E-WP9 - CALIBRATION: Calibration of probabilistic decadal climate forecasts

Calibration of probabilistic decadal climate forecasts

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.

CALIBRATION Teaser picture

Goals:

  • Transferring the climate conserving recalibration (CCR) approach described by Weigel et al. (2009) to the MiKlip decadal prediction system. Initially this approach has been designed for normally distributed quantities for the seasonal timescale and a stationary setting, i.e. no model drift and no climate trend. This is in contrast to the Miklip decadal prediction system showing typically the following characteristics: non-stationarity (climate trend), model drifts away from its initialization state towards model climatology, small ensemble size, and quantities which are non-normally distributed.
  • Investigation of effects on the CCR due to climate trend, model drift due to initialization, and small ensemble sizes, as well as potential ways of correcting deviations due to small-samples with respect to synthetically generated ensemble predictions, also called ‘toy model’ (Weigel et al., 2008). 
  • Investigation of possibilities to simultaneously re-calibrate and account for climate trend and model drifts.
  • Transferring the previous results to quantities which are not well described by a normal distribution.
  •  Implementation of calibration of probabilistic forecasts regarding dichotomous and countable events.
  • Implementation of the developed algorithms/software into the CES. Documentation of the software for operational use.

References

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.

Contact

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

MeteoSwiss
Dr. Mark Liniger
Dr. Jonas Bhend