Climate risk analytics: AI for better climate predictions

This Insight was last updated in July 2022; for an up-to-date market map and the latest funding information, please refer the Commercial Drone Tech industry hub

Advancements in AI and data sciences have made it possible to identify more granular and hyperlocal patterns in weather data, enabling more accurate and longer-term climate risk forecasts, for up to 80 years into the future in some cases. This is against a backdrop where even a seven-day legacy forecast is considered only 50% accurate.

The industry remains a hotspot, having raised close to a billion dollars in the last three years (more than 70% of total industry funding). A handful of startups have also already established their foothold in the market: Tomorrow.io provides a five-day advanced adverse weather warning solution for 20+ industries, Jupiter Intelligence claims to predict natural disasters from one hour to 50+ years in advance, while One Concern is building a digital twin of the Earth to predict climate and weather events.

In this Edge Insight, we deep dive into the latest developments in climate risk analytics as the industry attempts to outperform legacy numerical weather prediction (NWP) models with next-generation AI-driven predictive analytics tools, among other technologies.

What is climate risk analytics?

What are climate risks? Climate risks refer to probable socio-economic impacts of climate change. There are two types of climate risks: 1) physical and 2) transitional. Physical risks arise from adverse changes in weather and climate. These include short-term and one-off extreme weather events like floods, cyclones, droughts, and wildfires, as well as long-term and ongoing climate impacts such as global warming. Transitional risks arise from moving into a low-carbon future, which include policy and regulatory risks, technological risks, competitive risks, and economic losses from shifting to sustainable technologies. The focus of this report is the former.

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