Satellite measurements play an increasingly important role in the study of atmospheric ammonia (

Atmospheric ammonia (

Satellite measurements of

The first version of the IASI-

Returning to ANNI-

The IASI

Here, we give a brief overview of the ANNI algorithm and refer to the previously cited papers on the

Summary of the main quantities and associated symbols that are used in the AVK formalism.

The retrieval consists of two independent computational steps. The first one characterizes the

The second part of the algorithm relies on a neural network to link the measured HRIs to estimates

The general AVK framework that we introduce below bears a lot of similarity to the total column AVK formalism

One key element on which the total column AVK formalism of

Let

Numerical demonstration of the linearity and additivity of the HRI as a function of a change in partial column. In the blue and green scenario,

For this scene, we now introduce an additional trace amount

The HRI is by definition a linear combination of spectral channels

To make the link with Eq. (

Finally, introducing

Equation (

A priori vertical profile

The quantities

Two example AVKs for an

It is instructive to compare the total column averaging kernel, as defined above, with the one arising in optimal estimation retrievals

The first difference is the role of the a priori. For the total column retrieval, the a priori fixes only the vertical profile shape, while for the optimal estimation retrieval, the a priori affects both the vertical profile shape and the retrieved value at each altitude separately. Equation (

The second important difference relates to the fact that a vertical profile is retrieved in the optimal estimation type retrievals. The rows of

There are two alternative ways in which averaging kernels can be exploited to remove the impact of the vertical profile assumption of the retrieval

Let

Rather than altering the modelled column, an attractive alternative is to alter the retrieved column to use instead of the a priori vertical profile, the modelled profile (see

Both methods can be summarized as

Equations (

AVK normalization factors (

In the ANNI retrieval formalism, the total scaling factor

The formulas provided above are exact in the linear limit, but for large columns,

The necessity of using normalization factors follows from the fact that in the non-linear regime, the

The generalized error covariance matrix

The distribution of the eigenvalues of the covariance matrix used for the

Eigenvalue spectrum of the covariance matrix

However, small changes to the instrument calibration or post-processing can alter the contribution of these directions in the IASI spectra, and because they carry such a large weight in the HRI, they can affect its value considerably. This explains why the HRI in the past has been found to be very sensitive to such changes

After the pseudoinverse was implemented, an unexpected change was observed in the value of the HRIs on spectra from the period on which the covariance matrix was built. It turns out that while the scalar product of observed IASI spectra with the eigenvectors corresponding to the lowest eigenvalues

As the mean spectrum and covariance matrix that are used for the HRI are calculated from spectra measured within 1 reference year (2013), long-term changes in atmospheric composition that affect the spectral region of interest can have unwanted effects on the HRI. This was first noted in

An alternative approach is to account directly for the effects of CO

Monthly average HRI time series over 10 remote regions for the three IASI instruments separately. The top panel shows the uncorrected time series, and the other panels, from top to bottom, show the effects of the corrections that are applied consecutively.

The ERA5 model output replaces satisfactorily the IASI L2 for all input parameters, except for the surface temperature and cloud cover. These are spatially and temporally too variable for model output to be representative for an IASI footprint at a given time. All previous reanalysed ANNI-

The stability of the HRI was evaluated over 10 remote regions where only background columns of

A detailed analysis was made of this time series to detect offsets between the different instruments and shifts that coincide with known changes in the IASI L1C data. The largest of these shifts is the offset of 0.11 seen between IASI-C and the two other instruments. Small offsets in the HRI time series of IASI-A were found in 2010, 2015 and 2017, and in the HRI of IASI-B in 2015. For each of these, offset corrections were calculated in the range of 0.01–0.03. Thanks to the pseudoinverse, their magnitude is drastically reduced. Previously, offsets as large as 0.6 were observed. The resulting corrected time series is shown in the third panel. This time series is temporally stable and shows an excellent consistency between the three instruments, but it exhibits a weak seasonal cycle, likely due to the combined effect of seasonal changes in the concentrations of

