Abstract Wildfires emit volatile organic compounds (VOCs) that contribute to ozone and aerosol pollution. Quantification is challenged by the diversity of emitted species and by their complex dependence on fire characteristics, with scarce field observations available for model evaluation. Remote sensing in the thermal infrared (IR) is a powerful tool for detecting fire VOCs: many compounds exhibit distinct signatures at these wavelengths, and measurements are unaffected by fine aerosols prevalent in smoke. Here, we develop the first thermal IR VOC measurements from an aircraft‐based platform, using radiance observations from the Scanning High‐resolution Interferometer Sounder (S‐HIS) deployed aboard the NASA ER‐2 during the 2019 FIREX‐AQ campaign. We focus on methanol and ethene, and employ a neural network retrieval adapted from Cross‐track Infrared Sounder (CrIS) algorithms. The S‐HIS retrievals achieved high precision ( R 2 = 0.95–0.99) and accuracy (median absolute bias <5.5 × 10 15 molec./cm 2 ) versus the full training data set. However, at low‐to‐moderate concentrations, retrieval performance is degraded by instrument noise. When considering scenes below the median methanol signal encountered during FIREX‐AQ, 30‐fold aggregation is needed to achieve R 2 = 0.8, and no amount of aggregation yields the performance that would be achievable for a single pixel with the 10× lower noise of CrIS. The S‐HIS VOC observations exhibit clear enhancements over and downwind of fires that align with those seen by CrIS. Our results provide a foundation for future aircraft‐based VOC measurements in the thermal IR, and emphasize the critical importance of instrument noise for the design of future airborne and spaceborne sounders.
Hu et al. (Wed,) studied this question.