Sub-City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements.

While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA's GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations.

An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO Retrievals.

A growing constellation of satellites is providing near-global coverage of column-averaged CO observations. Launched in 2019, NASA's OCO-3 instrument is set to provide XCO observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO-3 observations and future space-based column-observing missions.