Wind-dependent Variables: Predict Wind Speeds of Tropical Storms Hosted By Radiant Earth Foundation


Woohoo! This competition has come to a close!

Many thanks to the participants for all of their hard work and commitment to using data for good!

Predict Wind Speeds of Tropical Storms

Tropical cyclones have become more destructive in the last decades as we experience a warmer climate. These winning solutions help estimate the potential consequences of storms and provide near real-time insight into these phenomena, saving lives and reducing damages.

— Hamed Alemohammad, Executive Director and Chief Data Scientist at Radiant Earth Foundation


Hurricanes can cause upwards of 1,000 deaths and $50 billion in damages in a single event, and have been responsible for well over 160,000 deaths globally in recent history. During a tropical cyclone, humanitarian response efforts hinge on accurate risk approximation models that depend on wind speed measurements at different points in time throughout a storm’s life cycle.

For several decades, forecasters have relied on visual pattern recognition of complex cloud features in visible and infrared imagery. While the longevity of this technique indicates the strong relationship between spatial patterns and cyclone intensity, visual inspection is manual, subjective, and often leads to inconsistent estimates between even well-trained analysts.

The Solution

There is a vital need to develop automated, objective, and accurate tropical cyclone intensity estimation tools from satellite image data. In 2018, the NASA IMPACT team launched an experimental framework to investigate the applicability of deep learning-based models for estimating wind speeds in near-real time.

The goal of the Wind-dependent Variables challenge was to improve on this model using satellite images captured throughout a storm’s life cycle. To build their solutions, participants drew on a dataset of single-band satellite images and wind speed annotations from over 600 storms prepared by the NASA IMPACT team and Radiant Earth Foundation.

The Results

Over 700 participants stepped up to this important challenge, generating more than 2,700 entries. Each of the top three models achieved at least a 50% reduction in Root Mean Square Error (RMSE) as compared to the existing model!

RMSE graph

Winning solutions were able to take advantage of the relative timing of images in a storm sequence to produce targeted wind speed estimates based on temporal trends. As a result, these solutions can help to improve disaster readiness and response efforts around the world by equipping response teams with more accurate and timely wind speed measurements. All of the prize-winning solutions from this competition are linked below and made available for anyone to use and learn from.