Figure  shows the NH3 distribution measured by IASI for 2011 and gridded at 0.25∘ lat × 0.5∘ long resolution for the areas covered by the available measurement data sets. In this graphic a weighting procedure has been applied on the IASI daily columns to take into account the error variability of the data set within the average . The main agricultural source areas are observed in the Indo-Gangetic plain, the North China Plain (NCP) as well as intensive agricultural valleys such as the Ferghana Valley in central Asia and the San Joaquin Valley in California . High NH3 values are also seen over burned areas mainly in Eastern Europe (April) , over the Magadan region in eastern Russia (May and especially second part of July) and in Indonesia (March). Ground-based data sets of NH3 concentrations for 2011 have been superimposed in Fig. . What is striking from the collected data sets for that year is the scarcity of the NH3 measurements in some parts of the world. Except for the IDAF stations in Africa, no measurements were available for 2011 in the rest of the tropical region and in the Southern Hemisphere. Nevertheless, we find a broadly similar pattern in the surface measurement as in the satellite-derived distribution, with the largest concentrations for the Chinese stations, followed by the IDAF sites in Africa. Europe and the USA present lower concentrations with the exception of the Eibergen station (EMEP) in the Netherlands (see also below).

Although the comparison with the IASI measurements is qualitatively reasonable, we find that in North America the satellite-retrieved columns are relatively higher than the ones reported by the AMoN network. It is worth noting that the sites with yearly concentrations available for 2011 are mainly located outside the intensive source areas for the USA. In China, the highest values are observed (from the ground and IASI) in the NCP and in the Chengdu Plain (Sichuan basin). The satellite columns are lower in southeastern China, which is consistent with the high-resolution NH3 emissions inventory from . In Europe, the EMEP stations are mainly located in its central part, where IASI-NH3 columns have low values, in agreement with the ground-based concentrations. The Netherlands is the principal hotspot region in the northern part of central Europe in both the satellite and surface measurements. A second hotspot in Europe is the Po Valley of northern Italy. Although this was not represented by the EMEP measurements, this is subject of a specific analysis under the ECLAIRE project (eclaire-fp7.eu).

Figure  presents a similar comparison with focus over Europe (left), China (middle) and Africa (right). The top panels show the IASI-retrieved column distributions (×1016 molec cm-2, right vertical color bar) on which the ground-based concentrations from the NEU and the NNDMN network (µg m-3, left vertical color bar) and the volume mixing ratio (VMR) from the IDAF network (ppbv, left vertical color bar) have been superimposed. The bottom panels provide the corresponding correlation plots with their linear regression fits (reduced major axis), and Pearson's correlation coefficient (r, underlined when significant). For this comparison the IASI columns have been converted to surface concentrations by using the model profiles considered in the retrieval procedure . Although such a conversion introduces additional errors, it allows direct comparison with similar quantities. We note that while the conversion to surface observation does not change the correlation much, it determines the intercept. The use of an inadequate profile shape leads unavoidably to biased surface concentrations from IASI. As an example, the Pearson's r calculated when comparing IASI total columns and NEU ground-based concentrations is equal to 0.284, while it is equal to 0.275 when comparing satellite and ground-based surface concentrations.

For Europe, the averaged satellite distribution for 2008, 2009 and 2010 is presented with superimposed the 3-year mean NEU ground-based observations (Fig. , top left). High values can in particular be seen in the western part of Russia, which is an area that was strongly impacted by the Russian fires of 2010 . The effect of these fires appears to be overrepresented in the satellite distribution due to the weighted averaging procedure associated with the higher IASI sensitivity in fire plumes . However, overall IASI observes a similar pattern as that reported by the NEU sites. The highest surface concentrations are measured in northwestern Europe, going up to 7 µg m-3 at Cabauw, Netherlands, where satellite columns are above 2×1016 molec cm-2. The lowest surface concentrations reported by the NEU network are observed at the northern European stations, where IASI columns are low too. The statistical analysis (Fig. , bottom left) gives a significant correlation. This is especially true considering that we are comparing monthly averaged surface concentrations from ground-based instruments (integrated over day and night) with the monthly satellite mean that includes only measurements from the morning overpass time (only monthly means with an IASI-weighted relative retrieval error below 100 % have been taken into account). The linear regression is indicated in red and is characterized by a Pearson's r of 0.28 (n=1337 number of coincidences), a slope of 0.42 and an intercept at 2.1 µg m-3. Note that if the satellite data are restricted to the monthly mean concentrations with associated error below 75 %, the Pearson's r increases to 0.35 (n=961). Almost all the sites in Europe are characterized by a positive correlation coefficient when it is significant (p value below 0.05). The highest correlation (r=0.81) is obtained for the Fyodorovskoye site (RU-Fyo), and this is explained by the very large columns observed during the Russian fire event of 2010. The second site characterized by a high correlation coefficient is the Monte Bondone site (IT-MBo) with r=0.71, which is characterized by high NH3 concentrations in summer and low in winter, consistent with local livestock grazing patterns. By contrast, the most northern sites (located in Finland) indicate a negative correlation (Hyytiälä, r=-0.44; Lompolojänkkä, r=-0.43; Sodankylä, r=-0.45) with an associated p value slightly above 0.05. These are the sites with very low ground level concentrations (3-year average close to 0.1 µg m-3), suggesting that the monthly variability for such small concentrations in a region associated with low thermal contrast cannot be reliably detected by IASI. The other NEU stations with a significant r above 0.5 include Brasschaat (BE-Bra, 0.54), Fougères (FR-Fou, 0.58) and Vall d'Alinyà (ES-VDA, 0.58). The list of stations and their respective correlation coefficient, as well as their associated p and n values, is provided in Table A1.

