This project develops an AI framework to enhance Earth and Planetary Science mission returns by combining the global coverage of passive polarimeter data with lidar's high-fidelity vertical profiling. The approach leverages PACE (SPEXone and HARP2 polarimeters) and EarthCARE (ATLID lidar), collocated to generate 100,000+ paired profiles by December 2025.
Using PyTorch, neural networks trained on these datasets will enable PACE polarimeters to detect clouds, retrieve cloud top height (<1 km uncertainty), and profile aerosol vertical distribution (CALIOP-like uncertainty at 1 km resolution), reducing dependence on model assumptions and calibration errors.
Deliverables include cloud and aerosol products with quantified uncertainties, results for the PACE Software Operations Team, and open-source AI code.
By fusing polarimetry's microphysical richness with lidar's vertical precision, this work maximizes science return from existing assets and builds a 3D cloud/aerosol validation framework for next-generation AI weather forecasting models.