Tropical Cyclone Wind Speed Estimation: A Large Scale Training Data Set and Community Benchmarking

Abstract

Abstract Tropical cyclones (TCs) cause significant disruptions to infrastructure and livelihood. The scale of loss due to TCs may be mitigated by prompt and accurate advisories about TC wind speed. Current advisories are consensus based and have a time delay of about 6 hr between each new update. As part of efforts to increase the frequency of wind speed estimations without compromising on the accuracy, we curate a high‐quality hurricane image and wind speed data set and develop a machine learning (ML) model for frequent wind speed estimation. The image and wind speed pairings are consolidated from different sources. The wind speeds are interpolated hourly to curate the TC wind speed estimation data set. The data set is supplemented with metadata that eases adoption of the data set by the ML community. We have also designed a competition to garner interest and encourage community involvement to build state‐of‐the‐art ML models to estimate wind speeds from satellite images of TCs. The competition winners were able to improve the wind speed estimation by almost 50% over the benchmark model.

Keywords

• training data set
• machine learning
• data science competition
• hurricane
• community involvement