CERBERUS: A Three-Headed Decoder for Vertical Cloud Profiles

TL;DR

CERBERUS is a probabilistic framework that generates detailed vertical cloud profiles from satellite data, improving understanding of cloud microphysics and structures in climate models.

Contribution

It introduces a three-headed encoder-decoder architecture for predicting zero-inflated vertical radar reflectivity distributions from satellite and meteorological data.

Findings

Recovers coherent cloud structures across regimes

Generalizes well to unseen test periods

Provides uncertainty estimates reflecting physical ambiguity

Abstract

Atmospheric clouds exhibit complex three-dimensional structure and microphysical details that are poorly constrained by the predominantly two-dimensional satellite observations available at global scales. This mismatch complicates data-driven learning and evaluation of cloud processes in weather and climate models, contributing to ongoing uncertainty in atmospheric physics. We introduce CERBERUS, a probabilistic inference framework for generating vertical radar reflectivity profiles from geostationary satellite brightness temperatures, near-surface meteorological variables, and temporal context. CERBERUS employs a three-headed encoder-decoder architecture to predict a zero-inflated (ZI) vertically-resolved distribution of radar reflectivity. Trained and evaluated using ground-based Ka-band radar observations at the ARM Southern Great Plains site, CERBERUS recovers coherent structures across cloud regimes, generalizes to withheld test periods, and provides uncertainty estimates that reflect physical ambiguity, particularly in multilayer and dynamically complex clouds. These results demonstrate the value of distribution-based learning targets for bridging observational scales, introducing a path toward model-relevant synthetic observations of clouds.