Estimating Emissions and Near-Surface Concentrations of Short- and Long-Lived Atmospheric Species From Satellite Observations Using the Directional Derivative Approach

The directional derivative approach represents the theory and algorithm to rapidly derive emissions using satellite observations without invoking a chemical transport model. The emission signal originates from the directional derivative of the satellite-observed column amount with respect to the horizontal wind. This approach is based on the principle of mass conservation and derives emissions directly from new-generation satellite products (e.g., TROPOMI and TEMPO) and global reanalysis (e.g., ERA5), both of which are publicly available within a few days of lag time. Topography and chemical loss effects are taken into consideration, which would otherwise result in spurious sources or sinks. The topographic correction term generates the scale height of the species as a byproduct, which is useful for estimating the near-surface concentration based on the column amount. This presentation will demonstrate three applications of the directional derivative approach, including post-COVID-19 NO_x emissions over three continents derived from TROPOMI NO₂ column observations (https://doi.org/10.5194/acp-23-8727-2023), methane emissions from key oil and gas basins derived from TROPOMI methane observations, and near-surface NO₂ concentrations over the contiguous US estimated from TROPOMI NO₂ column observations. This directional derivative approach has been implemented in object-oriented, open-source Python and is available publicly for high-resolution and low-latency emission estimation for different regions, atmospheric species, and satellite instruments.