Statistical bias correction on the climate model for el nino index prediction

Sri Nurdiati, Ardhasena Sopaheluwakan, Yoga Abdi Pratama, Mohamad Khoirun Najib


El Nino can harm many sectors in Indonesia by reducing precipitation levels in some areas. The occurrence of El Nino can be estimated by observing the sea surface temperature in Nino 3.4 region. Therefore, an accurate model on sea surface temperature prediction in Nino 3.4 region is needed to optimize the estimation of the occurrence of El Nino, such as ECMWF. However, the prediction model released by ECMWF still consists of some systematic errors or biases. This research aims to correct these biases using statistical bias correction techniques and evaluate the prediction model before and after correction. The statistical bias correction uses linear scaling, variance scaling, and distribution mapping techniques. The results show that statistical bias correction can reduce the prediction model bias. Also, the distribution mapping and variance scaling are more accurate than the linear scaling technique. Distribution mapping has better RMSE in December-March, and variance scaling has better RMSE in April-June also in October and November. However, in July-September, prediction from ECMWF has better RMSE. The application of statistical bias correction techniques has the highest refinement in January-March at the first lead time and in April at the fifth until the seventh lead time.



Distribution Mapping, El Nino-Southern Oscillation, Linear Scaling, Statistical Bias Correction, Variance Scaling.

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Alidoost, F., Stein, A., Su, Z., & Sharifi, A. (2021). Multivariate copula quantile mapping for bias correction of reanalysis air temperature data. Journal of Spatial Science, 66(2), 299–315.

Bahari, N. I. S., Muharam, F. M., Zulkafli, Z., Mazlan, N., & Husin, N. A. (2021). Modified linear scaling and quantile mapping mean bias correction of MODIS land surface temperature for surface air temperature estimation for the lowland areas of peninsular malaysia. Remote Sensing, 13(13), 2589.

Barbosa, C. C., Calijuri, M. do C., dos Santos, A. C. A., Ladwig, R., de Oliveira, L. F. A., & Buarque, A. C. S. (2021). Future projections of water level and thermal regime changes of a multipurpose subtropical reservoir (Sao Paulo, Brazil). Science of the Total Environment, 770.

Bennett, S. (1983). Log-logistic regression models for survival data. Applied Statistics, 32(2), 165–171.

Chen, J., Brissette, F. P., & Leconte, R. (2011). Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. Journal of Hydrology, 401(3–4), 190–202.

de Haan, L., & Ferreira, A. (2006). Extreme value theory: An introduction. In Springer Series in Operations Research and Financial Engineering. Springer.

Enayati, M., Bozorg-Haddad, O., Bazrafshan, J., Hejabi, S., & Chu, X. (2021). Bias correction capabilities of quantile mapping methods for rainfall and temperature variables. Journal of Water and Climate Change, 12(2), 401–419.

Hintze, J. L., & Nelson, R. D. (1998). Violin plots: A box plot-density trace synergism. American Statistician, 52(2), 181–184.

Hogg, R. V., & Craig, A. T. (1978). Introduction to mathematical statistics. In The Mathematical Gazette (4th ed., Vol. 45, Issue 354). MacMillan.

Irawan, B. (2006). Fenomena anomali iklim el nino dan la nina: Kecenderungan jangka panjang dan pengaruhnya terhadap produksi pangan. Forum Penelitian Agro Ekonomi, 24(1), 28–45.

Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions (2nd ed.). John Wiley.

Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis. Pearson Prentice Hall.

Lealdi, D., Nurdiati, S., & Sopaheluwakan, A. (2018). Statistical bias correction modelling for seasonal rainfall forecast for the case of Bali Island. Journal of Physics: Conference Series, 1008(1), 012018.

Lenderink, G., Buishand, A., & Van Deursen, W. (2007). Estimates of future discharges of the river rhine using two scenario methodologies: Direct versus delta approach. Hydrology and Earth System Sciences, 11(3), 1145–1159.

Liddle, A. R. (2007). Information criteria for astrophysical model selection. Monthly Notices of the Royal Astronomical Society: Letters, 377(1), L74–L78.

Mayer, J. (1987). Two-moment decision models and expected utility maximization. American Economic Review, 77, 421–430.

Misnawati, Boer, R., June, T., & Faqih, A. (2018). Perbandingan metodologi koreksi bias data curah hujan CHIRPS. LIMNOTEK - Perairan Darat Tropis Di Indonesia, 25(1), 18–29.

Mitra, R., Mishra, A. K., & Choubisa, T. (2012). Maximum likelihood estimate of parameters of nakagami-m distribution. Proceedings of the 2012 International Conference on Communications, Devices and Intelligent Systems, CODIS 2012, 9–12.

Najib, M. K., & Nurdiati, S. (2021). Koreksi bias statistik pada data prediksi suhu permukaan air laut di wilayah Indian ocean dipole barat dan timur. Jambura Geoscience Review, 3(1), 9–17.

Nurdiati, S., Khatizah, E., Najib, M. K., & Fatmawati, L. L. (2021). El nino index prediction model using quantile mapping approach on sea surface temperature data. Desimal: Jurnal Matematika, 4(1), 79–92.

Nurdiati, S., Sopaheluwakan, A., & Najib, M. K. (2019). Statistical bias correction for predictions of Indian ocean dipole index with quantile mapping approach. International MIPAnet Conference on Science and Mathematics (IMC-SciMath), Medan.

Papoulis, A. P., & Pillai, S. U. (2002). Probability, random variables, and stochastic processes (4th ed.). McGraw-Hill.

Philander, S. G. H. (1983). El Nino southern oscillation phenomena. Nature, 302(5906), 295–301.

Piani, C., Haerter, J. O., & Coppola, E. (2010). Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology, 99(1–2), 187–192.

Rahimi, R., Tavakol-Davani, H., & Nasseri, M. (2021). An uncertainty-based regional comparative analysis on the performance of different bias correction methods in statistical downscaling of precipitation. Water Resources Management.

Sarkar, S. K., & Balakrishnan, N. (1994). Handbook of the logistic distribution. In Journal of the American Statistical Association (Vol. 89, Issue 425). Marcel Dekker.

Shonk, J. K. P., Demissie, T. D., & Toniazzo, T. (2019). A double ITCZ phenomenology of wind errors in the equatorial atlantic in seasonal forecasts with ECMWF models. Atmospheric Chemistry and Physics, 19(17), 11383–11399.

Siddiqui, M. M. (1964). Statistical inference for rayleigh distributions. Journal of Research of the National Bureau of Standards, Section D: Radio Science, 68D(9), 1005.

Singh, S., Mall, R. K., Dadich, J., Verma, S., Singh, J. V., & Gupta, A. (2021). Evaluation of CORDEX-south asia regional climate models for heat wave simulations over india. Atmospheric Research, 248, 105228.

Talukdar, K. K., & Lawing, W. D. (1991). Estimation of the parameters of the rice distribution. Journal of the Acoustical Society of America, 89(3), 1193–1197.

Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456–457, 12–29.

Trenberth, K. (2020). The climate data guide: Nino SST indices (Nino 1+2, 3, 3.4, 4; ONI and TNI).

Yamamoto, K., Sayama, T., & Apip. (2021). Impact of climate change on flood inundation in a tropical river basin in Indonesia. Progress in Earth and Planetary Science, 8(1).

Zhang, L., & Singh, V. P. (2007). Gumbel–hougaard copula for trivariate rainfall frequency analysis. Journal of Hydrologic Engineering, 12(4), 409–419.


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