Physical informed neural network improving the WRF-CHEM results of air pollution using satellite-based remote sensing data

Keyphrases

• Remote Sensing Data 100%
• Air Pollution 100%
• WRF-Chem 100%
• Satellite Remote Sensing 100%
• Physical Informed Neural Networks 100%
• Deep Learning Model 50%
• Gas Concentration 50%
• Model Use 25%
• Spatial Resolution 25%
• Eastern China 25%
• Data-centric 25%
• Pollution Episodes 25%
• Measurement Accuracy 25%
• Forecasting Model 25%
• Observational Data 25%
• Satellite Observations 25%
• Pearson Correlation Coefficient 25%
• Weather Research 25%
• Weather Forecasting 25%
• Satellite Data 25%
• Central China 25%
• Atmospheric Pollution 25%
• Air Quality Management 25%
• Monitoring Data 25%
• Ground-based Observations 25%
• Cloud Coverage 25%
• Atmospheric Chemical Transport Model 25%
• Pollutant Gases 25%
• Multi-source Observations 25%
• Spatial Distribution of Pollutants 25%
• Pollutant Distribution 25%
• Space-time Distribution 25%
• MAX-DOAS 25%

Earth and Planetary Sciences

• Air Pollution 100%
• Remote Sensing 100%
• China 50%
• Spatial Distribution 50%
• Atmospherics 50%
• Satellite Observation 50%
• Nitrogen Dioxide 50%
• Monitoring Data 50%
• Spatial Resolution 50%
• Satellite Data 50%
• Chemical Transport 50%
• Air Quality Management 50%
• Satellite Remote Sensing 50%