Earthmover Highlights Climate Data Platform Role in Advanced Forecasting Webinar

According to a recent LinkedIn post from Earthmover, the company recently featured a webinar showcasing two distinct climate-forecasting approaches spanning timelines from one week to several decades. The post highlights Planette AI’s hybrid physics-and-AI model, which is described as delivering roughly double the forecasting skill of the CFSv2 system for wind at a one‑month lead, with daily updates on a 25 km grid.

The post also describes work from Columbia Climate School on the Bias-Corrected Downscaled Massive Ensemble, a ~1,400‑member set of climate projections at quarter‑degree resolution extending to 2100. This dataset, reportedly totaling about 100 TB, is positioned in the post as an example of how Earthmover’s cloud-native data layer and Marketplace can make large-scale scientific climate data more accessible to researchers and AI developers.

For investors, the content suggests that Earthmover is aiming to position itself as critical infrastructure for scientific AI and climate analytics, enabling both short‑ and long‑term forecasting applications. If adopted by enterprises in sectors such as energy, insurance, and infrastructure planning, this capability could support future revenue growth through data access, platform usage, and marketplace transactions.

The emphasis on interoperability with very large datasets may also indicate a strategy focused on technical differentiation versus traditional data vendors and legacy modeling pipelines. As climate risk and energy transition themes gain prominence in capital markets, Earthmover’s role in facilitating advanced climate modeling could strengthen its competitive standing and support valuation narratives tied to the broader climate‑tech and AI infrastructure segments.