Probabilistic prediction of raw and BMA calibrated AEMET-SREPS: the 24 of January 2009 extreme wind event in Catalunya

At 00:00UTC of 24 January 2009 (24Jan09) an explosive cyclogenesis placed at the Gulf of Vizcaya, reached its maximum intensity with observed surface pressures below 970 hPa on its center. During its path through the south of France there were strong westerly and northwesterly winds over Iberian Peninsula (above 150km/h). These extreme winds leaved 8 casualties in Catalunya, the north-east region of Spain.The aim of this work is validating the skill of the Spanish Meteorological Agency (AEMET) Short Range Ensemble Prediction System (SREPS) in forecasting this event. Two probabilistic forecasts of wind are compared, a non-calibrated (or raw) and a calibrated one using the Bayesian Model Averaging (BMA).AEMET runs a daily experimental SREPS twice a day (00:00 and 12:00UTC). This system consists on 25 members that are constructed by integrating five different Limited Area Models (LAMs) at 0.25 degrees of horizontal resolution. Each model uses five different initial and boundary conditions from five Global Models (GMs). Thus it is obtained a probabilistic forecast that takes into account initial, contour and model uncertainties.BMA is a statistical tool for combining predictive Probability Distribution Functions (PDFs) from different sources. BMA predictive PDF is a weighted average of PDFs centered on the individual bias-corrected forecasts. Each weight is a measure of the corresponding forecast skill. Here BMA is applied to calibrate probabilistic forecasts of wind speed.In this work two time forecast ranges (H+60 and H+36) of 10-m wind speed over Catalonia are verified subjectively at 12:00 UTC of 24Jan09 valid time. We focus on the location and intensity of 10-m wind speed maximum values. Observations at 29 automatic ground stations of AEMET are used for the verification.On one hand results indicate that raw AEMET-SREPS is able to forecast 60 h ahead mean winds higher than 36 and 54 km/h and that it correctly locates them in three different areas. On the other hand, predicted probability loses its skill after BMA calibration of the ensemble. This is due to the fact that BMA bias correction underestimates the intensity of wind.