Creating ML benchmarks for climate problems

Proposals should specifically cover one or more of the following topics:

• Greenhouse gas (GHG) monitoring: Sustained, near-real-time tracking of GHG emissions and sequestration, leveraging both "top-down" (atmospheric observations) and "bottom-up" (land surface, activity-based) approaches.

• Biomass estimation: Accurate above- and below-ground biomass assessments to quantify carbon storage in forests and other ecosystems.

• Crop yield prediction and leakage estimation: ML-driven models for anticipating crop yields, identifying and quantifying potential leakage from agricultural practices (e.g., land-use change, soil carbon loss) and optimizing carbon sequestration and storage strategies.

• Climate model scoring: Enhanced benchmarks to evaluate and refine climate models, increasing their accuracy and their ability to inform decision-making.

Good benchmarks allow AI researchers to make significant contributions to domains in which they may have little expertise. Consider WeatherBench, which catalyzed recent breakthroughs in AI for global weather prediction. We believe a number of factors contributed to WeatherBench’s success and may be useful design principles for ML benchmarks for climate problems:

1. Includes an associated, self-contained dataset (i.e., a coarsened version of the ERA5 reanalysis) for evaluating and building models.
2. Includes a set of top-line metrics (i.e., root mean squared error for 850 mbar temperature and 500 mbar height) that are accepted as meaningful by the scientific community and interpretable by non-experts.
3. Includes multiple baselines and points of comparisons for measuring progress, including clear thresholds to indicate runaway success (i.e., improving upon the best physics-based weather forecasts).
4. Includes example code for data preparation and evaluation.
5. Addresses an important and unsolved problem.

We aim to support projects that address these critical areas by:

• Developing benchmarks and datasets: Creating standardized ML benchmarks, curating data libraries, and potentially fueling Kaggle competitions to ignite innovation.

• Collecting transformative data: Facilitating the acquisition of key datasets linked to GHG flux and concentration tracking, above/below-ground carbon, wildfire emissions, and other factors observable from remote sensing.

• Advancing ML models for climate MRV: Developing models that leverage new or existing data sources (satellite, IoT, etc.) to improve carbon accounting accuracy and reduce uncertainty.

• Unlocking new insights from existing instruments: Extracting novel GHG-relevant information from both spaceborne and in-situ instruments.

We believe ML benchmarks hold the key to accelerating the development of nature-based solutions, supporting carbon markets, and informing climate adaptation strategies. Let's collaborate to build a more sustainable future, powered by data and innovation!

Award amounts vary by topic up to $300K USD, and are intended to support the advancement of the professor’s research during the academic year in which the award is provided. Additional cloud credits can be requested.

Funds will be disbursed to the institution under an agreement stating the funding cannot be used for overhead or indirect costs. The agreement will also require that research results, including any code, models, or other intellectual property, be made publicly available under open source licenses, unless otherwise agreed upon in a separate contract between Google and the recipient.

Listed on GARA landing page.