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<p>Uncertainty in the Air Mass Factor (AMF) causes systematic biases in satellite-retrieved nitrogen dioxide (NO<sub>2</sub>) vertical column densities (VCDs). We introduce the first physics-informed neural network that directly refines TEMPO’s AMF to improve the conversion of its slant columns to VCDs within a self-sufficient data pipeline. Our unique Transformer-Fourier Neural Operator hybrid architecture learns the dependencies among 2D and 3D radiative transfer features that govern AMF, using a Huber loss that enforces consistency between predicted AMF and radiative transfer theory. Trained on 74,919 TEMPO-Pandora observation pairs across North America from August 2023 to December 2024, our bias correction framework improves R<sup>2</sup> from 0.58 to 0.80 and reduces RMSE by 30%, with stable performance across all seasons. By incorporating an explicit physical constraint during training rather than relying on post-hoc bias fitting, our approach complements purely data-driven learning and provides a theory-consistent correction of AMF-driven biases in satellite VCD retrievals.</p>

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Hybrid transformer and physics-informed neural operator for correcting TEMPO NO2 biases over North America

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Sagun Gopal Kayastha,
  • Jincheol Park,
  • Yunsoo Choi

摘要

Uncertainty in the Air Mass Factor (AMF) causes systematic biases in satellite-retrieved nitrogen dioxide (NO2) vertical column densities (VCDs). We introduce the first physics-informed neural network that directly refines TEMPO’s AMF to improve the conversion of its slant columns to VCDs within a self-sufficient data pipeline. Our unique Transformer-Fourier Neural Operator hybrid architecture learns the dependencies among 2D and 3D radiative transfer features that govern AMF, using a Huber loss that enforces consistency between predicted AMF and radiative transfer theory. Trained on 74,919 TEMPO-Pandora observation pairs across North America from August 2023 to December 2024, our bias correction framework improves R2 from 0.58 to 0.80 and reduces RMSE by 30%, with stable performance across all seasons. By incorporating an explicit physical constraint during training rather than relying on post-hoc bias fitting, our approach complements purely data-driven learning and provides a theory-consistent correction of AMF-driven biases in satellite VCD retrievals.