Calibrating surface temperature forecasts using BMA method over Iran

Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-meter temperature over Iran. The ensemble makes use of three limited area models (WRF, MM5 and HRM), with WRF used five times with different configurations. The resulting ensemble of eight members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and corresponding verification data. The calibrated probabilistic forecasts were assessed using flatness of rank histogram and attribute diagrams. Results showed that application of BMA improved the calibration of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic style BMA forecasts performed usually better than the best member's deterministic forecast.