Koreksi Bias Statistik Pada Data Prediksi Suhu Permukaan Air Laut Di Wilayah Indian Ocean Dipole Barat Dan Timur

Mohamad Khoirun Najib, Sri Nurdiati

Abstract


The IOD can be measured using the Dipole Mode Index (DMI) which is calculated based on the sea surface temperature in the Indian Ocean. Therefore, DMI can be predicted using sea surface temperature forecasting data, such as data provided by the European Center for Medium-Range Weather Forecasts (ECMWF). However, the data still has a bias as compared to the actual data, so to get a more accurate prediction, corrected data is needed. Therefore, the aim of this study is to predict DMI based on sea surface temperature forecasting data that has been corrected for bias using the quantile mapping method, a method that connects the distribution of forecasting and actual data. The results showed that the DMI prediction using corrected data was more accurate than the DMI prediction using ECMWF data. DMI predictions using corrected data have high accuracy to predict IOD events in October-April.

Keywords


Bias Correction; ECMWF; Indian Ocean Dipole; Quantile Mapping

Full Text:

PDF

References


Arreyndip, N. A., & Joseph, E. (2016). Generalized extreme value distribution models for the assessment of seasonal wind energy potential of Debuncha, Cameroon. Journal of Renewable Energy.

Bramawanto, R., & Abida, R. F. (2017). Tinjauan Aspek Klimatologi (ENSO dan IOD) dan Dampaknya Terhadap Produksi Garam Indonesia. Jurnal Kelautan Nasional, 12(2), 91-99.

Buizza, R., Leutbecher, M., & Isaksen, L. (2008). Potential use of an ensemble of analyses in the ECMWF Ensemble Prediction System. Quarterly Journal of the Royal Meteorological Society: A Journal of the Atmospheric Sciences, Applied Meteorology and Physical Oceanography, 134(637), 2051-2066.

Dasanto, B. D., Boer, R., Pramudya, B., & Suharnoto, Y. (2014). Evaluasi Curah Hujan TRMM Menggunakan Pendekatan Koreksi Bias Statistik. Jurnal Tanah Dan Iklim, 38(1), 15-24.

Dee, D. P. (2004). Variational bias correction of radiance data in the ECMWF system. Proceedings of the ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, Reading, UK, 28, 97-112.

Dippe, T., Greatbatch, R. J., & Ding, H. (2019). Seasonal prediction of equatorial Atlantic sea surface temperature using simple initialization and bias correction techniques. Atmospheric Science Letters, 20(5), e898.

Kollu, R., Rayapudi, S. R., Narasimham, S., & Pakkurthi, K. M. (2012). Mixture probability distribution functions to model wind speed distributions. International Journal of Energy and Environmental Engineering, 3(1), 27.

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, 12-18. https://doi.org/10.1088/1742-6596/1008/1/012018

Macias, D., Garcia-Gorriz, E., Dosio, A., Stips, A., & Keuler, K. (2018). Obtaining the correct sea surface temperature: Bias correction of regional climate model data for the Mediterranean Sea. Climate Dynamics, 51(3), 1095-1117.

McNally, A. P., Derber, J., Wu, W., & Katz, B. (2000). The use of TOVS level-1b radiances in the NCEP SSI analysis system. Quarterly Journal of the Royal Meteorological Society, 126(563), 689-724.

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

Nurdiati, S., Sopaheluwakan, A., & Najib, M. K. (2019). Statistical Bias Correction for Predictions of Indian Ocean Dipole Index With Quantile Mapping Approch. International MIPAnet Conference on Science and Mathematics (IMC-SciMath), Medan.

Nurdiati, S., Sopaheluwakan, A., & Pratama, Y. A. (2019). Statistical Bias Correction On Global Climate Model For El Nino Index Predictions. The 15th IMT-GT International Conference on Mathematics, Statistics, and their Applications (ICMSA), Bogor.

Ogutu, G. E., Franssen, W. H., Supit, I., Omondi, P., & Hutjes, R. W. (2017). Skill of ECMWF system-4 ensemble seasonal climate forecasts for East Africa. International Journal of Climatology, 37(5), 2734-2756.

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. https://doi.org/10.1007/s00704-009-0134-9

Saji, N., Goswami, B., Vinayachandran, P., & Yamagata, T. (1999). A dipole mode in the tropical Indian Ocean. Nature, 401(6751), 360-363.

Stockdale, T. N., Anderson, D. L., Balmaseda, M. A., Doblas-Reyes, F., Ferranti, L., Mogensen, K., Palmer, T. N., Molteni, F., & Vitart, F. (2011). ECMWF seasonal forecast system 3 and its prediction of sea surface temperature. Climate Dynamics, 37(3-4), 455-471.

Walpole, R. E. (1990). Pengantar statistika, edisi ke-3 (Introduction to statistics). PT Gramedia Pustaka Utama. https://books.google.co.id/books?id=hzwjcgAACAAJ




DOI: https://doi.org/10.34312/jgeosrev.v3i1.8259

Bookmark and Share


Copyright (c) 2021 Author

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.