Journal Article

Extended-Range Forecast of Regional Persistent Extreme Cold Events Based on Deep Learning

Wu, Weichen, Wang, Yaqiang, Wei, Fengying, You, Xiaoxiong, Liu, Boqi

2023

Overview

Regional persistent extreme cold events are meteorological disasters that cause serious harm to people’s lives and production; however, they are very difficult to predict. Low-temperature weather systems and their effects have a significant low-frequency oscillation period (10–20 d and 30–60 d). This paper uses deep learning to analyze the extended-range time scale and predict regional persistent extreme cold events. The dominant low-frequency oscillation components of cold events are obtained via wavelet transform and Butterworth filtering. The low-frequency oscillation component is decomposed via empirical orthogonal function decomposition to extract the main spatial mode and time coefficient. A convolutional neural network is used to establish the correlation between large-scale circulations and the time coefficient of the low-frequency oscillation component of the lowest temperature. The proposed deep learning model exhibits good prediction accuracy for regional persistent extreme cold events with low-frequency oscillations.

• Publisher: MDPI AG
• Subject: Artificial neural networks / Cold weather / Decomposition / Deep learning / Disasters / Empirical analysis / Extended forecasting / Extreme cold / Extreme low temperatures / Extreme weather / Low temperature / low-frequency oscillation / Neural networks / Orthogonal functions / Oscillations / Precipitation / regional persistent extreme cold events / Temperature / Wavelet transforms / Weather forecasting