Four ways Google Research scientists have been using Empirical Research Assistance

Climate and sustainability: Using weather satellites to monitor CO₂

Regular observations of carbon dioxide (CO₂) began at Hawaii’s Mauna Loa Observatory in the late 1950s, yielding the iconic Keeling Curve that documents rising global CO₂ concentrations in Earth’s atmosphere. Mapping human greenhouse gas emissions and understanding how plants, trees, soils and oceans absorb those emissions requires us to track how CO₂ varies across regions and over time. Current space-based CO₂ sensors, like NASA’s Orbiting Carbon Observatory-2 (OCO-2) were designed to make high-precision observations, but they only map a tiny fraction of the Earth’s surface and return to each location just once every 16 days. Geostationary satellites, such as the GOES East satellite designed to support weather forecasting, orbit the Earth from a much higher altitude and can scan an entire hemisphere every 10 minutes. However, none of the existing geostationary satellites were designed to map CO₂.

Google researchers used ERA to develop a single-pixel, physics-guided neural network to distill a column-averaged CO₂ signal from the existing GOES East observations. To do so, the model combines data from 16 wavelength bands from GOES-East with lower-troposphere meteorology, solar angles, and day of the year. After training on the sparse observations from OCO-2 and OCO-3, the model was then able to derive estimates of column-averaged CO₂ everywhere and every 10 minutes.

Research shared at the International Workshop on Greenhouse Gas Measurements from Space shows that the AI-developed model is able to leverage the high spatial and temporal density of the GOES East observations to track column-averaged CO₂ with unprecedented spatial and temporal resolution. Comparisons against independent data from additional years of OCO-2 observations, and from the ground-based total column carbon observing network, confirm the model’s ability to capture real CO₂ variability.

These results show how an AI algorithm can extract additional value from existing observational instruments, especially for resource-intensive satellite research missions. This project is among several questions related to climate and greenhouse gases that Google researchers are exploring using ERA.