4DEnVar-based inversion system for ammonia emission estimation in China through assimilating IASI ammonia retrievals

The project sets up and tests a four‑dimensional ensemble variational (4DEnVar) data‑assimilation system to improve atmospheric ammonia emission inventories. Bottom‑up inventories often have large spatial and time uncertainties, which makes air‑quality modeling less reliable. To reduce these problems, the study uses satellite observations from the Infrared Atmospheric Sounder Interferometer (IASI) on the METOP operational satellites. The IASI retrievals are fed into the GEOS‑Chem chemical transport model, which adjusts the prior ammonia emission estimates for China. The approach runs monthly inversion experiments for key seasons. These experiments compare the 4DEnVar results with the Multi‑resolution Emission Inventory for China (MEIC). The assimilation creates spatial emission adjustments and highlights where the original inventory

underestimates emissions, especially in high‑emission areas of North, East, and Northwest China. The work also gives step‑by‑step guidance for building atmospheric‑chemistry models, setting up variational data‑assimilation pipelines, and checking the updated emission fields against independent ground‑based or satellite measurements. This should lead to more accurate regional air‑quality forecasts and better emission‑control strategies.

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