Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations

Area of Science:

• Atmospheric Science
• Computational Fluid Dynamics
• Machine Learning

Background:

• Accurate weather information is crucial for disciplines like air traffic management.
• Sparse and imprecise ground weather station data challenge atmospheric state descriptions.
• Reconstructing detailed atmospheric conditions requires advanced data processing techniques.

Purpose of the Study:

• To apply physics-informed neural networks (PINNs) for generating high-quality weather information.
• To reconstruct dense and precise wind and pressure fields using limited local measurements.
• To leverage the Navier-Stokes equations for regularization and accurate atmospheric state computation.

Main Methods:

• Utilizing machine learning architectures, specifically PINNs, which embed physics models.
• Applying Navier-Stokes equations for regularization of weather data.
• Reconstructing wind and pressure fields in data-scarce regions.

Main Results:

• PINNs successfully reconstruct dense and precise wind and pressure fields.
• The model regularizes and corrects noisy weather station data.
• Accurate computation of wind and pressure in target areas is achieved.

Conclusions:

• PINNs offer a robust method for enhancing weather information quality.
• The study highlights the importance of tuning the neural network's loss function for optimal performance.
• Accurate reconstruction of fluid phenomena is dependent on spatial and temporal resolution.