| 26 Sep 2025
Ammonia Bidirectional Flux Model Tailored for Satellite Retrieval Parameter Inversions
Abstract. Atmospheric ammonia is an important chemical species for air quality and ecosystem health, and has levels that have been either growing or stagnant in many regions, in contrast to many other pollutants that have been on the decline in recent decades. As bottom-up emissions inventories for ammonia often have large uncertainties, inversions using ammonia retrievals from satellite-borne instruments are an important tool for improving these emissions inventories. Bidirectional flux models for ammonia give a unified model for emission and dry deposition and have recently been incorporated into a number of atmospheric chemistry models. However, there have been relatively few studies using satellite observations in inversions to refine the parameters in bidirectional flux models. A new bidirectional flux model is introduced that is designed specifically for use with inversion systems. This bidirectional flux model reduces the number of redundant parameters, as viewed by the inversions, to yield a model that is both optimized for use with inversion systems and is easy to implement and maintain in atmospheric chemistry models. Inversions using CrIS ammonia retrievals with this bidirectional flux model implemented in the GEM-MACH air quality forecasting model were performed. With parameters set via inversions, significant differences in surface atmospheric ammonia concentrations between the existing unidirectional model and newer bidirectional model were observed in many agricultural regions, varying by as much as 10 ppbv (or between 50 % to 150 %) in these locations. The bidirectional flux model improved the agreement of GEM-MACH with surface observations in the important growing seasons (spring, summer, fall), with biases decreasing between 14 % and 26 % as compared to the unidirectional model and decreased the error standard deviation between 5 % to 20 %, but also degraded this comparison somewhat for the winter.
This study presents a novel bidirectional ammonia flux parameterization approach using satellite-based inversion, which directly links model parameters with ammonia concentrations observed from satellites. Compared with methods based on sporadic field measurements, the use of satellite observations provides continuous datasets with much broader spatial coverage. The results indicate that the bidirectional scheme with optimized parameters improves the performance of the air quality model when compared with ground-based ammonia observations during the growing season. Overall, I find the study scientifically innovative, and the methodological development valuable for the atmospheric ammonia modeling community.
However, I have several concerns regarding the inversion approach and certain aspects of the methodology that require further clarification.
First, the bidirectional flux scheme implemented in this work primarily represents ammonia exchange between the atmosphere and soil or ground surfaces, which is most relevant for agricultural soils and fertilizer-related emissions. In the inversion, however, satellite NH3 retrievals appear to be assimilated over all land areas without screening by land-use type. Satellite observations measure the total atmospheric NH3 column, which integrates signals from all nearby sources. If the inversion does not explicitly separate or mask different land-use categories, there is a risk that ammonia originating from non-soil sources, such as livestock facilities or urban emissions, may be incorrectly attributed to soil-related parameters in the bidirectional scheme. In such regions, the retrieved atmospheric ammonia may reflect a combination of emission processes that are not fully represented by the soil-based bidirectional scheme. This could also potentially explain why the model performance improvements appear strongest in cropland-dominated agricultural regions, where soil-related emissions are more consistent with the assumptions of the bidirectional model. I therefore suggest that the authors discuss whether any land-use filtering or weighting was applied when assimilating satellite observations, how non-soil ammonia sources may influence the inversion results, and whether the optimized parameters could be biased by the presence of mixed emission sources.
Second, the temporal resolution of the simulations and inversions is not entirely clear from the manuscript. The paper mentions that a month’s worth of satellite retrievals is used in each inversion, resulting in monthly mean parameters. However, it would be helpful if the authors could clearly state the temporal resolution of the model simulations, for example whether transport and chemistry are simulated at hourly time steps, the temporal aggregation used in the inversion procedure, and whether the satellite observations are assimilated at their native temporal resolution or aggregated before assimilation. Given the strongly episodic nature of ammonia emissions, particularly fertilizer-induced emission pulses following application events, the temporal resolution could influence the ability of the inversion system to properly constrain bidirectional exchange parameters.
Third, the model evaluation focuses primarily on surface ammonia concentration observations from the AMoN and NAPS monitoring networks. While these datasets are valuable for evaluating atmospheric ammonia concentrations, ammonia deposition observations are also widely used to constrain ammonia emissions and nitrogen budgets. Because bidirectional exchange influences both emissions and deposition processes, deposition measurements, such as ammonium wet deposition observations, could provide an additional and independent constraint on model performance. It would therefore be useful for the authors to discuss whether deposition observations could be used in future work to further evaluate the optimized bidirectional scheme.
Line 110: Please clarify the version and type of the “2011-based projected 2017 inventory for the United States.” The 2011 US NEI inventory has undergone several updates, and it would be helpful to specify whether the inventory used here is the emission factor–based inventory or one generated using a bidirectional scheme.
Line 175: “nonzero” should be written as “non-zero.”
It would also be helpful if the authors included a discussion of uncertainties associated with the inversion results. A brief discussion of how uncertainties in satellite retrievals, prior emissions, and model parameter assumptions may influence the inversion results and the retrieved parameters would help readers better interpret the robustness of the conclusions.