2.1 Infrared Atmospheric Sounding Interferometer NH3
The IASI instrument onboard the Metop-A satellite was launched into low-Earth polar sun synchronous orbit in October 2006. The instrument has two overpass times in the morning (09h30 local solar time or LST) and at night (21h30 LST), providing global coverage twice a day. The elliptical IASI pixels range in ground pixel resolution from 12 km × 12 km at nadir (directly below the instrument) to about 20 km × 39 km at the edges of the 2200-km-wide swath (Clarisse et al., 2011). The data product we use is the Level 2 cloud-free reanalysis product of total column NH3 (version 3R-ERA5) (Van Damme et al., 2021). The retrieval uses machine learning, specifically a neural network trained relationship between column NH3 and a so-called hyperspectral range index or HRI, where the HRI is a measure of the relative enhancement in the spectral signature due to NH3 (Van Damme et al., 2014a; 2017; Whitburn et al., 2016a). The data product includes reported retrieval errors estimated by perturbing individual input parameters in the neural network framework (Whitburn et al., 2016a). Products resulting from the neural network retrieval approach have been validated against global and regional networks of ground-based NH3 observations of surface concentrations and column densities (Dammers et al., 2016; Guo et al., 2021; Vohra et al., 2021a; Whitburn et al., 2016a). In general, IASI NH3 reproduces the temporal variability in surface concentrations of NH3, but exhibits a low bias (Dammers et al., 2017; Whitburn et al., 2016a).
We use daytime (09h30 LST) IASI NH3 for 2008-2018 to obtain multiyear monthly means. This dampens influence of interannual variability and ensures consistency with NAEI NH3emissions that are estimated with 30-year mean meteorology (Ricardo, 2019a). We grid the data to finer spatial resolution (0.1° × 0.1°; ~ 10 km) than the native resolution of the instrument using the tessellation oversampling technique described in Zhu et al. (2017) and Sun et al. (2018). This takes advantage of the spatial variability in coverage of individual orbits and the long data record from IASI to reduce noise and smooth out spatial gradients in the gridded product (Sun et al., 2018). Briefly, tessellation involves weighting individual IASI pixels by the area of overlap with the target grid and also includes error-weighting using the reported retrieval error. In our application of the tessellation gridding technique, we approximate the area of IASI pixels as a quadrilateral polygon, where the corners of each polygon are estimated as the distance midway between the centres of neighbouring IASI pixels.
Retrieval of NH3 over the UK is challenging, due to persistent clouds and relatively cool conditions. Extreme retrievals, identified as absolute columns > 5 × 1017molecules cm-2, are removed. We also exclude IASI NH3 columns retrieved on 26-27 July 2018, coincident with the summer 2018 heat wave (McCarthy et al., 2019). Record high temperatures (> 30°C) lead to UK IASI NH3column densities 4-times greater (~4 × 1016 molecules cm-2) than the UK July multiyear mean (~1 × 1016molecules cm-2). Including these days increases the July multiyear mean by 11% and reduces its representativeness as a climatological mean for comparison to the NAEI. A similarly large influence of heat waves on IASI NH3 columns was reported for the summer 2010 heat wave over mainland Europe (Van Damme et al., 2014b). After using oversampling to grid the data to 0.1° × 0.1°, gridded multiyear means with large relative error (>50%) are removed. This leads to loss of the majority of IASI NH3 columns in October-February, so only March-September multiyear means are considered. Additional filtering is applied to the gridded multiyear monthly means to remove extreme values identified as columns < -1 × 1016 molecules cm-2 and > 1 × 1017molecules cm-2. These only account for <0.1% of the March-September data, but affect spatial consistency between IASI and CrIS.