Evaluation of Bias Correction Method for Monthly Rainfall Prediction of ECMWF SEAS5 in Indonesia

Authors

  • Rheinhart C H Hutauruk BMKG
  • Edi Rahmanto Meteorology, Climatology, and Geophysical Agency (BMKG)
  • Abdul Hamid Al Habib Bandung Institute of Technology (ITB)
  • Priskila Wilhelmina Yoku Bandung Institute of Technology (ITB)
  • I Wayan Gita Giriharta Bandung Institute of Technology (ITB)
  • Nurjanna Joko Trilaksono Bandung Institute of Technology (ITB)
  • Tri Wahyu Hadi Bandung Institute of Technology (ITB)

DOI:

https://doi.org/10.31172/jmg.v25i2.1124

Keywords:

Precipitation, Bias Correction, ECMWF SEAS5, Indonesia

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.

References

A. T. Maulana and A. Andriansyah, "Mitigasi Bencana di Indonesia," COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat, vol. 3, no. 10, pp. 3996–4012, Feb. 2024, doi: 10.59141/comserva.v3i10.1213.

Yang, D., Wang, L., & Zhang, X. (2023). Impact of Climate Change on Rainfall Prediction and Disaster Management: A Review of Bias Correction Techniques. Environmental Modelling & Software, 163, 107132.

C. Müller et al., "Climate model configurations of the ECMWF Integrated Forecasting System (ECMWF-IFS cycle 43r1) for HighResMIP," Geoscientific Model Development, vol. 9, pp. 3681–3712, Sep. 2018, doi: 10.5194/gmd-11-3681-2018.

Gao, J., Zhao, L., & Zhang, L. (2022). Assessing the Performance of ECMWF Seasonal Prediction System in Tropical Regions: A Case Study in Southeast Asia. Climate Dynamics, 59(4), 1459–1472.

H. Qian and H. Xu, "Prediction of Autumn Precipitation over the Middle and Lower Reaches of the Yangtze River Basin Based on Climate Indices," Climate, vol. 8, no. 4, pp. 53, Apr. 2023, doi: 10.3390/cli8040053.

W. G. Kurnia et al., "Performa Koreksi Bias Prakiraan Curah Hujan Model European Centre Medium Weather Forecast (ECMWF) di Sulawesi," Buletin GAW Bariri (BGB), vol. 1, pp. 77–86, Dec. 2020, doi: 10.31172/bgb.v1i2.28.

C. Reyhan, "Evaluasi Luaran Model S2S (Subseasonal To Seasonal) ECMWF Dalam Menangkap Variabilitas Hujan Ekstrem di Sumatera Barat," Megasains, vol. 14, no. 2, pp. 14–22, Dec. 2023.

R. Muharsyah et al., "Implementation of Bayesian Model Averaging Method to Calibrate Monthly Rainfall Ensemble Prediction over Java Island," Agromet, vol. 34, no. 1, pp. 20–33, Apr. 2020, doi: 10.29244/j.agromet.34.1.20-33.

S. Hagemann, C. Chen, J. O. Haerter, J. Heinke, D. Gerten, and C. Piani, "Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models," Journal of Hydrometeorology, vol. 12, no. 4, pp. 556–578, Aug. 2011.

S. Shrestha et al., "Bias correction of climate models for hydrological modelling–are simple methods still useful?," Meteorological Applications, vol. 24, pp. 531–539, Jul. 2017, doi: 10.1002/met.1655.

Y. Luo et al., "Comparing bias correction methods used in downscaling precipitation and temperature from regional climate models: a case study from the Kaidu River Basin in Western China," Water, vol. 10, pp. 1046, Aug. 2018, doi: 10.3390/w10081046.

C. Piani et al., "Statistical bias correction for daily precipitation in regional climate models over Europe," Theoretical and Applied Climatology, vol. 99, pp. 187–192, Jan. 2010, doi: 10.1007/s00704-009-0134-9.

J. Rufibach, “Use of Brier score to assess binary predictions,” Journal of Clinical Epidemiology, vol. 63, pp. 938–939, Aug. 2010, doi: 10.1016/j.jclinepi.2009.11.008.

