Abstract
Abstract. Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven 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 relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability 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.
Highlights
Ensemble forecasting is a numerical prediction method that samples the uncertainties in initial conditions and model formulation
This paper describes the results of 48-h probabilistic surface temperature forecasts over Iran for the period of 15 December 2008 to 11 June 2009 using Bayesian Model Averaging for calibration of the ensemble outputs
The ensemble system consists of the Weather Research and Forecasting (WRF) model with five different configurations, MM5 and High Resolution Model (HRM) both with one configuration
Summary
In the last couple of years various statistical methods such as logistic regression (Wilks, 2006), Bayesian Model Averaging (Raftery et al, 2005), non-homogeneous Gaussian regression (Gneiting et al, 2005) and Gaussian ensemble dressing (Roulston and Smith, 2003; Wang and Bishop, 2005), among others, have been developed for calibrating the raw ensemble forecasts. Sloughter et al (2007) used a mixture of a discrete component at zero and a gamma distribution as predictive PDF for individual ensemble members and applied the BMA to daily 48-h forecasts of 24-h accumulated precipitation in the North American Pacific Northwest in 2003– 2004 using the University of Washington mesoscale ensemble. They could get PDFs corresponding to probability of precipitation forecasts that were much better calibrated compared to consensus voting of the ensemble members.
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