An

An additional change in the setup of the HRI concerns the choice of the spectra used for determining the mean background spectra and its associated covariance matrix. As before

Since ANNI v2

A final series of changes concern the network architecture and training database. In previous versions, separate neural networks were employed for the retrieval over land and ocean. These networks were trained respectively with Gaussian a priori profiles peaking at the surface, and a more general one, with profiles peaking at different altitudes. However, a careful comparison showed that the more general network performed as well as the network trained specifically for profiles peaking at the surface. For this reason, only one network was trained for version 4, for a priori profiles peaking at altitudes

As outlined before, v4 has an improved temporal consistency compared with v3. In this section, we provide a short assessment of the new

Comparison between retrieved columns with ANNI v3 and v4 for all morning observations of 17 June 2015.

Comparison between the

The most obvious remaining artefact in the v4 distribution concerns the continuity of the land–sea transitions. While they are reasonable for some regions of outflow (Gulf of Mexico, Mediterranean Sea), off the west coast of Africa, over the Arabian Sea, Gulf of Bengal or Yellow Sea, the transition is too abrupt to be realistic. The origin of this problem is that different

Given the low bias in ANNI

For the optimal estimation retrieval, the Atmosphit forward and inverse model was used

For the comparison, 2 days were selected, one over Europe and one over North America, with relatively high

The last detailed global validation of the IASI

In previous ANNI versions, an estimated uncertainty

In the ANNI retrieval framework, the input parameters include the skin temperature, the surface pressure, the HRI, the surface emissivity, the zenith angle, the width and the peak of the Gaussian vertical

Comparison between ANNI v4

In total, we report four types of uncertainty for each observation: random or systematic, and with or without the vertical profile uncertainty included. Reporting random and systematic uncertainties separately is a generally recommended practice

Random and systematic uncertainties can be combined and averaged in different ways according to the needs of the user. In particular, for a given measurement

As most input parameters come without an uncertainty budget, let alone covariances, we made best-effort estimates of the co(variance) based on the limited information that is available. For now, the same (co)variances were used for the near-real time as for the reanalysed

The (co)variances, summarized in Table

By definition, the random uncertainty on the HRI equals one. We estimate a systematic uncertainty of

Random and systematic uncertainties were set to 1.5 and 0.5 K respectively. These values are in line with the difference between the IASI L2 skin temperature product and the dedicated neural network used for the reanalysis product of ANNI (see Sect.

For emissivity, which originates from the monthly climatology of

Variances were set based on validation results of the IASI level 2

Relying again on the IASI level 2 validation report

A random and systematic uncertainty of 500 and 250 Pa was used.

The uncertainties related to the

Estimated random and systematic uncertainties of the input parameters.

It is useful, remembering the general form

Illustration of the absolute (left) and relative (right) components of the retrieval uncertainty. The top panels illustrate their dependence on thermal contrast, the bottom panels show the normalized count. Data in this plot originate from IASI-B observations on 15 January, April, July and October 2021, morning overpass, land only and between 60

The first term is in the optically thin limit independent of the HRI and thus the column, and solely depends on the scene conditions:

The absolute uncertainty contribution is illustrated in the left panels of Fig.

The term

In this paper, we presented v4 of the

The IASI-

LC led the research, conceptualized the ANNI retrieval changes and wrote the first version of the manuscript. BF, LC, JH-L, DH, SW and MVD contributed to the code or data processing. LC, MVD, TDG and LN prepared the figures. TDG, MVD and LC implemented the corrections presented in Sect.

The contact author has declared that none of the authors has any competing interests.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

Lieven Clarisse is grateful to Tim Hultberg for pointing out the necessity of using a pseudoinverse for the calculation of the HRI.

The research was co-funded by the Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex HIRS), the Air Liquide Foundation (TAPIR), EUMETSAT (AC-SAF) and ESA (CCI

This paper was edited by Alyn Lambert and reviewed by Daven Henze and one anonymous referee.