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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 21 — Oct. 21, 2013
  • pp: 24753–24769
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Partially scanned interferogram methodology applied to IASI for the retrieval of CO, CO2, CH4 and N2O

Giuseppe Grieco, Guido Masiello, Marco Matricardi, and Carmine Serio  »View Author Affiliations


Optics Express, Vol. 21, Issue 21, pp. 24753-24769 (2013)
http://dx.doi.org/10.1364/OE.21.024753


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Abstract

The technique of partially scanned interferograms is applied to the retrieval of trace gases from Infrared Atmospheric Sounding Interferometer (IASI) observations. For the specific case of CO, CO2, CH4 and N2O, we show that this methodology allows us to retrieve trace gases column abundances at an unprecedented accuracy at the level of the single IASI footprint. The technique consists in transforming the IASI spectra back to the interferogram domain where we identify small regions that are mostly sensitive to single gas species. The retrieval is then performed by directly applying Least Squares estimation to these small segments of interferometric radiances. One of the main advantages of the technique is that it allows the efficient use of the information contained in all the IASI channels that are available in the absorption bands of a specific gas species. The retrieval technique has been applied to IASI radiances measured over the Mediterranean sea during the month of July 2010, one of the hottest months on record. Results have been validated against ground-based measurements. We have also carried out a comparison with Atmospheric Infrared Radiometer Sounder data and IASI retrievals obtained with usual variational approaches in the spectral domain.

© 2013 OSA

1. Introduction

Fourier Transform Spectroscopy with Partially Scanned Interferograms (PSI) for the retrieval of trace gases from Infrared Atmospheric Sounding Interferometer (IASI [1

1. F. Hilton, F., R. Armante, R., T. August, T., C. Barnet, A. Bouchard, C. Camy-Peyret, V. Capelle, L. Clarisse, C. Clerbaux, P.-F. Coheur, A. Collard, C. Crevoisier, G. Dufour, D. Edwards, F. Faijan, N. Fourrié, A. Gambacorta, M. Goldberg, V. Guidard, D. Hurtmans, S. Illingworth, N. Jacquinet-Husson, T. Kerzenmacher, G. Klaes, L. Lavanant, G. Masiello, M. Matricardi, A. McNally, S. Newman, E. Pavelin, S. Payan, E. Pequignot, S. Péyridieu, T. Phulpin, J. Remedios, P. Schlüssel, P., C. Serio, L. Strow, C. Stubenrauch, J. Taylor, D. Tobin, W. Wolf, and D. Zhou, “Hyperspectral Earth Observation from IASI: four years of accomplishments,” B. Am. Meteorol. Soc. 93, 347–370 (2012), [CrossRef] .

]) observations has been recently investigated by [2

2. G. Grieco, M. Masiello, and C. Serio, “Interferometric vs spectral IASI radiances: Effective data-reduction approaches for the satellite sounding of atmospheric thermodynamical parameters,” Remote Sens. 2(10), 2323–2346 (2010), . [CrossRef]

5

5. C. Serio, G. Masiello, and G. Grieco, “Fourier transform spectroscopy with partially scanned interferograms as a tool to retrieve atmospheric gases concentrations from high spectral resolution satellite observations - Methodological aspects and application to IASI,” in: Atmospheric Model Applications, Dr. Ismail Yucel, (Ed.) InTech, 248–272 (2012), doi: [CrossRef] . Available online from: http://www.intechopen.com/.

]. PSI is used to compute the difference between spectra of atmospheric radiances at two different spectral resolutions. This differentiation of the spectrum acts as a high pass filter, which eliminates from the spectrum the low pass components, which include the surface emission in atmospheric window spectral regions. This property is particularly useful for nadir-looking instruments, where we need to separate the atmospheric windows emission component, which conveys information about atmospheric gases in the lower troposphere, from the surface emission.

PSI was first introduced by [6

6. T. G. Kyle, “Temperature soundings with partially scanned interferograms,” Appl. Opt. 16(2), 326–332 (1977). [CrossRef] [PubMed]

] for the retrieval of atmospheric parameters. Later on, [7

7. H. W. Goldstein, R. N. Grenda, M. H. Bortner, and R. Dick, “CIMATS: a correlation interferometer for the measurements of atmospheric trace species,” Proceedings of the 4th Joint Conference on Sensing of Environmental Pollutants. American Chemical Society, 586–589 (1978).

] proposed a correlation interferometer for the observation of atmospheric trace gases, whereas [8

8. W. L. Smith, H. B. Howell, and H. M. Woolf, “The use of interferometric radiance measurements for the sounding the atmosphere,” J. Atmos. Sci. 36, 566–575 (1979), doi: [CrossRef]

] further analysed and applied the PSI methodology to the direct inversion of small segments of interferometric radiances for the purpose of temperature retrieval.

When we apply PSI to the retrieval of total column amounts of trace gas species we exploit what is known as the multiplex or Fellgett gain of Fourier transform spectrometers. Each single interferogram sample is a linear combination of the entire set of spectral radiances forming the original spectrum. The peak contribution to top of atmosphere radiances comes from altitudes that change with wave number. Consequently, in the spectral domain we need to sample a relatively large portion of the spectrum if we want to retrieve information for an atmospheric column ranging from the Earth’s surface to the top of the atmosphere. Conversely, because in the interferogram domain each data point conveys information from any region of the spectrum, the vertical portion of the atmospheric column covered by any individual interferogram sample is much larger than the portion covered by any spectral sample at a given wave number. In addition, the IASI spectral sampling of 0.25 cm−1 allows us to resolve the specific periodic patterns that characterize the spectra of linear molecules such as CO2, CO and N2O. Due to the properties of the Fourier transform, these periodic patterns in the spectra result in well defined sharp signatures in the interferogram. This means that a small interval of the interferogram domain contains most of the information contained in the whole radiance spectrum. For instance, CO2 absorption lines are typically characterized by a regular spacing of ≈ 0.8 cm−1, this results in a sharp resonance feature in the interferogram. The resonance can be found at an optical path difference equal to the inverse of two times the spacing, i.e. ≈ 0.63 cm.

Following the PSI approach, [3

3. G. Grieco, G. Masiello, C. Serio, R. L. Jones, and M. I. Mead, “Infrared Atmospheric Sounding Interferometer correlation interferometry for the retrieval of atmospheric gases: the case of H2O and CO2,” Appl. Opt. 50, 4516–4528 (2011), . [CrossRef] [PubMed]

5

5. C. Serio, G. Masiello, and G. Grieco, “Fourier transform spectroscopy with partially scanned interferograms as a tool to retrieve atmospheric gases concentrations from high spectral resolution satellite observations - Methodological aspects and application to IASI,” in: Atmospheric Model Applications, Dr. Ismail Yucel, (Ed.) InTech, 248–272 (2012), doi: [CrossRef] . Available online from: http://www.intechopen.com/.

