The Usage of Ensemble Model Output Statistics for Calibration and Short-term Weather Forecast

Numerical Weather Prediction is a weather forecasting method that is translated into a system of mathematical equations that are solved by numerical methods. The transformation of the basic theory of NWP into computer code still produces errors. To reduce errors and increase the accuracy of the prediction results of the NWP model, statistical postprocessing can be performed using the Model Output Statistics (MOS) method. The use of model output statistics for weather prediction still has a deficiency, namely, it still produces high bias. To increase the accuracy of the prediction model, it can use the ensemble model output statistics (EMOS). This approach is set out from the ensemble prediction system (EPS) which has an understanding as a model consisting of a combination of two or more single prediction models that are verified at the same time. This technique yields probabilistic forecasts that take the form of Gaussian predictive probability density functions (PDFs) for continuous weather variables. The ensemble members in this study consist of prediction from PLS, PCR, and Ridge Regression. In these performances, EMOS offers predictive PDF and CDF from an ensemble forecast of a continuous weather variable, but it is not considered spatial correlation. For the training period over 20,30 and 40 days, EMOS temperature forecast at 3 sites into good and fair ones. Based on weather prediction assessment indicators like RMSE and CRPS, EMOS is better than raw ensemble in terms of accuracy and precision.