Earthmover Highlights Role in Scaling Access to Advanced Climate Forecasting Data

According to a recent LinkedIn post from Earthmover, the company recently featured its cloud-native data platform in a webinar focused on climate and weather forecasting across very different time horizons. The post highlights presentations from Planette AI on sub-seasonal to seasonal wind forecasting and from Columbia Climate School on multi-decadal climate projections.

The LinkedIn post describes Planette AI’s hybrid physics and AI approach, which reportedly delivers roughly twice the skill of the CFSv2 model for one-month-ahead wind forecasts on a 25 km grid. This capability is positioned as valuable for users needing higher-precision, short- to medium-term atmospheric insights, which may appeal to energy, commodities, and risk-management customers.

For longer horizons, the post points to Columbia Climate School’s Bias-Corrected Downscaled Massive Ensemble, a data resource of about 100 TB covering ~1,400 climate projections at quarter-degree resolution through 2100. The ensemble is portrayed as enabling users to understand how sensitive results are to different historical reanalyses, which can influence long-range planning and climate risk assessment.

Earthmover’s LinkedIn post emphasizes that datasets at this scale have been historically difficult for smaller teams to exploit, and suggests its cloud-native data layer aims to remove those infrastructure barriers. By positioning its Earthmover Marketplace as a way to foreground science while managing the data-heavy backend, the company appears to be targeting researchers, data scientists, and institutional users who require scalable access to large climate datasets.

For investors, the content suggests Earthmover is trying to build an ecosystem around high-value scientific and climate AI workloads rather than just offering raw storage or compute. If the platform gains adoption among climate researchers, energy firms, insurers, and other data-intensive users, it could support recurring usage-based revenue and strengthen Earthmover’s position within the growing climate and scientific AI infrastructure market.