Article révisé par les pairs
Résumé : Abstract. Satellite measurements play an increasingly important role in the study of atmospheric ammonia (NH3). Here, we present version 4 of the Artificial Neural Network for IASI (ANNI; IASI: Infrared Atmospheric Sounding Interferometer) retrieval of NH3. The main change is the introduction of total column averaging kernels (AVKs), which can be used to undo the effect of the vertical profile shape assumption of the retrieval. While the main equations can be matched term for term with analogous ones used in UV/Vis retrievals for other minor absorbers, we derive the formalism from the ground up, as its applicability to thermal infrared measurements is non-trivial. A large number of other smaller changes were introduced in ANNI v4, most of which improve the consistency of the measurements across time and across the series of IASI instruments. This includes a more robust way of calculating the hyperspectral range index (HRI), explicitly accounting for long-term changes in CO2 in the HRI calculation and the use of a reprocessed cloud product that was specifically developed for climate applications. The NH3 distributions derived with ANNI v4 are very similar to the ones derived with v3, although values are about 10 %–20 % larger due to the improved setup of the HRI. We exclude further large biases of the same nature by showing the consistency between ANNI v4 derived NH3 columns with columns obtained with an optimal estimation approach. Finally, with v4, we revised the uncertainty budget and now report systematic uncertainty estimates alongside random uncertainties, allowing realistic mean uncertainties to be estimated.

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