Clouds present the most pernicious remaining unknown in our climate forecasts. This is because processes at an incredible range of scales—from microscopic molecular interactions to planet-wide systems—govern how clouds form and evolve. Existing active sensors measure microscopic properties, whilst passive microwave tells us about mesoscale structures, but there is a paucity of observational constraints at intermediate scales.
Clouds Decoded, funded by the Advanced Research + Invention Agency, considers what high resolution satellites like Sentinel-2 and Landsat can tell us about clouds. For these sensors—designed to study processes on the Earth’s surface—clouds are currently viewed purely as an obstacle. Petabytes of cloudy data are left to gather dust in the archives. But we see hidden value in these forgotten images.
Building on our team’s expertise in cloud masking and removal for multispectral sensors, we are filling the observational gap by predicting multiple physical cloud properties at an unprecedented resolution. Deployed on petabytes of rarely used images, our models will produce measurements—outlined below—to be leveraged in collaboration with our network of academic partners to better understand cloud processes like turbulent updrafts, and ice-water phase heterogeneity, which are best modeled at the scales we target.
You can visualise some of the planned measurements by hovering over the image.
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