Based Weather

AI-based weather forecasting is rapidly advancing, aiming to improve the accuracy, speed, and accessibility of weather predictions. Current research focuses on developing and refining AI models, including transformer networks and convolutional neural networks, often incorporating elements of data assimilation techniques like ensemble Kalman filters and 4DVar to enhance forecast skill and address biases. These advancements offer the potential for more accurate and timely forecasts of various weather phenomena, including tropical cyclones and severe convective storms, ultimately improving weather-related risk management and societal preparedness. Furthermore, efforts are underway to create more efficient data handling and model deployment strategies to broaden access to these powerful tools.