Satellite remote sensing is the primary source of global aerosol observations, providing essential data for understanding aerosol-climate interactions and constraining global climate models. To solve the inverse problem at the heart of the retrieval process, traditional algorithms must make simplifications and often cannot quantify uncertainty. In this study, we explore the use of invertible neural networks (INNs) for retrieving aerosol optical depth (AOD) from spectral top-of-atmosphere (TOA) reflectance. INNs can handle the inherent uncertainty of underdetermined inverse problems. They model the forward and inverse processes simultaneously while learning additional random latent variables used to recover full nonparametric posterior distributions for the inverse predictions. We develop location-specific INNs for MODIS sensor data, training on synthetic datasets generated by combining atmospheric reflectance from MODIS dark target (DT) lookup tables (LUTs) and surface reflectance from a MODIS bidirectional reflectance product. The INNs successfully emulate the forward problem and achieve accurate AOD inversion results on synthetic test sets (RMSE$\approx ~0.05$). The posterior distributions obtained are reliable (mean absolute calibration error (MACE)$\approx ~2.5$%), efficiently providing informative predictive uncertainty estimates. In addition, the INNs’ invertible architecture is found to promote physically consistent predictions and uncertainties. To further validate them in a real-world context, the INNs are applied to MODIS L1B reflectance observations to produce full-resolution AOD estimates with pixel-level uncertainties. The retrievals are compared to collocated ground measurements from the Aeronet network. The INNs obtain good accuracy in all tested locations in line with the operational DT AOD product (RMSE$\approx ~0.1$, 74% within DT expected error (EE) bounds). The INNs are also able to retrieve AOD over bright surfaces where DT cannot be applied. Despite uncovered limitations out-of-distribution, the INNs show consistent skill in target domains across diverse land surfaces. The INNs’ unique modeling and uncertainty quantification features have the potential to enhance aerosol and climate studies in various real-world contexts.

Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses on designing neural network architectures and regularization functions to allow for scalable estimation of average and individual-level dose-response curves from high-dimensional, large-sample data. Such methodologies assume ignorability (observation of all confounding variables) and positivity (observation of all treatment levels for every covariate value describing a set of units), assumptions problematic in the continuous treatment regime. Scalable sensitivity and uncertainty analyses to understand the ignorance induced in causal estimates when these assumptions are relaxed are less studied. Here, we develop a continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds that agree with the observed data and a researcher-defined level of hidden confounding. We introduce a scalable algorithm and uncertainty-aware deep models to derive and estimate these bounds for high-dimensional, large-sample observational data. We work in concert with climate scientists interested in the climatological impacts of human emissions on cloud properties using satellite observations from the past 15 years. This problem is known to be complicated by many unobserved confounders.