] developed an estimation statistical method, which is based on a polynomial regression between interferogram radiance and the total column amount (TCA) of the trace gas. This parametric approach is computationally fast because does not require the calculation of Jacobian derivatives and allowed us to achieve accuracies, at the level of the single IASI observation (which has a footprint of 12 km radius at nadir), of about ±9 ppmv for CO2, ≈ ±16 ppbv for CO, ≈ ±0.1 ppmv for CH4 and ≈ ±30 ppbv for N2O [3

3. G. Grieco, G. Masiello, C. Serio, R. L. Jones, and M. I. Mead, “Infrared Atmospheric Sounding Interferometer correlation interferometry for the retrieval of atmospheric gases: the case of H2O and CO2,” Appl. Opt. 50, 4516–4528 (2011), . [CrossRef] [PubMed]

, 5

5. C. Serio, G. Masiello, and G. Grieco, “Fourier transform spectroscopy with partially scanned interferograms as a tool to retrieve atmospheric gases concentrations from high spectral resolution satellite observations - Methodological aspects and application to IASI,” in: Atmospheric Model Applications, Dr. Ismail Yucel, (Ed.) InTech, 248–272 (2012), doi: [CrossRef] . Available online from: http://www.intechopen.com/.

].

The limb-sounding of upper and lower stratosphere trace species is amongst the main goals of many satellite missions, e.g. Atmospheric Chemistry Experiment [11], MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) of the European Space Agency [12]. The objective of this study is to apply our methodology to the retrieval of trace gases from IASI, which is a nadir-looking instrument, with improved accuracy and horizontal spatial resolution.

The subject of remote sensing of atmospheric minor and trace gases from nadir looking instruments on board polar satellites is not a new subject. The sensors used for this scope include the Japanese IMG (Interferometric Monitoring of Greenhouse Gases) [13

13. A. M. Lubrano, G. Masiello, M. Matricradi, C. Serio, and V. Cuomo, “Retrieving N2O from nadir-viewing infrared spectrometers,” Tellus B 56(3), 249–261 (2004), doi: [CrossRef] .

], the American AIRS (Atmospheric Infrared Radiometer Sounder) [14

14. M. T. Chahine, C. Barnet, E. T. Olsen, L. Chen, and E. Maddy, “On the determination of atmospheric minor gases by the method of vanishing partial derivatives with application to CO2,” Geophys. Res. Lett. 32, L22803 (2005), doi: [CrossRef] .

16

16. W. W. Mc Millan, C. Barnet, L. Strow, M. T. Chahine, M. L. Mc Court, J. X. Warner, P. C. Novelli, S. Korontzi, E. S. Maddy, and S. Datta, “Daily global maps of carbon monoxide from NASA’s Atmospheric Infrared Sounder,” Geophys. Res. Lett. 32, L11801 (2005), doi: [CrossRef] .

], the European IASI [3

3. G. Grieco, G. Masiello, C. Serio, R. L. Jones, and M. I. Mead, “Infrared Atmospheric Sounding Interferometer correlation interferometry for the retrieval of atmospheric gases: the case of H2O and CO2,” Appl. Opt. 50, 4516–4528 (2011), . [CrossRef] [PubMed]

, 17

17. C. Crevoisier, A. Chédin, H. Matsueda, T. Machida, R. Armante, and N. A. Scott, “First year of upper tropospheric integrated content of CO2from IASI hyperspectral infrared observations,” Atmos. Chem. Phys. 9, 4797–4810 (2009), doi: [CrossRef] .

24

24. T. Kerzenmacher, B. Dils, N. Kumps, T. Blumenstock, C. Clerbaux, P.-F. Coheur, P. Demoulin, O. Garcia, M. George, D. W. T. Griffith, F. Hase, J. Hadji-Lazaro, D. Hurtmans, N. Jones, E. Mahieu, J. Notholt, C. Paton-Walsh, U. Raffalski, T. Ridder, M. Schneider, C. Servais, and M. De Maziere, “Validation of IASI FORLI carbon monoxide retrievals using FTIR data from NDACC,” Atmos. Meas. Tech. 5, 2751–2761 (2012), doi: [CrossRef] .

] and the Japanese GOSAT (Greenhouse Gases Observing Satellite) [25

25. D. Wunch, P. O. Wennberg, G. C. Toon, B. J. Connor, B. Fisher, G. B. Osterman, C. Frankenberg, L. Mandrake, C. O’Dell, P. Ahonen, S. C. Biraud, R. Castano, N. Cressie, D. Crisp, N. M. Deutscher, A. Eldering, M. L. Fisher, D. W. T. Griffith, M. Gunson, P. Heikkinen, G. Keppel-Aleks, E. Kyro, R. Lindenmaier, R. Macatangay, J. Mendonca, J. Messerschmidt, C. E. Miller, I. Morino, J. Notholt, F. A. Oyafuso, M. Rettinger, J. Robinson, C. M. Roehl, R. J. Salawitch, V. Sherlock, K. Strong, R. Sussmann, T. Tanaka, D. R. Thompson, O. Uchino, T. Warneke, and S. C. Wofsy, “A method for evaluating bias in global measurements of CO2 total columns from space,” Atmos. Chem. Phys. 11, 12317–12337 (2011), doi: [CrossRef] .

].

In this paper we offer a different perspective focusing on the use of the PSI methodology and documenting its capability of achieving columnar contents of trace gases with an unprecedented accuracy. The PSI methodology has been applied to IASI data recorded during July 2010 over the Mediterranean sea. The July 2010 period has been selected because of the weather pattern over the Mediterranean, which has been characterized by above normal surface temperatures associated to a relatively high frequency of blocking days. Likewise, a particularly long blocking event has been experienced over Western Russia that has lead to the occurrence of anomalously high temperatures over this region. Thus, we have decided to use our methodology to investigate whether the quiescent weather and the high temperatures could have resulted in any detectable behaviour in the concentration of the trace gases considered in this paper.

The state vector (surface temperature, temperature, H2O and O3 atmospheric profiles), which is needed to initialize the estimation procedure for the total column amount of traces gases, has been retrieved directly from IASI radiances, through the forward/inverse package that we call φ-IASI. The mathematical aspects and validation of φ-IASI have been discussed in detail in various publications [9

9. U. Amato, G. Masiello, C. Serio, and M. Viggiano, “The σ-IASI code for the calculation of infrared atmospheric radiance and its derivatives,” Environ. Model. Software 17(7), 651–667 (2002), doi: [CrossRef] .

, 10

10. A. Carissimo, I. De Feis, and C. Serio, “The physical retrieval methodology for IASI: the δ-IASI code,” Environ. Model. Software , 20(9), 1111–1126 (2005), doi: [CrossRef] .

, 26

26. U. Amato, M. F. Carfora, V. Cuomo, and C. Serio, “Objective algorithms for the aerosol problem,” Appl. Opt. 34(24), 5442–5452 (1995), doi: [CrossRef] . [PubMed]

32

32. G. Masiello and C. Serio, “Simultaneous physical retrieval of surface emissivity spectrum and atmospheric parameters from infrared atmospheric sounder interferometer spectral radiances,” Appl. Opt. 52(11), 2428–2446 (2013), doi: [CrossRef] . [PubMed]

]. Ancillary information used to constrain the retrieval of the atmospheric state vector has been obtained from the European Centre for Medium range Weather Forecasts (ECMWF) analyses, collocated in time and space with the IASI soundings.

The paper is organized as follows. In section 2 we describe the IASI data and the new aspects of our forward model σ-IASI, whereas the description of the basic methods used in the PSI approach is presented in sections 3. The retrieval methodology for CO2, CO, CH4 and N2O is discussed in sections 3.1 and 3.2. Section 3.1 covers the case of CO2 and is mainly intended to give an example of the overall retrieval methodology. Results obtained from the use of IASI data are presented for the aforementioned Mediterranean basin case study in section 4, where we also show a comparison to AIRS data and IASI retrievals obtained with variational approaches in the spectral domain [23

23. D. Hurtmans, P.-F. Coheur, C. Wespes, L. Clarisse, O. Scharf, C. Clerbaux, J. Hadji-Lazaro, M. George, and S. Turquety, “FORLI radiative transfer and retrieval code for IASI,” J. Quant. Spectroscop. Ra. 113(11), 1391–1408 (2012), doi: [CrossRef] .