Ben Bouallègue, Z., & Richardson, D. S, “On the ROC area of ensemble forecasts for rare events,” Weather and Forecasting, vol. 37, no. 5, pp. 787–796, May. 2022, doi: https://doi.org/10.1175/WAF-D-21-0195.1.

D. A. Lavers, S. Harrigan, and C. Prudhomme, “Precipitation biases in the ECMWF integrated forecasting system,” Journal of Hydrometeorology, vol. 22, no. 5, pp. 1187–1198, 2021.

D. Lealdi, S. Nurdiati, and A. Sopaheluwakan, “Statistical bias correction modelling for seasonal rainfall forecast for the case of Bali island,” in Journal of Physics: Conference Series, vol. 1008, no. 1, p. 012018, Apr. 2018, doi: 10.1088/1742-6596/1008/1/012018.

Widiastuti, E., & Andriani, D. (2020). Evaluation of Quantile Mapping and Linear Scaling for Bias Correction in ECMWF SEAS4 for Indonesia’s Seasonal Rainfall. Environmental Modelling & Software, 134, 104845.

Agustina, F., & Fajar, D. (2021). Assessing the Performance of Bias Correction Methods on ECMWF’s Sub-seasonal Predictions in Southeast Asia. Climate Dynamics, 56(7), 2053–2069.

S. Schauwecker, M. Schwarb, M. Rohrer, and M. Stoffel, “Heavy precipitation forecasts over Switzerland–an evaluation of bias-corrected ECMWF predictions,” Weather and Climate Extremes, vol. 34, p. 100372, 2021.

D. P. Dee, “Variational bias correction of radiance data in the ECMWF system,” in Proceedings of the ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, Reading, UK, vol. 28, pp. 97–112, Jun. 2004.

Liu, Y., & Li, X. (2020). "Improved calibration techniques for seasonal climate forecasts: A case study of precipitation over Southeast Asia." Journal of Climate Science, 10(2), 255–267.

Wang, J., et al. (2022). "Quantitative analysis of bias correction methods for seasonal precipitation forecasts: Application to the ECMWF SEAS5 model." Atmospheric Research, 254, 105–121.

Cheng, M., & Zhang, L. (2021). "Bias correction in seasonal prediction models for tropical regions: A focus on Southeast Asia." Geophysical Research Letters, 48(15), e2021GL092832.

Huang, H., et al. (2023). "Assessment of statistical bias correction techniques for improving seasonal rainfall forecasts in tropical climates." Climate Dynamics, 61(5), 1637–1651.

C. Teutschbein et al., “Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods,” Journal of Hydrology, vol. 456, pp. 12–29, Aug. 2012, doi: 10.1016/j.jhydrol.2012.05.052.

Y. Apriyana and T. E. Kailaku, “Climate variability and dynamics of rice planting time in the monsoonal and equatorial region,” Prosiding Seminar Nasional Masyarakat Biodiversitas Indonesia, vol. 1, pp. 366–372, Apr. 2015, doi: 10.13057/psnmbi/m010233.

M. Mamenun et al., “Validasi dan koreksi data satelit TRMM pada tiga pola hujan di Indonesia,” Jurnal Meteorologi dan Geofisika, vol. 15, Apr. 2014, doi: 10.31172/jmg.v15i1.169.

S. Mason et al., “Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation,” Quarterly Journal of the Royal Meteorological Society, vol. 128, pp. 2145–2166, Jul. 2020, doi: 10.1256/003590002320603584.

Maraun, D., “Bias correcting climate change simulations – a critical review,” Current Climate Change Reports, vol. 2, pp. 211–220, 2016.

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Published

2025-06-28

How to Cite

Hutauruk, R. C. H., Rahmanto, E., Al Habib, A. H., Yoku, P. W., Giriharta, I. W. G., Trilaksono, N. J., & Hadi, T. W. (2025). Evaluation of Bias Correction Method for Monthly Rainfall Prediction of ECMWF SEAS5 in Indonesia. Jurnal Meteorologi Dan Geofisika, 25(2), 103–112. https://doi.org/10.31172/jmg.v25i2.1124

Issue

Vol. 25 No. 2 (2024)

Section

Article

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