Typhoon Shanshan made landfall in the Kagoshima Prefecture of southern Japan on August 29th bringing considerable flooding and rainfall with it. Some areas broke rainfall records with over 300mm of rain in 24 hours. Early forecasts showed the potential for even more catastrophic outcomes with the storm forecast to make landfall as a powerful typhoon between Osaka and Tokyo, a highly populated area. However, the storm ended up making a sharp turn westward before turning north and making landfall (yellow track on map). The map below shows the forecast tracks for the ECMWF traditional and AI models initialized on August 23rd with the forecast strength of the storm indicated by text in each track point.
Weather models generally did a poor job forecasting this storm. One of the first models to pick up on the westward turn was the AI model run by ECMWF (blue track), one of the world leaders in weather prediction. In many ways this was a win for the AI models, they accurately forecast the track of a dangerous storm hours and days before traditional weather models (the ECMWF traditional model is the orange track). However, this success came with a massive caveat. The ECMWF AI model forecast the westward turn, but also forecast the storm would never rise above tropical storm strength, a known and recurring issue for AI weather models. In reality, the storm briefly reached category 4 strength, with sustained winds of 215km/h, before weakening to a category 1 storm prior to landfall. All together, Typhoon Shanshan presented an interesting case study in the promise and shortcomings of AI weather models.
