be fitted to forecast multiple lead times. To increase the coherence between lead times, we propose to use standardized anomalies instead of direct observations and...
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