Abstract
Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles in the form of predictive PDFs. It is known that BMA is able to improve the reliability of probabilistic forecast of short range and medium range rainfall forecast. This study aims to develop the application of BMA to calibrate seasonal forecast (long range) in order to improved quality of seasonal forecast in Indonesia. The seasonal forecast used is monthly rainfall from the output of the ensemble prediction system European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4) and it is calibrated against observational data at 26 stations of the Agency for Meteorology Climatology and Geophysics of Republic of Indonesia (BMKG) in Java Island in 1981 – 2018. BMA predictive PDFs is generated with Gamma distribution approach which is obtained based on sequential training windows (JTS) and conditionals training windows (JTC). BMA-JTS approach is done by selecting the width of the 30-month training window as the optimal training period while the BMA-JTC is carried out with a cross-validation scheme for each month. In general, Both of BMA-JTS and BMA-JTC better than RAW models. BMA-JTC calibration results are varying according to spatial and temporal, but in general the result is better in the dry season and during the El Nino phase. BMA is able to improve the distribution characteristics of the RAW model ECS4 prediction which is shown by: a smaller value of Continuous Rank Probability Score (CRPS), a larger value of the Continuous Rank Probability Skill Score (CRPSS) and more flat form of the Verification Rank Histogram (VRH) than the RAW model. BMA also increases the skill, esolution and reliability of prediction of probability Below Normal (BN) and Above Normal (AN), which is known from the increasing Brier Skill Score (BSS), and the increasing area under curve of Relative Operating Characteristics (ROC) compared to the RAW model. Furthermore, the reliability of BN and AN of BMA results also has the category of “still very useful” and “perfect” compared to RAW models that are in the “dangerous”, “not useful” and “marginally useful” categories. The reliability of BMA results with the category “still very useful” and “perfect” show that the probabilistic forecast of BN and AN events can be used in making decisions related to seasonal forecast.
Highlights
Bayesian Model Averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from an ensemble prediction in the form of predictive Probability Density Function (PDF)
Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature
Precipitation Calibration Based on the Frequency-Matching Method
Summary
Data Observasi dan Model Data yang digunakan berupa jumlah curah hujan dalam satu bulan. Data observasi diambil dari pengamatan pada 26 stasiun BMKG di P. Semua stasiun mempunyai data kosong kurang dari 20% dari periode 1981-2018 (456 bulan). Data kosong diisi dengan nilai klimatologi bulan yang bersesuaian. Selanjutnya, data model yang dikalibrasi adalah curah hujan keluaran model ECS4. Tabel 1 Karakteristik 26 stasiun BMKG di Pulau Jawa yang disusun berdasarkan jumlah persentase nilai curah hujan nol berdasarkan data curah hujan bulanan (Jan 1981 – Des 2018)
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