Advancing RemoTAP: A Deep Learning Framework for Predictive Dynamic Quality Assessment in Multi-Angle Polarimetry

The accurate retrieval of aerosol properties from space is a cornerstone of our ability to quantify climate forcing and monitor global air quality. Multi-angle polarimeters (MAPs), such as PARASOL-POLDER and the recently launched PACE-SPEXone and Metop-SG-3MI, offer unprecedented information content, disentangling complex aerosol microphysics from surface scattering. However, the efficacy of retrieval algorithms, such as RemoTAP, is often constrained by post-processing quality controls. Traditional methods rely on static goodness-of-fit thresholds or "one-size-fits-all" post-processing filters (e.g., Χ2 < 5), which enforce a rigid trade-off between data coverage and accuracy. Our recent analysis reveals that such static thresholds often fail to account for systematic biases over complex surfaces, leading to unnecessary data loss or effectively allowing high-error retrievals to silently contaminate climate records. In this work, we present a paradigm shift in quality assessment: a Predictive Dynamic Quality Filter powered by a Physics-Aware Deep Learning Framework. Unlike generic "black-box" approaches, our architecture is designed to explicitly decouple the competing influence of atmospheric state variables from complex surface reflectance signatures. By processing these distinct physical signals alongside a rich set of spectral multi-directional total and polarized reflection signatures of the surface, the model dynamically constructs a pixel-level error profile that adapts to the underlying scene, robustly handling diverse conditions ranging from bright surfaces to the intricate directional reflectance of heterogenous vegetation. This Surface-Aware framework effectively learns to identify the "trustworthiness" of a retrieval based on its physical context, rather than a fixed goodness-of-fit cost. Here we present results applying this framework to POLDER RemoTAP retrievals. To ensure robust generalization and address potential overfitting, we employed a rigorous validation strategy using a comprehensive dataset from 477 global AERONET sites spanning four years (2006-2009). The model was trained on a strategically stratified subset of these observations while its performance was evaluated against a strictly independent, hold-out validations group. Unlike static filtering, our dynamics framework adapts to local conditions, substantially increasing the volume of valid observations data while simultaneously driving a significant reduction in error. By optimizing the selection of high-quality retrievals without discarding valuable data, this method significantly refines the inputs available for climate models. The primary outcome of this framework is the ability to predict pixel-level compliance with Global Climate Observing System (GCOS) standards, offering a metric directly applicable to climate studies. This "Predictive Dynamic Quality Filter" transforms aerosol retrieval quality filtering from a passive estimation task into an active, self-assessing framework. By unlocking the full statistical potential of the RemoTAP algorithm, we provide a robust pathway for generating climate-quality datasets from historical POLDER archives, current instruments as SPEXone and 3MI and future missions like and CO2M, significantly refining our constraints on aerosol-cloud interactions and radiative forcing.