Weather Data

Weather data analysis focuses on improving the accuracy and resolution of weather forecasts using advanced computational methods. Current research emphasizes the development and application of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and diffusion models, often incorporating multimodal data sources like satellite imagery and weather station readings to enhance prediction capabilities. These advancements are crucial for various sectors, improving disaster preparedness, optimizing energy production (e.g., solar power), and supporting agricultural practices through more precise and timely predictions. Furthermore, research is actively addressing challenges like data cleaning, handling data imbalances, and improving the efficiency of model training and deployment.