Calibrating short-range ensemble forecasts using estimated model error variances

This study investigates the impact of a calibration technique using estimated global model error variances on the performance of short-range ensemble forecasts. The calibration technique is rather simple as it consists of adding a random white noise of zero mean and variance equal to estimated model error variances. Two calibrated ensemble forecasts are generated, one using a homogeneous calibration factor and another one using a heterogeneous calibration factor. Based on probabilistic scores, the qualities of these two ensemble forecasts are evaluated against a non-calibrated ensemble and the operational ensemble prediction system of Meteo-France. The results show that this calibration technique significantly improves the reliability and, to a less extent, the resolution of ensemble forecasts. Better reliability results are found using a spatially heterogeneous calibration factor instead of using the homogeneous calibration factor at forecast ranges beyond 48 and 72 h.