  Currently all major meteorological centres generate ensemble forecasts using
their operational ensemble prediction systems; however, it is a general problem
that the spread of the ensemble is too small, resulting in underdispersive
forecasts, leading to a lack of calibration. In order to correct this problem,
different statistical calibration techniques have been developed in the last
two decades. In the present work different post-processing techniques are
tested for calibrating 9 member ensemble forecast of temperature for Santiago
de Chile, obtained by the Weather Research and Forecasting (WRF) model using
different planetary boundary layer and land surface model parametrization. In
particular, the ensemble model output statistics (EMOS) and Bayesian model
averaging techniques are implemented and since the observations are
characterized by large altitude differences, the estimation of model parameters
is adapted to the actual conditions at hand. Compared to the raw ensemble all
tested post-processing approaches significantly improve the calibration of
probabilistic and the accuracy of point forecasts. The EMOS method using
parameter estimation based on expert clustering of stations (according to their
altitudes) shows the best forecast skill.

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