, 24

24. T. Kerzenmacher, B. Dils, N. Kumps, T. Blumenstock, C. Clerbaux, P.-F. Coheur, P. Demoulin, O. Garcia, M. George, D. W. T. Griffith, F. Hase, J. Hadji-Lazaro, D. Hurtmans, N. Jones, E. Mahieu, J. Notholt, C. Paton-Walsh, U. Raffalski, T. Ridder, M. Schneider, C. Servais, and M. De Maziere, “Validation of IASI FORLI carbon monoxide retrievals using FTIR data from NDACC,” Atmos. Meas. Tech. 5, 2751–2761 (2012), doi: [CrossRef] .

]. Finally, conclusions are drawn in section 5.

2. Data

IASI observations used in this work have been recorded during the entire month of July 2010 over the Mediterranean sea. We concentrate on clear sky, sea surface soundings. However, the retrieval methodology can be applied to any kind of surface.

IASI, which is flying on board the Metop-A/B (Meteorological Operational Satellite) platforms, has been developed in France by the Centre National d’Etudes Spatiales (CNES) and is the first of three satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) European Polar System (EPS). The IASI instrument [1

1. F. Hilton, F., R. Armante, R., T. August, T., C. Barnet, A. Bouchard, C. Camy-Peyret, V. Capelle, L. Clarisse, C. Clerbaux, P.-F. Coheur, A. Collard, C. Crevoisier, G. Dufour, D. Edwards, F. Faijan, N. Fourrié, A. Gambacorta, M. Goldberg, V. Guidard, D. Hurtmans, S. Illingworth, N. Jacquinet-Husson, T. Kerzenmacher, G. Klaes, L. Lavanant, G. Masiello, M. Matricardi, A. McNally, S. Newman, E. Pavelin, S. Payan, E. Pequignot, S. Péyridieu, T. Phulpin, J. Remedios, P. Schlüssel, P., C. Serio, L. Strow, C. Stubenrauch, J. Taylor, D. Tobin, W. Wolf, and D. Zhou, “Hyperspectral Earth Observation from IASI: four years of accomplishments,” B. Am. Meteorol. Soc. 93, 347–370 (2012), [CrossRef] .

] covers the spectral range from 645 to 2760 cm−1 (3.62 to 15.50 μm), with a sampling interval Δσ = 0.25 cm−1. Thus, each single spectrum is formed by 8461 data points or channels. The calibrated IASI interferogram extends from 0 to a maximum optical path difference of 2 cm. IASI is an across track scanning system with a swath range of ±48°20′. The scan movement is symmetric with respect to the nadir direction and results in a scan line with thirty footprint positions on the Earth’s surface. The effective field of view (EFOV) is the useful field of view at each scan position or footprint. Each footprint consists of a 2 × 2 matrix of instantaneous fields of view (IFOV). Any single IFOV has a diameter of 14.65 mrad, which corresponds to a ground resolution of 12 km at nadir assuming a satellite altitude of 819 km. The 2 × 2 matrix is centred on the viewing direction.

The IASI spectra were screened for cloud contamination using the IASI stand alone cloud detection algorithm developed by the authors [28

28. G. Grieco, G. Masiello, M. Matricardi, C. Serio, D. Summa, and V. Cuomo, “Demonstration and validation of the φ-IASI inversion scheme with NAST-I data,” Q. J. R. Meteorol. Soc. 133(s3), 217–232 (2007), doi: [CrossRef] .

31

31. G. Masiello, C. Serio, and V. Cuomo, “Exploiting quartz spectral signature for the detection of cloud-affected satellite infrared observations over African desert areas,” Appl. Opt. 43(11), 2305–2315 (2004), doi: [CrossRef] . [PubMed]

]. After cloud detection, a total of 34719 IASI spectra were selected.

For comparison purposes we have utilized AIRS level 2 CO2 products (AIRS version 5) for the same area and dates [33]. The data are not gridded and their horizontal spatial resolution is 90 km, which is coarser than the IASI 12 km resolution. For the case of CO, we have also used IASI data utilizing a different retrieval scheme. This is the scheme developed at LATMOS-ULB (Laboratoire d’Optique Atmosphérique - Université libre de Bruxelles) [23

23. D. Hurtmans, P.-F. Coheur, C. Wespes, L. Clarisse, O. Scharf, C. Clerbaux, J. Hadji-Lazaro, M. George, and S. Turquety, “FORLI radiative transfer and retrieval code for IASI,” J. Quant. Spectroscop. Ra. 113(11), 1391–1408 (2012), doi: [CrossRef] .

, 24

24. T. Kerzenmacher, B. Dils, N. Kumps, T. Blumenstock, C. Clerbaux, P.-F. Coheur, P. Demoulin, O. Garcia, M. George, D. W. T. Griffith, F. Hase, J. Hadji-Lazaro, D. Hurtmans, N. Jones, E. Mahieu, J. Notholt, C. Paton-Walsh, U. Raffalski, T. Ridder, M. Schneider, C. Servais, and M. De Maziere, “Validation of IASI FORLI carbon monoxide retrievals using FTIR data from NDACC,” Atmos. Meas. Tech. 5, 2751–2761 (2012), doi: [CrossRef] .

]. LATMOS-ULB IASI Level 2 CO data for the whole month of July 2010 over the Mediterranean basin have been downloaded from the Atmospheric Chemistry Data Center [34] site.

Sea level July averages of CO2, CH4 and N2O concentrations obtained from a number of Global Atmosphere Watch (GAW) (World Meteorological Organization) WMO stations located in the Mediterranean basin have been also used.

To specify the water vapour and ozone background vectors and covariance matrices utilized in the inversion of the IASI spectra, we have used ECMWF atmospheric analysis fields at 00:00, 06:00, 12:00 and 18:00 UTC during July 2010. The version of the ECMWF model which was operational at that time was characterized by a vertical discretization of the atmosphere into 91 pressure levels with the model top at 0.01 hPa and a horizontal truncation of T1279. This truncation corresponds to a grid spacing of about 16km or, equivalently, to a horizontal grid box of 0.141° × 0.141°. The vertical coordinates of the model are surfaces of constant pressure in the upper stratosphere and lower mesosphere whereas they follow the earth’s surface in the lower and mid-troposphere. In terms of vertical resolution (measured in geometric height), this is highest in the planetary boundary and lowest in the stratosphere and lower mesosphere. The analysis fields were extracted from the ECMWF archive at the full T1279 resolution, interpolated to a grid of points with a separation of 0.3° × 0.3° and then collocated in both space and time to the IASI soundings used in this study.

For T (temperature profile) and Ts (surface temperature) our IASI retrieval algorithm does not use the ECMWF analyses. These two parameters are first directly estimated from IASI observations with an Empirical Orthogonal Function linear regression procedure and, finally, simultaneously retrieved with the other parameters through iterations with a physical inverse scheme. This final physical retrieval step retrieves simultaneously the state vector (T, Q, O, Ts, ε), where Q is the water vapour mixing ratio profile (units of g/kg), O is the ozone mixing ratio profile (units of ppmv) and ε the surface emissivity spectrum. The most recent version of the scheme can be found in [32

32. G. Masiello and C. Serio, “Simultaneous physical retrieval of surface emissivity spectrum and atmospheric parameters from infrared atmospheric sounder interferometer spectral radiances,” Appl. Opt. 52(11), 2428–2446 (2013), doi: [CrossRef] . [PubMed]

]. Trace gases are not retrieved at this stage, their retrieval is performed sequentially using the PSI scheme utilizing the state vector (T, Q, O, Ts, ε) previously estimated from IASI.

