Efficient Neural-Network-Based High-Resolution Radiative Transfer for CO___ Retrieval, and Application to Interferometric Sensing
A feedforward multilayer perceptron (MLP) surrogate is proposed to replace computationally expensive line-by-line radiative transfer (RT) simulations for predicting top-of-atmosphere radiances in the CO2 weak band The model uses a combined mean absolute error (MAE) loss on both radiances and RT Jacobians, preserving spectral accuracy and sensitivity to geophysical parameters The MLP-based RT surrogate is coupled with the NanoCarb imaging interferometer's instrumental response to form an efficien
Analysis
TL;DR
- A feedforward multilayer perceptron (MLP) surrogate is proposed to replace computationally expensive line-by-line radiative transfer (RT) simulations for predicting top-of-atmosphere radiances in the CO2 weak band
- The model uses a combined mean absolute error (MAE) loss on both radiances and RT Jacobians, preserving spectral accuracy and sensitivity to geophysical parameters
- The MLP-based RT surrogate is coupled with the NanoCarb imaging interferometer's instrumental response to form an efficient forward model for CO2 concentration retrieval
- The work is motivated by the Horizon Europe SCARBOn project's goal of deploying a low-cost satellite constellation for high-revisit, high-spatial-coverage monitoring of CO2 and CH4 emissions
- This approach enables faster retrieval algorithms critical for distinguishing anthropogenic from natural greenhouse gas sources in climate change studies
Why It Matters
This research directly addresses a key computational bottleneck in atmospheric remote sensing: radiative transfer simulations are among the most expensive operations in full-physics retrieval pipelines, limiting the feasibility of high-resolution, high-frequency global monitoring. By replacing line-by-line RT with an MLP surrogate, the method enables near-real-time greenhouse gas concentration estimation from satellite data, which is essential for operational emissions monitoring and climate policy enforcement.
Technical Details
- Architecture: A feedforward multilayer perceptron (MLP) serves as a surrogate model for high-resolution radiative transfer in the CO2 weak band, predicting top-of-atmosphere radiances
- Loss Function: A combined MAE loss is applied to both radiances and RT Jacobians, ensuring the model preserves not only spectral fidelity but also the sensitivity structure required for inversion/retrieval algorithms
- Instrument Integration: The MLP-based RT surrogate is coupled with the NanoCarb imaging interferometer's instrumental response model, producing an end-to-end forward model tailored to the SCARBOn satellite constellation
- Application Domain: The system targets CO2 and CH4 concentration retrieval for the Horizon Europe SCARBOn project, which plans a low-cost satellite constellation for atmospheric greenhouse gas monitoring
- Cross-Disciplinary Scope: The work sits at the intersection of machine learning (cs.LG) and atmospheric physics (physics.ao-ph), combining physics-informed neural modeling with climate science requirements
Industry Insight
- Physics-informed neural surrogates for radiative transfer represent a scalable pattern that will likely extend beyond CO2/CH4 to other atmospheric constituents and spectral bands, accelerating the deployment of next-generation Earth observation missions
- The dual loss strategy (radiances + Jacobians) is a transferable design principle for any retrieval problem where sensitivity information is critical, not just spectral accuracy
- As satellite constellations for climate monitoring proliferate, the computational savings from ML-based forward models will become a decisive factor in determining which retrieval algorithms can run onboard or in near-real-time pipelines, favoring lightweight MLP architectures over full-physics codes
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