UC Berkeley

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. Plain Language Summary Measurements of are useful for understanding the sources, chemistry, and spatial distribution of pollution. The interpretation of satellite‐based remote sensing spectroscopy as a physical quantity, the column of , requires an initial estimate of the vertical distribution of , which is usually computed with a model. The models used for this often have a lower resolution than the observations and may inaccurately represent the wind which transports pollutants from their sources. Most satellite products compromise on spatial accuracy by using lower resolution models. We introduce a new method to estimate the initial distribution using the satellite measurements themselves, in combination with a lower resolution model. This eliminates some systematic biases in the interpretation of the spectrum and leads to wider differences in the pollution experienced by different economic groups within an urban area. It also increases estimates of the rate at which pollution is emitted and indicates differences in the chemistry that occurs downwind from sources. Key Points We describe a rapid, measurement based, high resolution retrieval for TEMPO This retrieval increases peak TEMPO column by over 20%, with reductions in negative bias relative to Pandora reference measurements Gradients in column are sharpened, impacting the study of emissions, lifetimes, and exposure disparities