El nino index prediction model using quantile mapping approach on sea surface temperature data

Abstract

El Nino is a global climate phenomenon caused by the warming of sea surface temperatures in the eastern Pacific Ocean. El Nino has a powerful effect on the intensity of rainfall in several areas in Indonesia. El Nino impacts can be minimized by predicting the El Nino index from the sea surface temperature in the Nino 3.4 area. Therefore, many researchers have tried to predict sea surface temperature, and many prediction data are available, one of which is ECMWF. But, in reality, the ECMWF data still contains systematic errors or bias towards the observations. Consequently, El Nino predictions using ECMWF data are less accurate. For that reason, this study aims to correct the ECMWF data in the Nino 3.4 area using statistical bias correction with a quantile mapping approach. This method uses ECMWF data from 1983-2012 as training data and 2013-2018 as testing data. For this case, the results showed that 60% of El Nino's predictions on the testing data had improved the mean value. Also, all of El Nino's predictions on the testing data have improved the standard deviation value. Moreover, data testing's expected error can be corrected for all months in the 1st to 4th lead times. But, in the 5th to 7th lead times, only November-June can be corrected.

Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

“…Therefore, the dryness of drought indicators and the worse condition of hotspots are on the distribution joint's upper tail (right-top). However, we return the original data for visualization and analysis (Najib et al 2021b). The probability density functions of each distribution are given by (Hilbe 2011;Pobočíková et al 2017;Farooq et al 2018;Sylvi et al 2018;Baran et al 2021)…”

“…Moreover, the joint return period (in months) is defined by 1/𝑃𝑃 (Zscheischler and Fischer 2020). However, this joint probability and return period are uncertain depending on the sample size (Link et al 2020;Najib et al 2021b). We can calculate the 95% confidence interval of the joint probability (Link et al 2020)…”

“…Meanwhile, we use hotspots data as fire size. The ENSO conditions are not used to calculate the joint distribution, but we use them to reduce the sample size of data (Najib et al 2021b). Therefore, we obtained three joint distributions between climate conditions and fire size for one model.…”

“…where Δ𝑦𝑦 𝑖𝑖 is the length of the sub-interval [𝑦𝑦 𝑖𝑖−1 , 𝑦𝑦 𝑖𝑖 ] ( Nurdiati et al 2021b). Meanwhile, we can calculate the 95% confidence interval [𝑦𝑦 𝑎𝑎 , 𝑦𝑦 𝑏𝑏 ], by (Boubakar et al 2018)…”

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

“…Nino 3.4 region is selected because this region represents the average equatorial sea surface temperature across the Pacific, from the dateline to the South America coast (Trenberth, 2020). Also, Nino 3.4 region is widely used by researchers to observe the El Nino phenomenon (Nurdiati et al, 2021).…”