Introduction

Automatic learning is revolutionizing many industries, including meteorological forecasting. The ability to use generatives to predict the weather can help provide more accurate and precise forecasts.

Technical Details

The SEEDS model is based on conditional diffusion, a type of generator that uses an input signature to create an output similar one. The model was developed using the PyTorch framework and achieved results similar to operational forecasts without the need for enormous resources.

Practical Implications

The ability to use generatives for meteorological forecasting can help provide more accurate and precise forecasts. In addition, the SEEDS model can be used to provide forecasts for extreme events like hurricanes or earthquakes.