A Signal‐Derived Retrieval Reduces Bias in TEMPO NO ₂

Abstract Satellite measurements of are widely used for investigations of emissions, pollution exposure, and photochemical ozone production. The most commonly available data products compromise on accuracy and spatial resolution in favor of lower compute time. In addition to limited spatial resolution, inaccurate modeling of winds and misrepresentation of the lifetime contribute to large systematic errors in the satellite retrievals that depend on these modeled outputs. We introduce a signal‐derived retrieval (SDR) for Tropospheric Emissions: Monitoring of Pollution (TEMPO) which derives instrument‐resolution a priori profiles from measured slant column densities. This approach enables fast reprocessing of TEMPO data and removes the gradient smoothing caused by low resolution priors and systematic errors in models. The SDR product reduces the negative bias of TEMPO total against Pandora reference measurements by , with a larger impact on the tropospheric column. This bias correction is shown to have important effects on the application of TEMPO for relevant science questions. The disparity between low and high income areas in San Jose, CA increased by using the SDR. The maximum to upwind line density ratio increased by using the SDR, indicating higher emissions and a shorter lifetime. These results outline the magnitude of systematic bias introduced by modeled a priori profiles and reduced by the SDR.