Weather Lab users can explore and compare predictions from various AI and physics-based models. Read together, these forecasts can help weather agencies and emergency services experts more accurately predict a cyclone’s path and strength. This will help experts and decision-makers better prepare for different scenarios, share news about the risks involved, and support decision-making to manage the impact of cyclones.
It’s important to emphasize that Weather Lab is a research tool. The live predictions shown are generated by a developing model and are not official alerts. Keep this in mind when using tools, including when supporting decision-making based on forecasts generated by Weather Lab. For official weather forecasts and warnings, contact your local weather agency or the National Weather Service.
Cyclone prediction using AI
Physics-based cyclone forecasting requires approximations to meet operational demands, making it difficult for a single model to successfully predict both a cyclone’s path and its intensity. This is because the cyclone’s path is governed by vast atmospheric steering currents, whereas the cyclone’s strength depends on complex turbulent processes in and around its compact core. Global, low-resolution models perform best in predicting cyclone paths, but regional high-resolution models are needed because they cannot capture the fine-scale processes that determine cyclone intensity.
Our experimental cyclone model is a single system that overcomes this trade-off, with internal evaluations showing state-of-the-art accuracy for both cyclone track and intensity. It is trained to model two different types of data. One is a massive reanalysis dataset that reconstructs past weather across the globe from millions of observations, and the other is a specialized database containing critical information on the tracks, strength, size, and wind radius of approximately 5,000 cyclones observed over the past 45 years.
By modeling analytical data and cyclone data together, the ability to predict cyclones is greatly improved. For example, an initial evaluation of NHC-observed hurricane data in the North Atlantic and East Pacific basins during test years 2023 and 2024 shows that our model’s 5-day cyclone track forecasts are on average 140 km closer to true cyclone locations than ECMWF’s primary global physics-based ensemble model, ENS. This is comparable to the accuracy of ENS’s 3.5-day forecast. A prediction that would normally take more than 10 years to achieve was improved by 1.5 days.
While previous AI weather models have struggled to calculate cyclone intensity, our experimental cyclone model outperformed the average intensity error of the National Oceanic and Atmospheric Administration’s (NOAA) Hurricane Analysis and Forecasting System (HAFS), a high-resolution physically-based model for key regions. Preliminary tests also show that the model’s size and wind radius predictions are comparable to physically-based baselines.
Here we visualize track and intensity prediction errors and present an evaluation of the average performance of experimental cyclone models up to 5 days in advance compared to ENS and HAFS.
