Radical Reads

Solving the Climate Crisis with AI: a Q&A with Max Evans of ClimateAI


Image: Headshots from Radical Ventures (Jeff Beardall) and ClimateAi 

ClimateAi is a Radical Ventures portfolio company that has created an AI platform uniquely able to predict weather and climate from 2 weeks to 10+ years in the future. These predictions are made at a localized level enabling businesses to adapt to weather changes and long-term climate shifts. I recently sat down with Max and students in the Master of Science in Applied Computing program at the University of Toronto to discuss research breakthroughs made by ClimateAi, and how AI is helping solve climate challenges. I’ve summarized some of his key takeaways from the talk.

Why start a business applying AI to climate change?

ClimateAi’s driving vision is building climate resilience in our supply chains. The founders started with an interest in understanding how climate change affects assets around the world. Most decision-makers today are using basic weather information based on simple historical extrapolation and coarse signals to make consequential business decisions. A product powered by better data, combined with cutting-edge ML, can dramatically improve the decision making capabilities of business leaders.

How does AI help make climate information more accessible to decision-makers? 

ClimateAi’s platform makes insights from our AI models available to businesses that need to make long-term strategic decisions based on climate predictions or granular day-to-day operations decisions based on accurate weather forecasts. For example, ClimateAi’s platform is guiding agriculture companies in planting and harvesting decisions, pinpointing when a seed will germinate and visualizing the process with a software product. We are also capable of going beyond decision-support, capturing novel applications for AI in the climate space through in-house toolsets, and building infrastructure support for data and climate science teams.

What are the recent research breakthroughs underpinning ClimateAi’s models? 

ClimateAi has successfully used transfer learning and generative adversarial networks (GANs) to improve weather and climate predictions. Transfer learning makes use of very coarse, low resolution, and sparse datasets by pre-training a neural network on a rich dataset and ‘transferring’ the learning. This method has been used successfully in other disciplines such as self-driving and video games. ClimateAi used a richer data set generated by physical simulations to train a model on the fundamental physics needed to make predictions on Earth observation data such as satellite images. This is new science. Appropriate satellite measurements have only been available for the last ten years and global temperature and precipitation measurements only for the last few decades but we need to predict changes to the weather a few decades out.

ClimateAi researchers have also demonstrated that GANs trained on global weather forecasts can correct for the biases in existing weather models. The new model downscales global forecasts to be as accurate as a local forecast, without requiring the vast amounts of computational, financial, and human resources previously needed for capturing data at such a small scale.

What is the future for the climate and data science field and those interested in contributing to further innovation? 

We are still in the early days of applying AI to climate research. Core research questions remain on the foundations of forecasting, downscaling, bias correction, signal processing, determining climate change signals, seasonal signals, and what is ultimately “normal” weather and climate. There is plenty of room for young data scientists to work and push the envelope. As the field continues to grow, specializations are extending beyond natural language processing and image recognition as new types of data such as spatiotemporal data need to be managed with new methods.

ClimateAi was built around making data more accessible to decision-makers and continues to be a pioneer in the climate intelligence space.

Listen to the full conversation.

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Radical Reads is edited by Leah Morris (Senior Director, Velocity Program, Radical Ventures).