structural:expansion, structural:reformatting
Models

Models can provide spatial and temporal information about airflow. Spatial information can be obtained through the interpolation of data from various measurement stations, allowing for horizontal data calculation. Alternatively, profiles, such as the logarithmic wind profile, can be utilized to derive vertical information.

Temporal information is typically computed by solving the Navier-Stokes equations within numerical weather prediction models, generating global data for General Circulation Models or specific regional data. The calculation of wind fields is influenced by factors such as radiation differentials, Earth's rotation, and friction, among others. Solving the Navier-Stokes equations is a time-consuming numerical process, but machine learning techniques can help expedite computation time.

Numerical weather prediction models have significantly advanced our understanding of atmospheric dynamics and have become indispensable tools in weather forecasting and climate research. By leveraging both spatial and temporal data, these models enable scientists to analyze and predict global and regional wind patterns, contributing to our comprehension of the Earth's complex atmospheric system.