For China (2009 to 2013), the spatial patterns in NH3 abundance are in good agreement (Fig. , top middle panel). We find the highest concentrations in the NCP area and the lowest in remote areas such as the Linzhi site (LZ, on the Tibetan Plateau) and several sites in northeast China. To better illustrate the potential for a high degree of correspondence between the satellite columns and the surface measurements, we provide in Fig.  the monthly averaged time series at Shangzhuang (SZ) from the ground and from space (r=0.72). This suburban station northwest of Beijing (40.14∘ N, 116.18∘ E), surrounded by agriculture and far from a main road , has one of the longest records of NH3 concentrations in China, from January 2008 to February 2014. The IASI-derived total columns (top, blue, molec cm-2) reproduce nicely the ground-based concentrations (bottom, red, µg m-3) time evolution with concentration peaks in summer (June–July), close to 25 µg m-3 at the surface and around 3×1016 molec cm-2 observed from IASI. Looking at interannual variability, for example in 2013, the fertilization peaks of March, July and October are clearly observed . It is interesting to note that the highest January surface concentration (5.1 µg m-3) and satellite total column (7.8 ×1015 molec cm-2) are measured during the severe winter pollution episode of 2013 (see ). The correlation analysis for the monthly values of the 43 NNDMN sites gives a Pearson's r equal to 0.39 (considering the IASI data with a monthly relative retrieval error below 100 %) and a slope of the regression equal to 0.21 (n=1149). The Pearson's r is globally higher than for Europe probably due to the higher concentrations in China. This is also observed in the monthly retrieval error which are overall lower. In addition to Shangzhuang, the sites with a significant Pearson's r above 0.5 are China Agricultural University (CAU, 0.51), Lingshandao (LSD, 0.55), Changdao (CD, 0.58), Gongzhuling (GZL, 0.52), Bayinbuluke (BYBLK, 0.59), Fengyang (FY, 0.64), Ziyang (ZY, 0.58), Yanting (YT, 0.70) and Dianchi (DC, 0.64), where the p values for these sites are all less than 0.01. Only one site in the NNDMN network gives a significant negative correlation: the cleanest site in the network in Tibetan Plateau (LZ, r=-0.75). With a mean ground-based concentration of 1.03 µg m-3, this again illustrates, as suggested with the Finnish sites, the limitation of IASI to provide reliable monthly NH3 values at very low concentrations. The results for all the sites are presented in Table A2.

For Africa, both IASI and surface network report the highest values in western and central Africa and the lowest in South Africa for the 4-year average (2008 to 2011, Fig.  top right panel). However, over Western Africa the highest VMR are reported by the network for the dry savanna sites (located above 10∘ N), while IASI observes higher columns closer to the coast. This could possibly be due to the lack of IASI sampling above these Sahelian sites during the rainy season in June–July, which is characterized by large concentrations measured at the surface. The high density of livestock concentrated on the fresh pasture at that time implies high surface emissions. However, this time period is typically also associated with a high deposition and/or cloud coverage, preventing IASI from capturing these events while they are monitored from the ground . have shown that the dry savanna sites are characterized by higher concentrations during the wet season (June–July); conversely, the wet savanna sites (closer to the West African coast) present the larger amount during the dry season (January–February). The latter may therefore be better monitored by IASI. The right bottom plot (Fig. ) compares the surface VMR for Africa in ppbv. There is no correlation when the data of all the sites are grouped. This could be explained by the sparsity of the IDAF data set combined with the difficulties of the satellite to catch specific high emission events. The only station showing a positive significant Pearson's r is Lamto (Ivory Coast, 0.39), which is not surprisingly a wet savanna site. The comparison for each station individually is provided in Table A3.