2.1. Forward model

Radiative transfer calculations are based on our σ-IASI model [9

9. U. Amato, G. Masiello, C. Serio, and M. Viggiano, “The σ-IASI code for the calculation of infrared atmospheric radiance and its derivatives,” Environ. Model. Software 17(7), 651–667 (2002), doi: [CrossRef] .

]. The σ-IASI package consists of a monochromatic radiative transfer computer code designed for the fast computation of spectral radiances and their derivatives (Jacobians) with respect to a set of geophysical parameters. In its original formulation σ-IASI uses a vertical grid of sixty atmospheric layers bounded by pressure levels ranging from 1050 to 0.005 hPa. The model is based on a look-up table of optical depths computed using the line-by-line code LBLRTM [35

35. S. A. Clough, M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, “Atmospheric radiative transfer modeling: a summary of the AER codes,” J. Quant. Spectroscop. Ra. 91(2), 233–244 (2005), doi: [CrossRef] .

]. The current optical depth look-up table is based on LBLRTM version 12 released on January 2011 in conjunction with the water vapour continuum model MT CKD version 2.5.2 and the line file aer v3.0 [36]. The σ-IASI model parameterizes the monochromatic optical depths using a second order polynomial. At a given pressure, the optical depth for the generic i-th molecule, is computed according to
χσ,i=qi(cσ,0,i+cσ,1,iT+cσ,2,iT2)
(1)
where T is the temperature, qi the molecule concentration and cσ,j,i with j = 0, 1, 2, are the regression coefficients which are obtained using a Least Squares procedure and are themselves stored in the optical depth look-up-table. For water vapour, unlike other gases, in order to take into account effects depending on the gas concentration, such as self-broadening, a bi-dimensional look-up-table is used [37

37. G. Masiello and C. Serio, “An effective water vapor self-broadening scheme for look-up-table-based radiative transfer,” in Remote Sensing of Clouds and the Atmosphere VII. Proceedings of the SPIE, Volume 4882. (K. P. Schaefer, O. Lado-Bordowsky, A. Comeron, and R. H. Picard, eds.), 52–61 (2003), doi: [CrossRef] .

]. Thus, for water vapour, identified with i = 1, the optical depths is calculated according to
χσ,1=q1(cσ,0,1+cσ,1,1T+cσ,2,1T2+cσ,3,1q1)
(2)
The subscript σ indicates the monochromatic quantities. In the original version of σ-IASI [9

9. U. Amato, G. Masiello, C. Serio, and M. Viggiano, “The σ-IASI code for the calculation of infrared atmospheric radiance and its derivatives,” Environ. Model. Software 17(7), 651–667 (2002), doi: [CrossRef] .

], the monochromatic optical depth was computed and parameterized at the spectral resolution of 10−4 cm−1. A recent analysis of the trade-off between accuracy and computational efficiency of the code has shown that this spectral sampling can be reduced without sacrificing the robustness and accuracy of the inverse scheme. The analysis has shown that for IASI, the optical depth can be averaged and re-sampled at the lower rate of 10−2 cm−1. This is referred to as lut-coarse (look-up-table-coarse) sampling in contrast to the lut-fine sampling of 10−4 cm−1. Also, in the case of the lut-coarse sampling interval, the optical depth can be parameterized with a low order polynomial where the regression coefficients are obtained as explained below. For each species i we can define an equivalent optical depth χσ〉,i, which can be parameterized with respect to temperature in the same way we do for lut-fine sampled quantities (Eqs. (1) and (2)). The equivalent optical depth is
χσ,i=qi(cσ,0,i+cσ,1,iT+cσ,2,iT2)
(3)
where the angular brackets, 〈·〉 indicates the average operation over the wave number. In Eq. (3) the equivalent coefficients cσ,j,i with j = 0, 1, 2, are obtained by fitting the layer transmittance averaged from the fine to the coarse spectral resolution
qi(cσ,0,i+cσ,1,iT+cσ,2,iT2)=log(τσ,i)=log[exp(χσ,i)]
(4)
Due to the lut-coarse sampling and other optimizations, σ-IASI runs more than 100 times faster than the older version that used the fine-mesh look-up table. It is also to be stressed that the code with the lut-coarse sampling may not be appropriate for use in the upper stratosphere where the representation of the features of the CO2 absorption would be as a result significantly degraded. The lut-coarse sampling does not affects, however, the results presented in this paper where we are mainly interested in the study of the lower atmosphere.

3. PSI basics and retrieval methodology

For a Fourier transform spectrometer such as IASI the quantity which is effectively measured is the interferogram, I(x), with x the OPD (optical path difference in units of cm). The spectrum, R(σ), with σ the wave number (in units of cm−1) is recovered through a Fourier transform operation. For a calibrated, bandlimited spectrum, whatever its origin and measurement technique, the interferogram is simply computed as the Fourier transform of the symmetrized spectrum, r(σ)
I(x)=+r(σ)exp(2πiσx)dσ;withr(±σ)={R(σ)2;σ1<σ<σ20;otherwise
(5)
with i the imaginary unit and σ1, σ2 are the band endpoints. The bandwidth is σ2σ1 and both endpoints are positive. In this way, the interferogram can be thought of as a mathematical tool which can be applied to any kind of spectrum, either measured with a radiometer or an interferometer. A partial interferogram, I(x); x1xx2, is the interferogram over a given optical path difference interval with endpoints, x1 and x2, respectively. Details about the properties of the partial interferogram can be found in [3

3. G. Grieco, G. Masiello, C. Serio, R. L. Jones, and M. I. Mead, “Infrared Atmospheric Sounding Interferometer correlation interferometry for the retrieval of atmospheric gases: the case of H2O and CO2,” Appl. Opt. 50, 4516–4528 (2011), . [CrossRef] [PubMed]

]. For the present analysis, these properties are well illustrated by the case for CO2 whose periodic pattern of absorption lines, superimposed on vibrational transitions, yields a typical interferogram resonance around x = 0.65 cm. This resonance is plotted in Fig. 1, which shows a typical interferogram for a tropical IASI sounding. The interferogram has been obtained by considering the IASI band 1 (645 to 1210 cm−1) alone. In this band we have the ν2 fundamental absorption band of CO2 and the hot bands in the atmospheric window. It is important to stress that the partial interferogram shown in Fig. 1 conveys information from the entire IASI band 1. This means that due to the spectral characteristics of CO2 lines, all the IASI band 1 information on CO2 emission is compressed in a relatively small interval in the interferogram domain. This is all the more evident when we consider the sensitivity of the partial interferogram to the concentration of CO2. The sensitivity is computed as the Jacobian derivative of the interferogram radiance, JI with respect to the CO2 atmospheric profile, that is
JI(x,p)=I(x)qCO2(p)
(6)
where qCO2 (p) (with p the pressure) is the mixing ratio profile of CO2. The Jacobian derivative is shown in the mesh surface of Fig. 2 for the range of OPD around the CO2 resonance. From this figure we see that the sensitivity of I(x) to CO2 extends throughout the entire atmospheric column, down below to the planetary boundary layer.

Fig. 1 a) IASI band 1 spectrum for a tropical sounding; b) its interferogram; c) partial interferogram over the range 0.6–0.7 cm, which evidences the beating due to the CO2 line emission structure. The spectrum is in radiance units (1 r.u.=1 W m−2 sr−1 (cm−1)−1); the interferogram is in interferogram units (1 i.u.= 1 W m−2 sr−1).
Fig. 2 Mesh surface of JI: the Jacobian derivative of the interferogram radiance with respect to mixing ratio profile of CO2 (1 i.u.= 1 W m−2 sr−1).

3.1. The case of CO2

For the retrieval of the total column amount of CO2, we consider the partial interferogram which extends from x1 = 0.55 cm to x2 = 0.75 cm. This interferogram is derived from IASI radiances in band 1, which covers the range from 645 to 1210 cm−1. The partial interferogram is shown in Fig. 3 for the case of a typical tropical air mass. The contribution to the signal due to the potential interfering molecules H2O and O3 is also shown in the figure. For the generic interfering factor, X, the contribution to the interferogram is computed by considering the difference, I(x) − IX (x), where I(x) is the interferogram with the full state vector, and IX (x) that with the state vector where X has been set to zero. It is seen that within the range 0.55–0.75 cm the interferogram signal is mostly due to CO2. The effect of H2O is significant, whereas that of O3 can be considered to be an effect of second order.

Fig. 3 (left) Exemplifying the partial interferogram used for the retrieval of CO2. The figure also shows the signal contributed from two possible interfering atmospheric gases (1 i.u.= 1 W m−2sr−1). (right) Reference mixing ratio profiles for the trace gases analyzed in this paper.

For the specific case of IASI, the number of interferogram samples within the range 0.55–0.75 cm is N = 227. Let I be the interferogram vector, I = (I1,..., IN)T), where the super-script T stands for transpose and Ij stands for I(xj), j = 1,...,N and let v = (qCO2, T, Q, Ts)T be the state vector. We develop the interferogram in a Taylor series around a reference state vector, vo truncated at the first order term,
I=Io+JCO2(qCO2qCO2,o)+JT(TTo)+JQ(QQo)+Js(TsTso)
(7)
where, as usual, as before Ts is the surface temperature, T the temperature profile, Q the water vapour profile and qCO2 the CO2 profile. The reference state vector, vo, apart from the CO2 profile, is that determined by physical inversion of the same IASI spectrum as that we use for the final retrieval of CO2. The Jacobian derivatives are computed as usual at the reference state vector.

Because the reference atmospheric state vo has been itself retrieved by using IASI observations, the assumption of linearity is reasonable, unless the retrieval failed. In addition, we do not need at this stage to retrieve the fine structures of the vertical profiles of T and Q. These parameters are included, along with Ts, to avoid that their residual uncertainty could bias the final estimate of the total column amount of CO2. For this reason we assume that the true and the reference atmospheric parameters may be different up to a scaling factor, that is
Ts=(1+fTs)Tso;T=(1+fT)ToQ=(1+fQ)Qo;qCO2=(1+fCO2)qCO2,o
(8)
With this assumption, Eq. (7) simplifies in
IIo=a1fCO2+a2fT+a3fQ+a4fTsa1=JCO2qCO2,o;a2=JTTo;a3=JQQo;a4=JsTso
(9)
The four vectors a1, a2, a3, a4 are of size N, the same as the vector I. They can be horizontally concatenated to form the matrix, A = (a1, a2, a3, a4) of size N × M, where M = 4 is the number of scaling factors in Eq. (9). Then, Eq. (9) can be written as
Y=IIo=Af;withf=(fCO2,fT,fQ,fTs)T
(10)
In Eq. (10) we have M = 4 unknowns and N = 227 observations, therefore we can use Least Squares to seek for an optimal solution. We have checked that no further constraints have to be considered to get an estimate of f and its covariance matrix, S, therefore we can use unconstrained Least Square, this is important because it means that the final solution is not biased,
f^=(ATSI1A)1ATSI1Y;andSf^=(ATSI1A)1
(11)
where SI is the observational covariance matrix of the interferometric radiances. The observational covariance matrix, SI has been constructed by considering that for the IASI spectrum, SR, which, in turn, is constructed on the basis of IASI radiometric noise. SR is non-diagonal because the IASI spectrum is apodised. The transformation of SR to SI has been performed taking correctly into account the apodisation effect.

Finally, an estimate CO2 of the total column amount of CO2 is obtained by
q¯CO2=(1+f^CO2)pg0qCO2(p)dp=(1+f^CO2)q¯CO2,o
(12)
where pg is the ground pressure and with CO2,o the total column amount of the CO2 reference profile. The accuracy, eCO2 of the estimate is given by
eCO2=Sf^(1,1)q¯CO2,o
(13)
We should observe that in the retrieval procedure we also need a reference mixing ratio profile of CO2 for the retrieval algorithm. Using the same approach as that shown in [3

3. G. Grieco, G. Masiello, C. Serio, R. L. Jones, and M. I. Mead, “Infrared Atmospheric Sounding Interferometer correlation interferometry for the retrieval of atmospheric gases: the case of H2O and CO2,” Appl. Opt. 50, 4516–4528 (2011), . [CrossRef] [PubMed]

], we have checked that the choice of either an uniform CO2 profile (constant mixing ratio with the altitude) or a non-uniform profile is not critical. This sensitivity check has been performed by considering retrievals obtained with uniform profiles with a fixed mixing ratio value of 385 ppmv (see Fig. 3) and non-uniform CO2 profiles obtained from ECMWF analysis (e.g., see Fig. 2 in ref [3

3. G. Grieco, G. Masiello, C. Serio, R. L. Jones, and M. I. Mead, “Infrared Atmospheric Sounding Interferometer correlation interferometry for the retrieval of atmospheric gases: the case of H2O and CO2,” Appl. Opt. 50, 4516–4528 (2011), . [CrossRef] [PubMed]

]) colocated in time and space with the JAIVEx soundings. As a result we have a difference, (non-uniform)-(uniform), which does not exceed +1 ppmv.

The various scaling factors which are determined within the retrieval scheme also provide a good quality check of the final solution. We do not expect dramatic variations in the values corresponding to Ts, T and H2O. The reference atmospheric parameters have been obtained by a physical inversion of the same spectra we use to retrieve CO2. In case we find large variations, it is likely that the retrieval has failed due to some unknown source of additional error, most likely cloudiness. For temperature we expect at most to have variations below ±1 K, for water vapour we could expect some larger deviation from the reference, because the IASI retrieval may have a large uncertainty in the lower troposphere. However, variation larger than 20% are suspicious and the retrieval is flagged as of poor quality in this case.

Fig. 4 (left) Retrieved CO2 for the JAIVEx experiment; (right) retrieved scaling factors for the JAIVEx experiment.

