Inigo Seasonal Prediction and Analysis of RisK: I-SPARK

AI weather forecasting is in the news again this week, with Google DeepMind’s release of GenCast. AI weather models have made rapid progress for forecasts up to 15 days in advance, but less progress has been made on the notoriously difficult problem of seasonal predictions. Accurate seasonal predictions would provide an opportunity for society to prepare well in advance of hazardous conditions.  Here at Inigo we’ve spent the last few months refining our strategy for seasonal hurricane prediction. A major part of that work has been creating I-SPARK (Inigo Seasonal Prediction and Analysis of RisK), a new AI tool to help make accurate seasonal hurricane predictions at insurance relevant lead times of three to nine months.

I-SPARK uses freely available sea surface temperature forecasts to predict the level of risk for the upcoming hurricane season. The figure shows some of the sea surface temperature regions that the model will be using to make its December initiated forecast in a few weeks time. Interestingly, our model disregards most ENSO information until after spring due to low predictability. As the figure inset shows, the model skill rapidly increases by February, but then falls off in spring. This gives us confidence that useful hurricane risk information can be derived as early as February, but we need to do more work to understand why the model struggles in spring.

This work is being presented at the American Geophysical Union conference in early December, 2024. You can check out the full conference poster and additional details on GitHub. Please reach out if you want to learn more.