Next Article in Journal
Ensemble Dispersion Simulation of a Point-Source Radioactive Aerosol Using Perturbed Meteorological Fields over Eastern Japan
Next Article in Special Issue
K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China
Previous Article in Journal
A New Methodology for Assessing the Interaction between the Mediterranean Olive Agro-Forest and the Atmospheric Surface Boundary Layer
Previous Article in Special Issue
A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network
Article

Survey on the Application of Deep Learning in Extreme Weather Prediction

1
Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
3
Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215325, China
4
Department of Computer, Texas Tech University, Lubbock, TX 79409, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Bin Wang and Ertug Ercin
Received: 3 April 2021 / Revised: 11 May 2021 / Accepted: 17 May 2021 / Published: 21 May 2021
Because of the uncertainty of weather and the complexity of atmospheric movement, extreme weather has always been an important and difficult meteorological problem. Extreme weather events can be called high-impact weather, the ‘extreme’ here means that the probability of occurrence is very small. Deep learning can automatically learn and train from a large number of sample data to obtain excellent feature expression, which effectively improves the performance of various machine learning tasks and is widely used in computer vision, natural language processing, and other fields. Based on the introduction of deep learning, this article makes a preliminary summary of the existing extreme weather prediction methods. These include the ability to use recurrent neural networks to predict weather phenomena and convolutional neural networks to predict the weather. They can automatically extract image features of extreme weather phenomena and predict the possibility of extreme weather somewhere by using a deep learning framework. View Full-Text
Keywords: deep learning; extreme weather prediction; recurrent neural network; convolutional neural network deep learning; extreme weather prediction; recurrent neural network; convolutional neural network
Show Figures

Figure 1

MDPI and ACS Style

Fang, W.; Xue, Q.; Shen, L.; Sheng, V.S. Survey on the Application of Deep Learning in Extreme Weather Prediction. Atmosphere 2021, 12, 661. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12060661

AMA Style

Fang W, Xue Q, Shen L, Sheng VS. Survey on the Application of Deep Learning in Extreme Weather Prediction. Atmosphere. 2021; 12(6):661. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12060661

Chicago/Turabian Style

Fang, Wei, Qiongying Xue, Liang Shen, and Victor S. Sheng 2021. "Survey on the Application of Deep Learning in Extreme Weather Prediction" Atmosphere 12, no. 6: 661. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12060661

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop