Daily maximum temperature forecasting in changing climate using a hybrid of Multi-dimensional Complementary Ensemble Empirical Mode Decomposition and Radial Basis Function Neural Network

Abstract

Study regionMetropolitan Taipei city, Taiwan. Study focusThe severity of climate change in Taiwan is increasing, and the average temperature in Taiwan has increased by 1.0–1.4 °C over the last century, which is higher than the global average. A state-of-the-art hybrid of Multi-dimensional Complementary Ensemble Empirical Mode Decomposition (MCEEMD) and Radial Basis Function Neural Network (RBFNN) is developed for the analysis, simulation, and forecasting of extreme temperature events. The MCEEMD is first introduced to process non-stationary, nonlinear gridded climate data from 1960 to 2017. The RBFNN model is then coupled with the MCEEMD algorithm to forecast the 7-day daily maximum temperatures in Taipei. New hydrological insights for the regionLong-term time series data such as temperature exhibit non-stationarity, and non-linearity, thus making it more challenging to assessing the future trend and variability. It is shown that for daily maximum temperatures in Taipei, changes in extremes are more pronounced and impactful than changes in the mean values in response to global warming. The newly developed hybrid MCEEMD-RBFNN model is capable of analyzing the nonstationary and nonlinear daily maximum temperature data, and then forecasting the daily maximum temperature over the next 7 days. Short-term trends and variability of daily maximum temperature in Taipei can thus be identified and diagnosed for hazard prevention and risk assessment of heatwave events.