| 16 Jun 2026
A New NO2 Profile Retrieval from a Combination of Scanning Long-Path DOAS and Point Measurements Using Bayesian Inference-Based Spatiotemporal Reconstructions
Abstract. This study presents a fully Bayesian approach for retrieving reliable vertical profiles of nitrogen dioxide (NO2) from a combination of path and in-situ measurements. Instead of focusing on complete two-dimensional reconstructions, we employ a three-dimensional correlated field model (2D in space + 1D in time) as a physically consistent intermediary step to extract profile information. The Bayesian inference framework, implemented using the Numerical Information Field Theory for Python library (NIFTy), allows for a rigorous propagation of measurement uncertainties and explicitly includes spatial and temporal correlations. Data from the CINDI-III campaign, including two in-situ ICAD devices and a new DOAS system, were used as input. The new DOAS system consists of a fast scanning active and passive DOAS instrument that can be used for Long-Path as well as MAX-DOAS measurements, called HyDOAS (Hybrid Differential Optical Absorption Spectroscopy). The resulting profiles were compared against independent MAX-DOAS retrievals as a consistency check. Our results demonstrate that the Bayesian spatiotemporal framework yields physically consistent and temporally coherent NO2 profiles that agree well with established methods within their uncertainties. The approach offers a promising pathway to derive high-quality vertical information even from geometrically limited measurement configurations.
Competing interests: S.S., J.L., and D.P. are employees of Airyx GmbH, which manufactures the HyDOAS and ICAD instruments used in this study. The remaining authors declare no competing interests.
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