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All Journal Agromet Jurnal Meteorologi dan Geofisika
Tri Wahyu Hadi, Tri Wahyu
Faculty of Earth Sciences and Technology, Bandung Institute of Technology Jalan Ganesa No. 11, Bandung 40132, West Java, Indonesia
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Energy Physics

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Implementation of Bayesian Model Averaging Method to Calibrate Monthly Rainfall Ensemble Prediction over Java Island Muharsyah, Robi; Hadi, Tri Wahyu; Indratno, Sapto Wahyu
Agromet Vol. 34 No. 1 (2020): JUNE 2020
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1296.784 KB) | DOI: 10.29244/j.agromet.34.1.20-33

Abstract

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). BMA is commonly used to calibrate Ensemble Prediction System (EPS) in a shorter-range forecast. Here, we applied the BMA for a longer forecast at a seasonal interval. This study aimed to develop the implementation of the BMA method to calibrate the seasonal forecast (long range) of monthly rainfall from the RAW output of the EPS European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4). This model was calibrated with observational data from 26 stations over Java Island in 1981-2018. BMA predictive PDF was generated with a gamma distribution, which was obtained based on two training schemes, namely sequential (BMA-JTS) and conditional (BMA-JTC) training windows. Generally, both of BMA-JTS and BMA-JTC were able to produce better distribution characteristics of ensemble prediction than that of RAW model ECS4. Both BMA methods showed a good performance as indicated by a high accuracy, small bias, and small uncertainty to the observed rainfall. Our findings revealed that BMA-JTC was able to improve the quality of probabilistic forecasts of below and above normal events. The improvement was shown in most stations over Java Island, in which the model was a good skill forecast based on Brier Skill Score (BSS).
EVALUASI METODE KOREKSI BIAS UNTUK PREDIKSI CURAH HUJAN BULANAN ECMWF SEAS5 DI INDONESIA Hutauruk, Rheinhart C H; Rahmanto, Edi; Al Habib, Abdul Hamid; Yoku, Priskila Wilhelmina; Giriharta, I Wayan Gita; Trilaksono, Nurjanna Joko; Hadi, Tri Wahyu
Jurnal Meteorologi dan Geofisika Vol. 25 No. 2 (2024)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v25i2.1124

Abstract

The seasonal rainfall forecast from ECMWF SEAS5 often suffers from biases that reduce its accuracy, limiting its use in applications like water resource management and agricultural planning. This study evaluates the effectiveness of bias correction methods in enhancing the skill of ECMWF SEAS5 seasonal precipitation forecasts in Indonesia. Observational data from 148 BMKG rain gauges and SEAS5 raw output from 2011 to 2020 are used. Three bias correction methods—linear scaling (LS), empirical distribution quantile mapping (EQM), and gamma distribution quantile mapping (GQM)—are applied to the raw model. Model performance is assessed using scatter plots, root mean square error (RMSE), correlation, and Taylor diagrams. The results show LS consistently outperforms EQM and GQM, reducing RMSE from 128 to 102 and improving correlation from 0.57 to 0.65. Additionally, Brier Score (BS) and Relative Operating Characteristic (ROC) analysis highlight significant improvements in probabilistic predictions, especially in areas with high rainfall variability. These findings indicate LS as a particularly effective approach for bias correction, enhancing accuracy and reliability. This study underscores the potential of applying bias correction methods like LS to improve ECMWF SEAS5 forecasts, supporting better decision-making for climate change adaptation and mitigation in Indonesia.
Co-Authors Al Habib, Abdul Hamid Giriharta, I Wayan Gita Hutauruk, Rheinhart C H Indratno, Sapto Wahyu Muharsyah, Robi Rahmanto, Edi Trilaksono, Nurjanna Joko Yoku, Priskila Wilhelmina