Finally, the problem of the interdependence between the total column amount of CO2 and the temperature profile is addressed. First, we note that through the scaling factor, fT we retrieve a bulk temperature of the atmosphere and not a detailed temperature profile. The fact that we have independent information for both fT and fCO2 is evidenced by the existence of the unconstrained Least Squares solution we get through Eq. (11). A strong interdependency between the two parameters fT and fCO2 would yield a Kernel A highly ill-conditioned. However, normally the conditioning number of this Kernel is below 100, which means that the linear system expressed by Eq. (10) is made up with equations, which are largely independent. The interdependency between atmospheric parameters can be also analysed by looking at the a-posteriori covariance matrix, Sf or better at the correlation matrix, Cf defined according to
Cf(i,j)=Sf(i,j)Sf(i,i),Sf(j,j)i,j=1,,M=4
(14)
Keeping in mind the definition of the f-vector in Eq. (10), a typical example of the correlation matrix Cf is shown in Eq. (15) below
Cf=(1.000,0.14,0.130,0.490.14,1.000,0.850,0.5400.130,0.850,1.000,0.2600.49,0.540,0.260,1.000)
(15)
From the first row of this matrix we learn that the correlation of the CO2 total column amount with temperature and water vapour is very poor: the correlation is −0.14 with temperature and 0.13 with water vapour. Furthermore, these two correlations have opposite sign and tend to cancel out. In contrast, the correlation with surface temperature reaches the value of −0.5. This result says that for the retrieval of the total column amount of CO2 a good reference value of the surface temperature is much more critical than a good reference value for temperature and water vapour profiles. Also from Eq. (15), second row, we note a relatively high interdependency between T and Q, and T and Ts.

In summary, for the retrieval scheme we have developed the interdependence between temperature profile and the total column amount of CO2 is not a major concern, a source of possible bias is much more linked to Ts, for which we need a good reference value to initialize the inverse scheme to retrieve CO2.

3.2. The case of CO, N2O and CH4

The retrieval methodology developed for CO2 has been also applied to CO, N2O and CH4. Of course, the partial interferogram depends on the given gas and is selected in order to minimize interfering effects from other species.

For the case of CO, a linear molecule, its spectrum (at the IASI spectral resolution) has regularly spaced spectral lines in the atmospheric window between 2080 and 2200 cm−1. The line spacing is ≈ 2 cm−1 in the spectral domain, which yields a resonance in the interferogram space at the inverse of two times the line spacing, that is 0.25 cm. The partial interferogram we use for CO total column amount retrieval is obtained by Fourier transforming the IASI band 3 alone. The interferogram interval extends from 0.2230 to 0.3118 cm and is shown in Fig. 5(a) for the case of a tropical model of atmosphere. At the IASI sampling interval, we have a total of N = 136 data points. In this range, apart from the ubiquitous presence of water vapour, CO is the most important contributors to the spectrum, with N2O and O3 playing a secondary role.

Fig. 5 Partial interferogram used for a) CO, b) N2O and c) CH4. (1 i.u.= 1 W m−2sr−1).

N2O is a linear molecule as well, with regularly spaced absorption lines at the spectral IASI resolution. The main N2O absorption bands (ν1 and ν3 absorptions bands) are located in IASI band 1, 2 and 3. For this reason, to take advantage of the contribution from all lines, the interferogram from N2O is obtained by Fourier transforming the whole IASI spectrum (band 1, 2 and 3). The partial interferogram for N2O is shown in Fig. 5(b) and extends over two separate intervals from 1.0459 to 1.0499 cm and 1.1220 to 1.1270 cm, respectively, for a total of 40 data points. N2O is the most difficult gas to retrieve. Its ν1-absorption band overlaps that of methane at 1306.6 cm−1, and the ν3 in the short-wave region overlaps that of CO2. The partial interferogram range we have selected is the only short portion of the whole interferogram where the main contributor is N2O, whereas the contribution from interfering species, especially by CH4 is minimized.

Finally, CH4 is present in the IASI band 2 (ν4 absorption band centred at 1306.2 cm−1). Methane is not a linear molecule, however its spectrum also generates (at IASI spectral resolution) a characteristic wave-like structure in the interferogram domain. For the purpose of the retrieval of the total column amount of CH4, the interferogram has been obtained by considering only the IASI band 2, which limits potential interfering effects mostly to N2O alone. The partial interferogram we have selected for the retrieval of CH4 is shown in Fig 5(c). It extends from 0.7399 to 1 cm for a total of 412 data points. In this range the contribution from N2O is nearly zero.

The reference profiles we use for CO, CH4, N2O and CO2 as well are summarized in Fig. 3. The total column amount from the reference profile yields 109 ppbv for CO, 306 ppbv for N2O and 1646 ppbv for CH4.

As for CO2 we have that the sensitivity of our retrieval methodology extends to a large portion of the troposphere. Normally, the lower troposphere, which extends from 1000 to ≈ 800 hPa shows a very poor sensitivity, mostly for CH4 and N2O, as it is possible to see from Fig. 6, which shows mesh surfaces of the Jacobian derivative of the interferogram radiance with respect to the volume mixing ratio of CO, N2O and CH4, respectively.

Fig. 6 Jacobian derivative of I(x) with respect to the volume mixing ratio of CO (left); N2O (middle); CH4 (right) (1 i.u.= 1 W m−2sr−1).

For the set of 22 IASI spectra from the JAIVEx experiment, Fig. 7 shows the retrieval for the total amount of CO, N2O and CH4, respectively. The accuracy of the retrieval for a single IASI observations is ≈ ±1.5 ppbv, ≈ ±13 ppbv and ≈ ±0.015 ppmv respectively for CO, N2O and CH4. Figure 7 shows that the retrieval is largely in agreement with the global annual cycle of CO, CH4 and N2O, respectively (e.g., [40

40. T. J. Blasing, “Recent Greenhouse Gas Concentrations,” (2011) doi: [CrossRef] .

]). In addition, as already found for the case of CO2, we also have that the standard deviation of the retrieved values for CH4 and N2O perfectly parallels the accuracy assessed trough the retrieval covariance matrix, Eq. (11), that is ±15 ppbv for CH4 and 13 ppbv for N2O. For CO the standard deviation is 3 ppbv, which is about two times the accuracy. This is quite compatible with the high variability of this atmospheric gas.

Fig. 7 Retrieval analysis for the JAIVEx experiment. a) CO; b) N2O; c) CH4.

4. Application to IASI for the Mediterranean sea

To check the quality and reliability of the retrieval algorithm for trace gases when applied to a large set of IASI data, we have focused on the Mediterranean basin for the period of July 2010. We have a total of 34719 IASI soundings, which have been checked for clear sky using the IASI stand alone cloud detection scheme described in [28

28. G. Grieco, G. Masiello, M. Matricardi, C. Serio, D. Summa, and V. Cuomo, “Demonstration and validation of the φ-IASI inversion scheme with NAST-I data,” Q. J. R. Meteorol. Soc. 133(s3), 217–232 (2007), doi: [CrossRef] .

31

31. G. Masiello, C. Serio, and V. Cuomo, “Exploiting quartz spectral signature for the detection of cloud-affected satellite infrared observations over African desert areas,” Appl. Opt. 43(11), 2305–2315 (2004), doi: [CrossRef] . [PubMed]

]. Each IASI sounding has been inverted for Ts and T, Q, O. These parameters have been used as the reference state in the sequential retrieval for trace gases.

The maps for the four trace gases considered in this study are shown in Fig. 8. The original IASI retrieval has been smoothed with a Wiener filter [41

41. J. S. Lim, Two-Dimensional Signal and Image Processing (Prentice Hall, 1990), p. 548.

] and projected on 0.5×0.5 deg (lat × lon) grid. To a variable degree, all maps shows a positive gradient into the direction North-West to South-East. This is in agreement with the general circulation over the Mediterranean sea for the month of July. For the case of CO2 we have that the total column amount is distributed over a range of values form 380 to 390 ppmv, with an average of 384.8 ppmv. This can be compared to the global average value of the annual cycle for the month of July 2010, which according to the NASA Earth System Monitoring Laboratory (Global Monitoring Division) [39] is equal to 387.21 ppmv.

