Statistical calibration of temperature and wind speed forecasts in the central area of Chile / Mailiu Díaz Peña.

Doctor en Estadística.

The improvement of the weather forecasts is essential around the world because it has an impact on dierent elds such as renewable energy, air quality, and other meteorological events. Ensemble prediction systems have been developed in the last years but their forecasts are often aected by biases especially when implemented on complex terrains. The main aim of this work is to improve the prediction of surface temperature and wind speed in the central zone of Chile by using statistical post-processing techniques. In order to do this, we rst generate an ensemble of nine members from WRF model using dierent initial conditions. Then, we present some statistical post-processing techniques such as the ensemble model output statistic (EMOS), the Bayesian model averaging (BMA), the quantile regression forest (QRF) and the ensemble copula coupling (ECC). The proposed calibration techniques are implemented with a regional and semi-local approach. Also, we include other variables from the WRF simulations to calibrate wind speed with the QRF model and we determine the importance of each predictor in the calibration model. As results of the two case studies (surface temperature and wind speed), we determine that EMOS with normal distribution provided better performance to calibrate the surface temperature in Santiago, and QRF including additional features is the best for calibrating the wind speed around Valparaso and Santiago de Chile. Also, both models provide better performances with a semi-local approach. Finally, some conclusions and new insights are given in order to develop bi-variate techniques.