Using Bayesian Model Averaging to Calibrate Temperature Forecast Ensembles in Hawassa Town, Ethiopia

Abstract

Bayesian model averaging (BMA) is a statistical way of post-processing forecasts ensembles to create predictive probability density functions (pdfs) for weather quantities. BMA has been proposed as way of correcting underdispersion in ensemble forecasts. The output of ensemble BMA is a weighted average of pdfs centered on the individual bias-corrected ensemble forecast. The BMA weights are posterior probabilities of the models generating the forecast, and show the relative contribution of the component models to the prediction over the training period. This paper focuses on short range forecasting of daily maximum temperature. Forecasting temperature, we have approximated the conditional pdf by normal distribution centered at a linear function of the forecast. Since we are not able to get an ensemble of forecasts for Ethiopia, we have used the previous eight years from 2003 to 2010 data as ensemble and 2011 as observation. The BMA approach yields predictive distribution for temperature thatwere much better calibrated than those based on naive averaging of the forecasts.