Fig. 8 July 2010 maps derived from IASI soundings. From top left, clockwise, total column amount of CO2, CO, CH4 and N2O, respectively.

The average values for CH4, N2O, and CO are 1926 ppbv, 313 ppbv and 88 ppbv, respectively. The value observed for CH4 is a bit higher of that credited to this gas from ground based observations at mid-latitude, that is 1874 ppbv [40

40. T. J. Blasing, “Recent Greenhouse Gas Concentrations,” (2011) doi: [CrossRef] .

], whereas for N2O our average concentration of 313 ppbv is slightly smaller than the global value, which is 324 ppbv [40

40. T. J. Blasing, “Recent Greenhouse Gas Concentrations,” (2011) doi: [CrossRef] .

]. These differences have to be expected when we compare sea surface measurements with satellite observations, which are not sensitive to the planetary boundary layer (e.g. see Fig. 6). The observed differences just says that the volume mixing ratio of both N2O and CH4 is not uniform with the altitude. For N2O aircraft observations tend to confirm that the concentration of N2O in the boundary layer is some 10–15 ppbv larger than that in the open troposphere [42].

For the case of CO, we have to say that this is a gas which has a large time and space variability, so that the comparison with sea surface values makes less sense. What we can say is that sea surface observations also show a pronounced annual cycle with its minimum in the summer season. Also, aircraft records show that the summer vertical spatial distribution of CO concentration has a rapid decrease from the boundary layer to the free troposphere [43

43. A. Klonecki, M. Pommier, C. Clerbaux, G. Ancellet, J.-P. Cammas, P.-F. Coheur, A. Cozic, G. S. Diskin, J. Hadji-Lazaro, D. A. Hauglustaine, D. Hurtmans, B. Khattatov, J.-F. Lamarque, K. S. Law, P. Nedelec, J.-D. Paris, J. R. Podolske, P. Prunet, H. Schlager, S. Szopa, and S. Turquety, “Assimilation of IASI satellite CO fields into a global chemistry transport model for validation against aircraft measurements,” Atmos. Chem. Phys. 12, 4493–4512 (2012), doi: [CrossRef] .

]. This behaviour is in agreement with our retrieval. We also stress that our CO retrieval results are in excellent agreement with those obtained with the LATMOS-ULB algorithm [23

23. D. Hurtmans, P.-F. Coheur, C. Wespes, L. Clarisse, O. Scharf, C. Clerbaux, J. Hadji-Lazaro, M. George, and S. Turquety, “FORLI radiative transfer and retrieval code for IASI,” J. Quant. Spectroscop. Ra. 113(11), 1391–1408 (2012), doi: [CrossRef] .

, 24

24. T. Kerzenmacher, B. Dils, N. Kumps, T. Blumenstock, C. Clerbaux, P.-F. Coheur, P. Demoulin, O. Garcia, M. George, D. W. T. Griffith, F. Hase, J. Hadji-Lazaro, D. Hurtmans, N. Jones, E. Mahieu, J. Notholt, C. Paton-Walsh, U. Raffalski, T. Ridder, M. Schneider, C. Servais, and M. De Maziere, “Validation of IASI FORLI carbon monoxide retrievals using FTIR data from NDACC,” Atmos. Meas. Tech. 5, 2751–2761 (2012), doi: [CrossRef] .

]. The comparison to our CO retrievals is shown in Fig. 9. The LATMOS-ULB mean value for the month of July is 89.4 ppbv with a variability of 9.08 ppbv over the basin. This figure can be compared to our figure of 88.2 ppbv for the mean and 7.20 ppbv for the variability. The slightly lower mean and variability obtained using our retrieval is likely to be a result of the use of a more stringent cloud mask in the processing of the IASI data. Nevertheless, the two maps plotted in Fig. 9 show remarkably consistent features and display the same horizontal spatial gradients.

Fig. 9 July 2010 maps derived from IASI soundings. LATMOS-ULB retrieval scheme (left); this study (right).

For the CO2 case we have carried out a comparison with AIRS CO2 level 2 products by projecting IASI and AIRS CO2 fields on the same 2 × 2 deg (lat × lon) grid. Results are shown in the maps plotted in Fig. 10. It is evident that the results shown in the AIRS map display all the elements of a random behaviour. Furthermore, the AIRS CO2 level 2 data give an average value of 391.5 ppmv with a variability (standard deviation) of 2.8 ppmv. This means that, even taking into account its variability, the AIRS estimate of the average value exceeds both the value of the annual cycle for the month of July (387.21 ppmv) and the annual trend for 2010 (388.58 ppmv [39]). In contrast, our scheme retrieves an average value of 384.8 ppmv, which, within a variability of 4.8 ppmv, is in agreement with both the cycle and the trend. Besides, this variability of 4.8 ppmv is not just random. It reflects a North-to-South gradient as shown in Fig. 10. This pattern is absent in the AIRS results which clearly show that the variability of the retrieval is fundamentally random.

Fig. 10 CO2 monthly map on 2 × 2 deg grid. Left, IASI. Right, AIRS.

As shown in Fig. 10, the North-to-South gradient retrieved by the IASI PSI scheme is in agreement with the observations of CO2 and CH4 at the sea surface level obtained from the GAW WMO network for the month of July 2010. For the case of CO2 it is interesting to see that the IASI PSI retrieval is capable of capturing the local minimum around Filokalia station (see Fig. 11). The capability of the PSI methodology of capturing the correct horizontal gradients is also evident in the case of methane. These results support our claim of being able to retrieve the concentration of trace gases to an unprecedented accuracy. Also, we note that the North-West to South-East gradient for CO2 and CH4 is in agreement with the known biogenic activity of the Mediterranean sea. In summer the North-West part of the sea is a sink for CO2, whereas the South-East part behaves like a source [44

44. F. D’Ortenzio, D. Antoine, and S. Marullo, “Satellite-driven modeling of the upper ocean mixed layer and air-sea CO2flux in the Mediterranean Sea,” Deep-Sea Res . Pt. I 55(4), 405–434 (2008), doi: [CrossRef] .

].

Fig. 11 Comparison of IASI retrieval with observations at sea surface stations. CO2 (left); CH4 (right).

5. Conclusion

We have demonstrated that the technique of partial correlation interferometry applied to IASI observations is capable of improving the accuracy of trace gas retrieval to the unprecedented values of ±3 ppmv for CO2, ±13 ppbv for N2O, ±1.5 ppbv for CO and, finally ±15 ppbv for CH4 for each single IASI observation with a horizontal spatial resolution of ≈ 12 km. The analysis of IASI data over the relatively small basin of the Mediterranean sea has shown that we can retrieve relatively small scale structures. This has allowed us to detect for the first time a summer North-West to South-East gradient in the concentration of trace gases of climatological importance. The pattern of CO2 is consistent with the known biogenic activity of the Mediterranean sea in July. The global monthly maps of methane, carbon monoxide, carbon dioxide and nitrous oxide retrieved using IASI data are consistent with the known annual cycles of these trace gases. As an example, we were able to detect the strong decrease of carbon monoxide concentration in July, when the concentration of this gas in the open troposphere goes below 100 ppbv. It has also to be stressed that our scheme is a physical-based algorithm, which exploits the capability of the σ-IASI forward model to compute analytical Jacobian derivatives. Examples of analytical Jacobian have been provided that show the sensitivity of IASI to the vertical concentration of trace gases. Because of this capability, our scheme can be easily generalized to retrieve also information along the vertical. However, the important aspect of the present methodology is that it is based on unconstrained Least Square, which yields unbiased results. If we want to move to the retrieval of vertical profiles, we will need additional constraints, which inevitably will add bias to the final solution. To conclude, the vertical spatial resolution and the magnitude of the bias will depend on the background constraint, an effect which will be investigated in future work.

