Advancing Atmospheric Retrieval: A Rapid Physics-Informed Data-Driven Approach using FORUM Simulated Measurements

FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) represents the ninth Earth Explorer mission chosen by the European Space Agency (ESA) in 2019. This satellite mission focuses on delivering interferometric measurements within the Far-InfraRed (FIR) spectrum, constituting approximately 50% of the Earth's longwave flux emitted into space. Enhanced accuracy in measuring the Top Of the Atmosphere (TOA) spectrum in the FIR is crucial for minimizing uncertainties in climate models. However, current instruments fall short, necessitating the incorporation of innovative computational techniques. The mission aims to refine understanding across various atmospheric variables, including tropospheric water vapor, ice cloud properties, and notably, surface emissivity in the FIR.
During the mission's early development, an End-to-End Simulator (E2ES) was devised to showcase proof-of-concept and assess the impact of instrument characteristics and scene conditions on the accuracy of reconstructed atmospheric properties. This simulator comprises a sequence of modules simulating the entire measurement acquisition process, accounting for all major sources of discrepancies in operational conditions leading to the retrieval of geophysical quantities.
From a mathematical perspective, two challenges arise: the radiative transfer equation, known as the direct problem, and its inversion, referred to as the retrieval problem. Both problems can be addressed through a full physics method, particularly applying the Optimal Estimation (OE) approach—a specialized Tikhonov regularization scheme based on Bayesian formulation. However, the computational demands of a full physics method hinder Near Real-Time (NRT) data analysis. Faster models become imperative for next-generation satellites measuring hundreds of spectra per minute and climatology models simulating years of global-scale radiative transfer.
To expedite solutions for both problems, a hybrid approach is employed, combining an a priori regularized data-driven method utilizing the Moore-Penrose pseudoinverse and a neural network approach.