Development of Digital Twin Technologies for Climate Projections

Climate projections are increasingly needed for adaptation, climate resilience and related decision making. However, existing projections have systematic biases, are limited in scope, and are not readily available for most potential users. While the ideal of an observational data-driven digital twin for climate is initially attractive, there is only a very limited set of climate data available with which to train such a tool. Nonetheless, there are a number of digital twin technologies that can be used to enhance the development of climate models, the weighting of model projections, the scope of climate projection scenarios, and the ability to extract useful data from those projections. This white paper proposes to tackle four specific examples that use a variety of Machine Learning (ML) techniques in cases where digital twin technology is likely to make a difference.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

This is early/mid-stage (TRL 3) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.