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Deep Neural Networks for Weather Forecasting


Affiliations
1 Faculty of Computers & Artificial Intelligence, Helwan University, Cairo, Egypt
2 Faculty of engineering, Banha university, Egypt
 

Numerical Weather Prediction focuses on taking current observations of weather and processing these data with computer models to forecast the future state of weather and is used to produce shortand medium-range weather forecasts from 10-15 days of the state of the atmosphere. A weather satellite is a type of satellite that is primarily used to monitor the weather and climate of the Earth. Electromagnetic radiation is energy emitted by all matter above absolute zero temperature ex., visible light, infrared light, heat, microwaves, and radio and television waves and Electromagnetic radiation is absorbed mainly by several gases in the Earth's atmosphere, among the most important being water vapor, carbon dioxide, and ozone. The paper proposed methodology involves training deep neural networks to take reanalysis weather data at a given point in time as input, and then produce reanalysis weather data at a future point in time as output. We used a single point in time for both input and output

Keywords

weather prediction, computer models ,Electromagnetic radiation
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  • Deep Neural Networks for Weather Forecasting

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Authors

Naglaa Saeed Shehata
Faculty of Computers & Artificial Intelligence, Helwan University, Cairo, Egypt
Mohamed Saeed Shehata
Faculty of engineering, Banha university, Egypt

Abstract


Numerical Weather Prediction focuses on taking current observations of weather and processing these data with computer models to forecast the future state of weather and is used to produce shortand medium-range weather forecasts from 10-15 days of the state of the atmosphere. A weather satellite is a type of satellite that is primarily used to monitor the weather and climate of the Earth. Electromagnetic radiation is energy emitted by all matter above absolute zero temperature ex., visible light, infrared light, heat, microwaves, and radio and television waves and Electromagnetic radiation is absorbed mainly by several gases in the Earth's atmosphere, among the most important being water vapor, carbon dioxide, and ozone. The paper proposed methodology involves training deep neural networks to take reanalysis weather data at a given point in time as input, and then produce reanalysis weather data at a future point in time as output. We used a single point in time for both input and output

Keywords


weather prediction, computer models ,Electromagnetic radiation

References