Forecasting daily extreme temperatures in Chinese representative cities using artificial intelligence models

Abstract

Accurate daily extreme temperature forecasts are important for public safety. However, traditional Numerical Weather Prediction (NWP) methods for temperature forecasting become increasingly costly and time-consuming as forecast resolution improves. To overcome these challenges, recent studies have embraced artificial intelligence (AI) techniques. In this paper, we select five AI models: Multiple linear regression, Support Vector Regressor, Gradient Boosting Regression Tree (GBRT), Long Short-Term Memory, and multilayer perceptron (MLP) to forecast extreme temperatures in nine representative Chinese cities, which are Harbin, Xian, Beijing, and Lhasa in the North and Shanghai, Guiyang, Guangzhou, Shenzhen, and Haikou in the South. To assess the performances, we compare AI models with two benchmarks: the persistence method by using the previous day's temperature extremes directly, and the prediction from the NWP method of the Global Forecast System (GFS) provided by the National Centers for Environmental Prediction. Among the 7 models forecasting daily extreme temperatures in nine cities, the MLP model outperforms the best, followed by the GBRT model. Compared with the persistence forecast, all 5 AI models and GFS show forecasting ability for daily maximum temperature. However, the GFS performs worse than the persistence model for daily minimum temperature. Specifically, for the MLP model, the average Mean Absolute Errors (MAEs) for forecasting daily maximum and minimum temperatures in the 9 cities are 1.61 °C and 1.14 °C, reducing the GFS model's MAEs for daily maximum and minimum temperatures forecasts by 15% and 45%, respectively. Overall, AI models show promise in enhancing temperature forecasting accuracy.

Keywords

• Daily extreme temperature
• Forecast
• Artificial intelligence
• Machine learning
• Deep learning
• Shapley value