201. Extended-Range Forecast of Regional Persistent Extreme Cold Events Based on Deep Learning.
- Author
-
Wu, Weichen, Wang, Yaqiang, Wei, Fengying, Liu, Boqi, and You, Xiaoxiong
- Subjects
- *
DEEP learning , *CONVOLUTIONAL neural networks , *HILBERT-Huang transform , *ORTHOGONAL decompositions , *ORTHOGONAL functions , *WAVELET transforms - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF