The scheme is based on the optimal estimation method (Rodgers, 2000), which solves an otherwise under-constrained inverse problem by introducing prior information. This method finds the optimal state vector x (which contains the parameters we wish to retrieve) by minimising a cost function: χ2=(y-F(x))TSy-1(y-F(x))+(a-x)TSa-1(a-x), where y is a vector containing each IASI spectral brightness temperature (BT) measurement used by the retrieval; Sy is a covariance matrix describing the errors on the measurements; F(x) is the forward model (FM), which predicts measurements given x; and Sa is the a priori covariance matrix, which describes the assumed errors in the a priori estimate of the state, a. As the FM is non-linear with respect to perturbations in methane and other elements of the state vector, the solution state needs to be found iteratively. In this case we adopt the well-known Levenberg–Marquardt method (summarised in Press, 1995), assuming convergence to have occurred when the change in cost-function value is smaller than 1.

IASI (Blumstein, 2004) provides spectra at 0.5 cm-1 apodized resolution, sampled every 0.25 cm-1, from 625 to 2760 cm-1. Spectra are measured with four detectors, each with a circular field of view on the ground (at nadir) of approximately 12 km diameter, arranged in a 2×2 grid within a 50×50 km2 field of regard (FOR). IASI scans to provide 30 FORs (120 individual spectra) evenly distributed across a 2200 km wide swath. Our retrieval scheme uses measurements between 1232.25 and 1288.00 cm-1, chosen following the work of Razavi (2009) to (a) minimise errors caused by neglecting line mixing in the forward model and (b) include channels with relatively clear transmission to the ground, to help constrain co-retrieved cloud parameters and surface temperature. Channels between 1245–1246.75 and 1267–1270 cm-1 are omitted to avoid problematic spectral features (in the former range attributed to line-mixing effects).

The noise on individual IASI spectra is particularly low in this spectral range (Hilton, 2012), with noise-equivalent brightness temperature (NEBT) around 0.07 K (for a reference scene temperature of 280 K), corresponding to a noise-equivalent spectral radiance (NESR) of 5.8 nW/(cm2 sr cm-1). Early retrievals from this range revealed a significant contribution from scene photon noise, so we adopt the following in-house model of the estimated error in each channel: Δynoise=h+oI‾, where parameters o and h are constants derived empirically from an analysis of the random component of fit residuals in this range (from an early version of the retrieval scheme) and I‾ is the mean spectral radiance over the complete IASI band. This yields NESR values ranging from 3 to 10 nW/(cm2 sr cm-1) over the typical range of band-averaged measured radiances.

RTTOV version 10 (Matricardi, 2009) is the basis of the RAL FM. RTTOV estimates radiances convolved with the IASI spectral response function by use of spectrally averaged layer transmittances, based on a fixed set of coefficients which weight atmospheric-state-dependent predictors. The RTTOV v10 model is sufficiently fast to enable global processing of the IASI mission with modest computational resources. In order to allow interference from the water vapour isotopologue HDO to be modelled adequately, this is handled as an independent variable to the major isotopologue H216O. We have derived coefficients specifically for this spectral range, by running the line-by-line Reference Forward Model (RFM) (Dudhia, 2016) using HITRAN 2008 (Rothman, 2009) spectroscopic line data for the same set of atmospheric profiles and predictors used in Matricardi (2009), with the exception that predictors of the type used for CO are used instead to predict HDO transmittances. The accuracy of the RTTOV model with the new HDO coefficients is tested by comparing radiances from RTTOV to those calculated directly with the RFM for an independent set of atmospheric profiles. The mean and standard deviation (SD) of these differences are generally found to be <0.05 K, though SDs can exceed 0.2 K in a few spectral channels particularly where water vapour transmittances are comparable to those of another variable gas. This source of FM error is accounted for in the retrieval by adding the RTTOV–RFM variances to the diagonals of the IASI measurement error covariance matrix (Sy)

Spectral correlations in the RTTOV error are neglected. In practise this is adequate, as error correlations are found to be weak for the few channels in which this error is large in comparison to the noise.

The ECMWF ERA-Interim (Dee, 2011) reanalysis is used to define the atmospheric temperature profile and the surface pressure appropriate for each IASI scene. Reanalysis fields are provided at 6-hourly intervals, approximately 0.7∘ horizontal resolution, on 60 vertical levels. These fields are linearly interpolated to the IASI location and time. Profiles of methane and water vapour (including the isotopologue HDO) are defined by the retrieval state vector (see below), as is the surface (skin) temperature.

Over sea, the RTTOV sea surface emissivity model is used. Over land, the retrieval scheme currently uses the University of Wisconsin surface emissivity database (Seeman, 2008). Principal components of an ensemble of measured land surface spectral emissivity spectra are used in conjunction with 0.1∘ latitude–longitude gridded monthly data from MODIS to generate global maps of surface spectral emissivity.

It is found that fits to measured spectra based on this FM

We refer here to fits by the FM implemented in a manner identical to that described in this paper, with the exception that points 1–3 above were not applied, i.e. ERA-Interim temperature profiles and University of Wisconsin emissivities are used and methane and other state vector elements are retrieved as described in Sect. 2.5.

A distinguishing feature of the RAL scheme is the approach developed to specify the N2O vertical profile at each IASI measurement location for use in the FM. N2O is modelled so that its spectral features can be exploited to co-retrieve two effective cloud parameters and thereby mitigate related errors on the retrieved methane. In the troposphere, N2O exhibits variations with latitude and season smaller than ∼ 0.5 % (IPCC, 2013); approximately an order of magnitude smaller than those of methane. The annual growth rate of N2O since IASI became operational in 2007 (around 0.23 %year-1) has been much more consistent than that of methane in recent decades. Mixing ratios of both N2O and methane decrease with height in the stratosphere. In the case of N2O, the decrease commences at lower altitude and is steeper than for methane

In the case of N2O, the decrease with height is due to UV photolysis whereas for methane (CH4) it is due to reaction with O(1D).

The BT difference between the IASI observation in a window channel (950 cm-1) and that simulated on the basis of ERA-Interim, assuming clear-sky conditions, is used to screen out scenes which are strongly affected by cloud. If this difference (observation – simulation) is outside the range of -5 to 15 K, the scene is not processed. Furthermore, only scenes having a BT larger than 240 K in the same channel are currently processed, since (a) the retrieval information content is significantly degraded over very cold surfaces (see below) and (b) convergence is often found to be slow in these conditions, leading to a disproportionate use of computational effort.

The IASI MetOp-A orbits from 29 May 2007 to 17 November 2015 have all been processed with this approach, selecting from the four pixels in each FOR the IASI pixel with the warmest BT at 950 cm-1.

The state vector for the retrieval scheme consists of 34 elements, as follows:

Methane mixing ratio (in ppmv, relative to dry air) defined on 12 fixed pressure levels corresponding to z* values of 0, 6, 12, 16, 20, 24, 28, 32, 36, 40, 50 and 60 km, where z* is a simple transformation of pressure, p (in hPa), to approximate geometric altitude (km):z*=16(3-log⁡10p).The prior state is defined to be a fixed value of 1.75 ppmv in the troposphere, broadly representative of Southern Hemisphere mid-latitudes in 2009, and an annual average height–latitude cross section in the stratosphere. The zonal mean is calculated by averaging into 5∘ latitude bins 2 years (September 2008–2010) of output from the TOMCAT chemical transport model run into which ACE-FTS observations of long-lived stratospheric tracers have been assimilated (Chipperfield, 2002). A priori error statistics are estimated as the root-mean-square combination of the SD of the model methane field about its zonal mean and 10 % of the a priori value itself. The model SDs in the troposphere are around 5 %, so a priori errors in the troposphere are set to 175 ppbv (i.e. 10 % of the prior value), increasing (in fractional terms) above the tropopause up to peak values of around 50 %. The prior state and errors are interpolated in latitude to the location of a given IASI observation. To help regularise the retrieval, off-diagonal elements are defined, assuming a Gaussian function in the vertical with full-width half-maximum 6 km (in z∗ units). Here we make a deliberate choice to define a simple and relatively weak constraint to emphasise the vertically resolved information in the IASI measurements. A tighter constraint, which could be justified based on climatological variability, is not necessary to regularise the retrieval and would increase the bias towards the prior (already evident towards high latitudes from the evaluation reported below).

In this section we present comparisons of the height-resolved IASI retrievals with independent sources. Ideally, retrievals would be validated by comparing to accurate, co-located, measured profiles of methane which sample spatial and temporal variability as extensively as IASI and with comparable or finer resolution. Given such information, retrieved column- and layer-averaged methane mixing ratios could be compared to the corresponding independent data both directly and also accounting for the influences of the prior and vertical sensitivity of the IASI retrieval using averaging kernels as follows: ctxI=ca+At(xt-at), where ca is the column average computed from the a priori profile; At is the averaging kernel matrix for the layer, computed from the retrieval gain matrix and spectral weighting function matrix evaluated on the (finely resolved) vertical grid of the independent data; xt is the independently measured mixing ratio profile; and at is the a priori profile interpolated to the vertical grid of the independent data. There are, however, no directly measured datasets which provide sufficient spatial and temporal coverage to enable validation of the IASI retrievals globally over the duration of its mission. We therefore compare the following four sources of correlative data which each have complementary attributes:

The MACC-II GHG flux inversion reanalysis (Bergamaschi, 2013) provides global 3-D methane fields which span the period of our IASI methane retrievals. It employs the TM5-4DVar flux inversion system driven by meteorology from ECMWF's ERA-Interim reanalysis. Daily average methane distributions are provided at a horizontal resolution of 6∘ longitude by 4∘ latitude. Here we focus on the reanalysis dataset (“v10-S1NOAA_ra”) which spans 2000–2012 and is based on the assimilation of NOAA surface level flask measurements. Although the reanalysis does not provide an unambiguous validation dataset (being based on assimilation of data into a model), it remains valuable to compare to IASI because (1) the reanalysis has been validated against independent observations (see Bergamaschi, 2013) and also found to compare very well with the GOSAT measurements also used here (Parker, 2015); and (2) it provides complete global profile information such that comparisons with IASI column- and layer-averaged mixing ratios can properly take into account vertical sensitivity (using Eq. 12) and be performed consistently over the full globe and most of the IASI mission duration. The dataset is particularly valuable for testing that the retrievals realistically represent differences in the spatial and seasonal behaviour of the upper- and lower-tropospheric layer averages (an aspect not well covered by measurements due to limited sampling). MACC GHG data are also used here to enable retrieval sensitivity to be accounted for in comparisons with other datasets, as described below.

In all cases we compare column averages. Where the independent data allow (MACC and HIPPO), we also compare separately lower- and upper-tropospheric layer-averaged methane from surface to z*=6 km and between z*=6 and 12 km. In each case, comparisons are made both directly and taking into account retrieval sensitivity using the averaging kernels. Where the independent data are height resolved, this is done by applying Eq. (12). In the case of GOSAT and TCCON (which only provide column information) this cannot be done, so instead we estimate the (smoothing) error in the IASI column average using the co-located MACC profile, given by the difference (cM-cMxI), where cM is the directly computed MACC layer average and cMxI is the result of Eq. (12) applied to the MACC profile. The smoothing error (cM-cMxG) of the GOSAT–TCCON profile can be similarly estimated, using the corresponding GOSAT–TCCON kernels and prior profiles. We then estimate what GOSAT–TCCON would be expected to observe, given the IASI measurement, accounting for the respective smoothing errors

Because the sensitivity of GOSAT (and TCCON) varies relatively little with height compared to IASI, there is very little difference in practice between results using cMxG in this equation or the uncorrected MACC-II GHG column average, cM.

Comparisons with IASI are made by interpolating MACC-II GHG data for a given day to the geographical locations of individual IASI observations and averaging both into 2.5∘×2.5∘ longitude–latitude bins for individual months. Column-averaged methane and layer averages between pressure levels corresponding to z*=6 and 12 km are considered.

Figure 6 shows seasonal mean distributions of the column averages from IASI and MACC-II GHG based on data processed between June 2009 and May 2010 and averaged into 7.5∘×5∘ longitude–latitude bins over 3-month intervals

This grid size is chosen to be appropriate for the comparisons to GOSAT as discussed in the following section.

There are regions over the tropical oceans in which IASI is lower than MACC-II GHG by ∼20 ppbv, especially in March–May. Similar patterns appear also in IASI–GOSAT comparisons. A low bias in IASI data could plausibly be explained by persistent low cloud cover (see Sect. 4 above); however, the geographical pattern of the bias is not fully consistent with this explanation (a similar bias is not present in other regions of known persistent low cloud cover). The bias could also be explained by the presence of slightly higher than expected tropospheric N2O mixing ratio in these areas, which might be supported by HIPPO observations (Kort, 2011).

Figures 7 and 8 compare the 0–6 and 6–12 km layer averages from IASI with MACC-II GHG. The IASI global distributions are seen to capture well methane source regions (e.g. South-East Asia, Amazon, central Africa) which feature prominently in the MACC-II GHG distribution, although the amplitudes of such regional enhancements retrieved by IASI are lower than MACC-II GHG, consistent with mixing ratios in source regions being highest nearest to the surface where IASI sensitivity is lowest. After applying IASI averaging kernels to MACC-II GHG, however, agreement is much improved, with differences comparable to those found for column-averaged mixing ratios. IASI observes the Australian methane source to be larger than MACC-II GHG, which could perhaps explain why methane values in Southern Hemisphere mid-latitudes are generally higher for IASI than for MACC-II GHG, particularly in summer. Methane distributions in the 6–12 km layer are markedly different to those in the 0–6 km layer for both IASI and MACC-II GHG. The pattern of land surface sources is reflected only weakly and the asymmetry between Northern and Southern hemispheres is markedly reduced. The most prominent feature in Northern Hemisphere summer and autumn is seen to be outflow from the South-East Asian monsoon, which is captured quite consistently by IASI and MACC-II GHG. In direct comparisons, MACC values are generally 10–20 ppbv lower than IASI. When averaging kernels are applied, which capture the influence on the retrieved 6–12 km layer average from the atmosphere both above and below, agreement is generally improved. However, a discrepancy is then revealed at high northern latitude, particularly in spring when MACC-II GHG values are ∼20 ppbv larger than IASI values. These differences must be related to differences in representing the stratospheric vertical profile and its contribution to the 6–12 km layer average in either IASI or MACC-II GHG

For MACC-II GHG, stratospheric 3-D structure is that specified by TM5 whereas, for IASI, it is an annual average zonal-mean cross section from TOMCAT, from which there will be systematic, seasonally dependent departures.

The evolution in time of these comparisons has been studied by averaging the monthly-binned IASI and MACC values from 2.5∘×2.5∘ latitude–longitude intervals into the regions illustrated in Fig. 9. Over land, these regions correspond to those used in the TRANSCOM model intercomparison exercise (from Fraser, 2013, based on Gurney, 2002). Time series of the column averages in these regions are shown in Fig. 10. Each panel compares IASI with MACC-II GHG (direct and after applying the averaging kernels). The caption in each panel gives the correlation coefficient (r), mean difference (m) and SD of the differences (s) between IASI and MACC-II GHG. Panels are organised in order of average latitude from north to south. IASI and MACC-II GHG clearly agree well on the secular trend in methane over the 8-year period of 2007–2015, and seasonal cycles also agree well. As noted previously, averaging kernels have most impact at high northern latitudes, significantly increasing correlation coefficients in that case. After applying the averaging kernels, correlation coefficients are all ≥0.85. Mean differences are ≤10 ppbv except in southern mid-latitudes, where IASI values are ∼20 ppbv higher than MACC-II GHG. SDs of the monthly, regional mean values are typically ∼3 ppbv (although these are somewhat larger at high northern latitudes). This is an order of magnitude lower than the typical ESD of ∼30 ppbv for an individual IASI sounding, but large in comparison to the standard error in the mean (since many thousands of individual IASI soundings contribute to each monthly regional mean).

Figures 11 and 12 show the corresponding time series for the 0–6 and 6–12 km z* layer averages. In the 0–6 km layer, the impact of vertical sensitivity is more pronounced, with IASI typically underestimating the true layer average, particularly at high northern latitudes and also in temperate Eurasia and other land regions where near-surface methane concentrations are particularly high. Much of these discrepancies are explained by the averaging kernels, leaving correlations between 0.7 (boreal Eurasia, where IASI sees more pronounces seasonal variations) and 0.94 (temperate North America/mid-latitudinal Atlantic). In many locations, the seasonal patterns at 6–12 km are quite different from those of the total and 0–6 km layer average. In particular, tropical regions have relatively weak seasonal cycles in this upper layer and this is captured by IASI in accordance with MACC-II GHG. In temperate Eurasia (affected strongly by the monsoon outflow) and mid-latitude Pacific, the opposite is true (stronger seasonal cycle in upper layer). In the North Atlantic, there is a pronounced phase shift in the seasonal cycle exhibited by the two layers, consistently seen in IASI and MACC (with or without kernels). These differences in seasonal cycles, together with the level of consistency between IASI and MACC, support the assertion that IASI is providing vertically resolved information. As with the column average, correlation coefficients are ∼0.9 after applying averaging kernels. The Arctic region shows the largest differences, with a quite distinct seasonal cycle evident in IASI compared to MACC (whether or not averaging kernels are applied). Throughout the tropics and southern latitudes, IASI tends to be 10–20 ppbv higher than MACC (after applying averaging kernels) in the upper layer. Biases in the lower layer are typically much smaller, except over Australia.

The approach described in Sect. 5.2 for MACC-II GHG is adapted here to account for the sparse spatial sampling of GOSAT, especially over the ocean where retrievals only exist in sun-glint geometry. The starting point is to obtain 2.5∘×2.5∘ longitude–latitude daily binned fields from GOSAT and IASI (daytime only retrievals); monthly mean fields are then accumulated from these daily files. In the IASI case, geographical sampling for each given day is restricted to only those grid cells for which GOSAT data also exist. The resulting monthly means are then further averaged to 7.5∘×5∘ longitude–latitude resolution over 3-month intervals. A 7.5∘ longitude resolution was chosen to be just sufficient to eliminate gaps that would otherwise appear between GOSAT orbit tracks in the resulting plots. The number of GOSAT or IASI retrievals in each of these bins is sufficiently large that the estimated random error on the mean value (based on the estimated error on individual retrievals) is well below 1 ppbv. According to Parker (2015), GOSAT retrievals have a bias (vs. TCCON) of around 4.8 ppbv.

Results are illustrated in Fig. 13. Differences in direct comparisons between GOSAT and IASI are seen to be broadly similar to those found between MACC-II GHG and IASI (Fig. 6), which is consistent with the agreement found between the same GOSAT and MACC-II GHG datasets in Parker (2015). Adjusting for the different vertical sensitivities of IASI and GOSAT (using MACC-II GHG) is seen to account for most of the systematic difference between the two at high northern latitudes in winter and spring. The IASI values adjusted for vertical sensitivity are seen to be lower by 10–20 ppbv over Arabia, North Africa and over tropical oceans. At southern mid-latitudes IASI is higher (by ∼ 10 ppbv) than GOSAT. This bias is about a factor of 2 smaller than that also seen in this region in the comparison to MACC-II GHG (from September to February), implying that MACC-II GHG is negatively biased with respect to both IASI and GOSAT in this region.

Here we compare IASI to all available TCCON data from version “GGG2014”. This includes data from the following sites: Armstrong Flight Research Center, Edwards (Iraci, 2014a), Ascension Island (Feist, 2014), Białystok (Deutscher, 2014), Bremen (Notholt, 2014), California Institute of Technology (Wennberg, 2014c), Darwin (Griffith, 2014a), Eureka (Strong, 2014), Four Corners (Dubey, 2014b), Garmisch (Sussmann, 2014), Indianapolis (Iraci, 2014b), Izana (Blumenstock, 2014), Jet Propulsion Laboratory (Wennberg, 2014a, e), Karlsruhe (Hase, 2014), Lamont (Wennberg, 2014d), Lauder (Sherlock, 2014a, b), Manaus (Dubey, 2014a), Orleans (Warneke, 2014), Paris (Te, 2014), Park Falls (Wennberg, 2014b), Réunion Island (De Maziere, 2014), Rikubetsu (Morino, 2014b), Saga (Shiomi, 2014), Sodankylä (Kivi, 2014), Tsukuba (Morino, 2014a) and Wollongong (Griffith, 2014b). For some stations, additional temporal coverage was given in the earlier GGG2012 release, and we exploit this here, using GGG2012 in periods when this is available but GGG2014 is not. Data for Ny-Ålesund were taken from the 2012 release. Locations of the TCCON stations used are indicated in Fig. 9.

IASI column-average data within 200 km of each TCCON station are averaged over a month for the days on which there are TCCON observations. Monthly averages are presented if there are at least 100 IASI individual observations and 10 TCCON observations contributing to each mean. Typically there are several thousand IASI observations contributing to each monthly mean, for which a standard error in the mean of ≤1 ppbv would be expected from typical ESDs on individual retrieved column averages.

Since TCCON does not provide profile information, we cannot directly account for the effect of the IASI vertical sensitivity but do so indirectly using MACC-II GHG as a transfer standard, as described in Sect. 5.1. In order to extend this correction beyond the end of 2012, we use the MACC-II GHG delayed mode analysis (“v10_an”), which is available from 2013 until mid-2014. This assimilates GOSAT retrievals (from the RemoTec proxy scheme of Schepers, 2012), in addition to the NOAA surface flask observations.

Time series of comparisons to TCCON stations are shown in Fig. 14. In the absence of any TCCON measurements in a month, the IASI time series is completed using the IASI monthly mean considering all days in the month (these points are shown for context/continuity in the plots but do not contribute to any derived statistics). As indicated in the panel headings, results from some stations (seen to sample broadly similar methane variations) are grouped together to make the time series in each panel more complete and to enable results to be presented in a more compact form.

The caption in each panel gives the correlation coefficient (r), mean difference (m) and SD of individual TCCON–IASI differences (s); two values are given for each quantity (separated by a slash), corresponding to the direct comparison of TCCON to IASI and the comparison to IASI after adjusting for vertical sensitivity (via MACC-II GHG profiles). Correlation coefficients before correction are 0.76–0.91 and generally improve after the adjustment. Correlation is only 0.65 (0.73 after correction) at Izana. Some of this degradation may be related to the high altitude of the station (2300 m) and/or its specific location (on Tenerife), which is subject to peculiar localised meteorological conditions and is in an area often affected by desert dust.

As might be expected, the correction generally raises the IASI values (as the tropospheric prior value is usually systematically low). The correction tends to have more impact in the north, although agreement with TCCON is not always improved. The correction usually reduces the SD in the difference between IASI and TCCON. After correction, the mean difference and SD are ≤10 ppbv in most cases, with the notable exception of the Four Corners site. Here IASI captures the seasonal cycle well but underestimates TCCON by ∼25 ppbv, even after correction. This anomaly is presumably explained by the fact that the Four Corners TCCON site is located close to an extremely strong methane source associated with coal mining and related activities (Kort, 2014), and so it samples localised methane variability represented neither by IASI data sampled over a circular region of radius 200 km nor by the MACC-II GHG data used in the adjustment.

Each panel in Fig. 14 also shows (as histograms along the x axis, referring to the right-hand y axis) the SDs of IASI and TCCON data in monthly bins. Grey bars show the SDs of all individual IASI retrievals in each monthly bin. These are generally higher than those for TCCON (shown in light red), reflecting the significant contribution of random errors to individual IASI retrievals. These SDs are comparable to the ESD of individual retrievals, as computed by the optimal estimation retrieval scheme (illustrated in Fig. 3). The histogram indicated by the thin black line, shows for each given month the SD of the set of the daily mean IASI values. Each daily mean is the average of all IASI observations within 200 km of the TCCON site. This quantity generally agrees rather well with the TCCON SD, reflecting the day-to-day variation of methane within each month as seen by the two sensors. Again, the exception here is Four Corners, where TCCON has a higher SD than IASI, supporting the hypothesis that this station samples methane variability which is substantial on a local scale.

Figure 15 summarises the TCCON comparisons for 2013 in a compact form, showing the individual monthly samples from TCCON and IASI as individual points, colour-coded by the site (or grouping of sites). Four Corners is excluded from this plot because of the sampling issue identified above. Error bars show the SDs of daily mean values in each monthly bin. The top-left panel plots TCCON against the retrieval prior, whose variation arises from meridional structure in the annual mean stratospheric distribution. The absence of any discernible correlation between prior and TCCON values clearly demonstrates that agreement between the retrieval and TCCON to be wholly dependent upon information provided by the IASI measurements. It also illustrates why IASI retrievals tend to be biased low at high northern latitudes, where measurement sensitivity is lowest and the prior contribution is therefore most influential. The top centre panel shows the direct IASI–TCCON scatter, which indicates a correlation coefficient of 0.92 and that IASI is systematically lower by ∼11 ppbv. The top right panel shows the scatter between TCCON and the adjusted IASI retrieval, in which the correlation coefficient is increased to 0.95 and the negative IASI bias is reduced to ∼1.5 ppbv. The SD in the monthly IASI–TCCON differences is ∼10 ppbv.

We co-locate IASI and HIPPO data by selecting, for each HIPPO profile, all IASI observations within 200 km and 6 h (taking the mean latitude, longitude and time of the aircraft measurement). We then compare each HIPPO profile to the mean of the associated IASI observations. We also compute the SD of individual IASI soundings. The number of IASI samples in each mean profile is variable (typically in the range 1–20) due to cloud occurrence and latitude (more samples are found at high latitude where IASI's 2200 km swaths overlap). Figure 16 shows the comparison for HIPPO campaign 5, which is particularly interesting for our purposes as variations in the upper- and lower-tropospheric layers (represented here by 0–6 and 6–12 km layer averages) are quite distinct. These differences are very poorly captured by the IASI prior but relatively well captured by the retrieval; illustrating that IASI can effectively resolve two independent layers centred in the lower and upper troposphere. There is a particularly pronounced meridional structure in the 6–12 km layer between profile index 30–40, which is captured by IASI and clearly present in both HIPPO and the part of the field which has been infilled with MACC-II GHG data.

Figure 17 summarises (in a similar format analogous to figures of Alvaredo, 2015) the level of agreement between IASI and HIPPO, binning into 10∘ latitude intervals, the differences between the individual IASI and HIPPO matches, considering all five campaigns. The figure shows the mean difference (HIPPO–IASI) in each bin (with and without applying IASI averaging kernels), together with the SD about the mean of the differences between the individual IASI profiles and the HIPPO profile associated to them. Each HIPPO profile is only used once in accumulating the statistics: SDs are computed first for all IASI profiles which match individual HIPPO profiles, then these are accumulated over all HIPPO profiles in a given latitude bin. For comparison, the mean ESD of the IASI observations is also shown. The number of samples in each bin is typical several hundred, so the standard error in the mean value is almost always <1 ppbv (whether estimated from the predicted retrieval error or the SD in the individual profile differences). Differences larger than this are therefore indicative of statistically significant bias between IASI and HIPPO. Assuming HIPPO to be correct, biases are most likely either from IASI retrieval error or the representativity of the HIPPO sample of the spatial area sampled by the selected IASI observations. The following points are evident. (1) After applying averaging kernels, the mean bias is -1.5 ppbv (column average), 3.7 ppbv (0–6 km layer average) and 0.24 ppbv (6–12 km layer average). As previously noted, averaging kernels have most impact in the Northern Hemisphere (where the prior methane is biased particularly low). (2) The SD in the difference is similar whether or not averaging kernels are applied and is almost always slightly smaller (21–41 ppbv, depending on layer) than the reported ESD (27–47 ppbv) (summaries of the SDs are given for each layer separately in the caption in each panel of Fig. 17). This is not surprising as the ESD includes significant smoothing error, which will not manifest entirely as (quasi) random variability in the agreement between IASI and HIPPO. (3) There is some latitude dependence in the bias, particularly in the lower-tropospheric layer (still mainly within ±20 ppbv). In particular, IASI seems to underestimate in the tropics. This behaviour is similar to that found between IASI and GOSAT column averages over the tropical oceans, as noted in Sect. 5.3.