In general, the regression intercept is relatively high, while the slope is relatively low. For all three regions the largest measured concentrations are underestimated in the IASI surface-derived data. This smaller range of concentrations could be a consequence of using a fixed vertical profile shape in the retrieval procedure for IASI spectra. Indeed, in the case of a low boundary layer height, concentrations are larger in that narrow layer, which is not considered by the straightforward procedure applied to retrieve IASI surface concentrations from the columns. Moreover, the vertical profiles above land from the GEOS-Chem model have already been identified as being too low above California, especially for strongly polluted areas . Another reason could lie in the influence of very local sources on the ground-based data (see further). Contrary to what we find for high concentrations, there is a positive bias of IASI for low concentrations. There are at least three possible reasons for this. The first stems from the way the relative retrieval errors are taken into account. Applying weighted averaging of IASI columns using the relative retrieval error tends to favor the largest concentrations, as the lower values are associated with higher error estimates for the same value of thermal contrast . A second reason, in the case of very low NH3 amounts, is that the random error of the HRI always translates into a positive contribution of the column (and hence does not average to zero for zero NH3). The third reason is again linked with a misrepresentation of NH3 vertical distribution. In low NH3 abundance cases and/or in areas where NH3 is being deposited, there is no strong vertical gradient near the surface. The use of a polluted profile shape (peaking at the surface) in the retrieval procedure of IASI surface concentrations tends to overestimate the surface contribution of the total columns.

We recall that all regressions shown and discussed in this section are based on weighted monthly IASI-derived NH3 means, with the weight being determined by the relative error of the individual retrieved columns. We also made the comparisons with unweighted monthly means and found somewhat different parameters for the regression line but an overall weak impact on the regression coefficients. In all cases, the slope remains well below 0.5 and the intercept smaller but still positive.

The LML data set offers long-term hourly resolved NH3 measurements allowing the investigation of individual IASI observations and testing the effect of averaging different time scales. However, it is worth noting that this network provides measurements in an area (the Netherlands) that is not particularly favorable for infrared remote sensing of surface pollution (low thermal contrast and relatively high cloud coverage). Table  summarizes the results from the comparison of the IASI concentrations (columns converted to surface concentrations as above) to those measured from the ground. The upper part presents the comparison from the linear regression for each individual site (slope (a) and intercept (b) of the linear regression, Pearson's r (underlined when significant) and the number of observations (n)). We took into account only the IASI measurements covering the stations, which means that the true elliptical footprint is considered and only the observations including the LML site are kept. Six of the sites show significant correlation and for all, except for one (Valthermond, which has a negative intercept and fairly high slope), we find a very low slope (0.03–0.3) but a high intercept between 0.89 µg m-3 (De Zilk) and 1.91 (Wekerom). This shows again the difficulties of IASI to capture the entire range of local surface concentrations, as explained above. The best Pearson's r are observed for Valthermond (0.49), De Zilk (0.39) and Eibergen (0.38) sites. When all the sites are taken together we obtain a Pearson's r of 0.29, a slope of 0.07 and an intercept of 1.68 µg m-3. These results, obtained by comparing individual IASI value with ground-based measurement at the overpass time of the satellite, could be due, as discussed previously, to the misrepresentation of the vertical distribution in the processing of the spectra and the need to consider the boundary layer height.

A factor preventing one from drawing strong conclusions from this comparison is the spatial representativity of the ground-based measurement within the large footprint of the satellite (going from 113 km2 at nadir to around 613 km2 for the highest satellite viewing angles). It means that local sources inside the satellite footprint could have a strong influence on the IASI column values but not be represented in the ground-based data sets due to the high horizontal gradient of NH3 concentrations around the source . Conversely, background concentrations within the IASI pixel will tend to mask single local sources. The representation issue of ground-based measurement stations has already been highlighted during model performance evaluation . To avoid this uncertainty, which is difficult to assess in the validation, we could consider the general rule that the LML network is representative for the entire Netherlands . For instance, using the eight stations together we can calculate a daily mean concentration of NH3 at the overpass time of the satellite that we compare with the IASI-weighted mean of the measurements inside the area covered by the network (considered here as the following box: 51.185–53.022∘ N and 4.01–7.106∘ E). Doing so, we find that the Pearson's r increases to 0.28, while the slope and the intercept of the linear regression are 0.21 and 0.96 µg m-3, respectively.

Finally, to investigate the impact of temporal variability and to compare with the previous results of monthly resolved data, we calculated monthly average of IASI measurements above the Netherlands and the LML ground-based concentrations at the IASI overpass time. The time series of Fig.  show the temporal variability in 2008, 2009, 2010, 2011 and 2012 of IASI-NH3 columns (top panel, blue, molec cm-2) and of the surface concentrations derived from IASI columns (bottom panel, red, µg m-3) and monitored by the LML sites (bottom panel, black, µg m-3). Only values with a relative retrieval error below 100 % have been taken into account. The spring peak linked with fertilization practices in the Netherlands is clearly identifiable in the LML and IASI time series in March/April. Looking at the surface concentrations, a fair agreement is found between the ground-based and the satellite observations. The monthly variability is consistent between both instruments even if the IASI-derived values are not reproducing with the same amplitude the peaks observed from the ground. This could be due, as explained previously, to a misrepresentation of the NH3 vertical distribution. As shown in the lowest row of Table , the comparison based on monthly data is characterized by a Pearson's r of 0.47, substantially higher than the ones obtained with the previous data sets. The linear regression has a slope of 0.19 and an intercept of 1.86 µg m-3.