Ice Cloud Geometry Retrieval with Calibrated Uncertainty from Passive Satellite Imagery

We present a dense prediction system for ice cloud geom-etry retrieval from extremely sparse supervision, where active satellitesensors (EarthCARE radar/lidar) provide accurate but spatially narrowtraining labels (∼1.6% of pixels) and passive imagers (VIIRS, VisibleInfrared Imaging Radiometer Suite) observe the full globe. A ConvNex-tUNet trained on co-located tracks predicts eight targets at every pixelwith calibrated 90% prediction intervals using conformalized quantileregression (CQR). The best configuration, a five-member quantile en-semble, achieves R2= 0.742 with prediction quality constant regardlessof distance to the supervision track, confirming that the model learnsa per-pixel spectral retrieval rather than interpolating from nearby la-bels. Calibration is robust across latitudes and cloud types, with deepconvective clouds as the main failure mode. Deployed globally, the sys-tem produces 609M predictions from one day of VIIRS data in 2.4 hourson a single GPU, enabling dense 3D cloud characterization at planetaryscale.1