Given the uncertainties in initial conditions of weather and climate, forecasts should be and are increasingly issued in a probabilistic way. These forecasts then account for the uncertainties due to imperfectly known initial conditions and potentially also for model uncertainties. One issue frequently observed for probabilistic forecasts is that they tend to be not reliable, i.e. the forecasted probabilities are not consistent with the relative frequency of the associated observed events.
This project aims at developing and implementing post-processing approaches for (re-)calibrating the MiKlip decadal prediction ensemble; addressing the typical problems encountered for decadal predictions, i.e. relatively small ensemble sizes and limited availability of hindcast-observation pairs. Starting with normally distributed variables, strategies will be specifically tailored to problems encounter for decadal predictions (model drift, climate trend). In a later stage, non-normally distributed quantities will be considered, as variables relevant to the end-user do not necessarily follow a Gaussian distribution (e.g. precipitation, humidity or wind gusts). Moreover calibration methods for probabilistic forecasts of dichotomous and countable events (e.g., droughts) will be also considered. An implementation into the central evaluation system (CES) allows all other MiKlip projects to (re-)calibrate the ensemble predictions and prepares the calibration for operational use.
Weigel, A.P., M. A. Liniger, and C. Appenzeller. 2008: Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Quart. J. Royal Meteor. Soc., 134 (630):241 260.
Weigel, A.P., M. A. Liniger, and C. Appenzeller, 2009: Seasonal ensemble forecasts: Are recalibrated single models better than multimodels? Mon. Weather Rev., 137(4):1460–1479.
Freie Universität Berlin, Institute for Meteorology
Prof. Dr. Uwe Ulbrich
Prof. Dr. Henning Rust
M.Sc. Alexander Pasternack
Max-Planck-Institut für Meteorologie
Dr. Wolfgang Müller
Dr. Mark Liniger
Dr. Jonas Bhend