Abstract
Numerical Weather Prediction (NWP) has not yet been able to produce the weather forecast accurately. In order to overcome that, one approach could be taken is ensemble postprocessing. Ensemble is a combination of several methods to improve its accuracy and precision yet still possesses underdispersive nature. Bayesian Model Averaging (BMA) is intended to calibrate the ensemble prediction and create more reliable interval, though, does not consider spatial correlation. Unlike BMA, Geostatistical Output Perturbation (GOP) reckons spatial correlation among many locations altogether. Analysis applied to calibrate the temperature forecast at eight meteorological sites within Jakarta, Bogor, Tangerang and Bekasi (Jabotabek) are BMA and GOP. The ensemble members of BMA are the prediction of PLS, PCR, and Ridge. For training period over 30 days and based on some assessment indicators, BMA is better than GOP in terms of accuracy, precision, and calibration
Highlights
Numerical Weather Prediction (NWP) has not yet been able to produce the weather forecast accurately
As in [3], Bayesian Model Averaging (BMA) combines the whole ensemble member forecast based on weighted mean, posterior probabilities, that depends to some statistical models instead of the single one
Geostatistical Output Perturbation (GOP) is a method of weather forecast being able to generate ensemble prediction of any size based on spatial association identified from the error correlation [5]
Summary
Numerical Weather Prediction (NWP) has not yet been able to produce the weather forecast accurately. Ensemble forecast still possesses underdispersive nature, that is the forecast tends to concentrate at a point with low variance causing the observation outside the predictive interval, as a consequence they need to be calibrated [2]. In order to handle such case, BMA and GOP could be applied to calibrate the ensemble forecast, among others. As in [3], BMA combines the whole ensemble member forecast based on weighted mean, posterior probabilities, that depends to some statistical models instead of the single one. GOP is a method of weather forecast being able to generate ensemble prediction of any size based on spatial association identified from the error correlation [5]. Like BMA, GOP needs iterative approach, Limited-Memory BFGS (L-BFGS), to estimate its spatial parameters due to faster convergence when parameters being interest are large in size [6]
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