Acknowledgments

IASI has been developed and built under the responsibility of the Centre National d’Etudes Spatiales (CNES, France). It is flown onboard the Metop satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMETCast near real time data distribution service. We thank Dr Stuart Newman (Met Office) for providing the JAIVEx data. The ground based measurements in Lampedusa and Sede Boker have been provided by NOAA/ESRL and have been downloaded from (http://www.esrl.noaa.gov/gmd/dv/iadv/). The ground based measurements in Begur and Finokalia have been provided by Laboratoire des Sciences du Climat et de l’Environnement (http://www.lsce.ipsl.fr/). The ground based measurements in Cairo have been provided by the Egyptian Meteorological Authority (http://ema.gov.eg/). The ground based measurements in Lampedusa for what concerns N2O have been provided by the Italian Agency ENEA. Work supported by project Ritmare-Ricerca Italiana per il Mare (CNR-MIUR)

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https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat/instruments/mipas

13.

A. M. Lubrano, G. Masiello, M. Matricradi, C. Serio, and V. Cuomo, “Retrieving N2O from nadir-viewing infrared spectrometers,” Tellus B 56(3), 249–261 (2004), doi: [CrossRef] .

14.

M. T. Chahine, C. Barnet, E. T. Olsen, L. Chen, and E. Maddy, “On the determination of atmospheric minor gases by the method of vanishing partial derivatives with application to CO2,” Geophys. Res. Lett. 32, L22803 (2005), doi: [CrossRef] .

15.

M. T. Chahine, L. Chen, P. Dimotakis, X. Jiang, Q. Li, E. T. Olsen, T. Pagano, J. Randerson, and Y. L. Yung, “Satellite remote sounding of mid-tropospheric CO2,” Geophys. Res. Lett. 35, L17807 (2008), doi: [CrossRef] .

16.

W. W. Mc Millan, C. Barnet, L. Strow, M. T. Chahine, M. L. Mc Court, J. X. Warner, P. C. Novelli, S. Korontzi, E. S. Maddy, and S. Datta, “Daily global maps of carbon monoxide from NASA’s Atmospheric Infrared Sounder,” Geophys. Res. Lett. 32, L11801 (2005), doi: [CrossRef] .

17.

C. Crevoisier, A. Chédin, H. Matsueda, T. Machida, R. Armante, and N. A. Scott, “First year of upper tropospheric integrated content of CO2from IASI hyperspectral infrared observations,” Atmos. Chem. Phys. 9, 4797–4810 (2009), doi: [CrossRef] .

18.

P. Ricaud, J.-L. Attié, H. Teyssédre, L. El Amraoui, V.-H. Peuch, M. Matricardi, and P. Schluessel, “Equatorial total column of nitrous oxide as measured by IASI on MetOp-A: implications for transport processes,” Atmos. Chem. Phys. 9, 3947–3956 (2009), doi: [CrossRef] .

19.

A. Boynard, C. Clerbaux, P.-F. Coheur, D. Hurtmans, S. Turquety, M. George, J. Hadji-Lazaro, C. Keim, and J. Mayer-Arnek, “Measurements of total and tropospheric ozone from the IASI instrument: comparison with satellite and ozonesonde observations,” Atmos. Chem. Phys. 9, 6255–6271 (2009), doi: [CrossRef] .

20.

L. Clarisse, P.-F. Coheur, A. J. Prata, D. Hurtmans, A. Razavi, T. Phulpin, J. Hadji-Lazaro, and C. Clerbaux, “Tracking and quantifying volcanic SO2with IASI, the September 2007 eruption at Jebel at Tair,” Atm. Chem. Phys. 8, 7723–7734 (2008), doi: [CrossRef] .

21.

L. Clarisse, C. Clerbaux, F. Dentener, D. Hurtmans, and P.-F. Coheur, “Global ammonia distribution derived from infrared satellite observations,” Nature Geosci. 2, 479–483 (2009), doi: [CrossRef]

22.

C. Clerbaux, A. Boynard, L. Clarisse, M. George, J. Hadji-Lazaro, H. Herbin, D. Hurtmans, M. Pommier, A. Razavi, S. Turquety, C. Wespes, and P.-F. Coheur, “Monitoring of atmospheric composition using the thermal infrared IASI/Metop sounder,” Atmos. Chem. Phys. 9, 6041–6054 (2009), doi: [CrossRef] .

23.

D. Hurtmans, P.-F. Coheur, C. Wespes, L. Clarisse, O. Scharf, C. Clerbaux, J. Hadji-Lazaro, M. George, and S. Turquety, “FORLI radiative transfer and retrieval code for IASI,” J. Quant. Spectroscop. Ra. 113(11), 1391–1408 (2012), doi: [CrossRef] .

24.

T. Kerzenmacher, B. Dils, N. Kumps, T. Blumenstock, C. Clerbaux, P.-F. Coheur, P. Demoulin, O. Garcia, M. George, D. W. T. Griffith, F. Hase, J. Hadji-Lazaro, D. Hurtmans, N. Jones, E. Mahieu, J. Notholt, C. Paton-Walsh, U. Raffalski, T. Ridder, M. Schneider, C. Servais, and M. De Maziere, “Validation of IASI FORLI carbon monoxide retrievals using FTIR data from NDACC,” Atmos. Meas. Tech. 5, 2751–2761 (2012), doi: [CrossRef] .

25.

D. Wunch, P. O. Wennberg, G. C. Toon, B. J. Connor, B. Fisher, G. B. Osterman, C. Frankenberg, L. Mandrake, C. O’Dell, P. Ahonen, S. C. Biraud, R. Castano, N. Cressie, D. Crisp, N. M. Deutscher, A. Eldering, M. L. Fisher, D. W. T. Griffith, M. Gunson, P. Heikkinen, G. Keppel-Aleks, E. Kyro, R. Lindenmaier, R. Macatangay, J. Mendonca, J. Messerschmidt, C. E. Miller, I. Morino, J. Notholt, F. A. Oyafuso, M. Rettinger, J. Robinson, C. M. Roehl, R. J. Salawitch, V. Sherlock, K. Strong, R. Sussmann, T. Tanaka, D. R. Thompson, O. Uchino, T. Warneke, and S. C. Wofsy, “A method for evaluating bias in global measurements of CO2 total columns from space,” Atmos. Chem. Phys. 11, 12317–12337 (2011), doi: [CrossRef] .

26.

U. Amato, M. F. Carfora, V. Cuomo, and C. Serio, “Objective algorithms for the aerosol problem,” Appl. Opt. 34(24), 5442–5452 (1995), doi: [CrossRef] . [PubMed]

27.

G. Masiello and C. Serio, “Dimensionality-reduction approach to the thermal radiative transfer equation inverse problem,” Geophys. Res. Lett. 31, L11105 (2004) doi: [CrossRef] .

28.

G. Grieco, G. Masiello, M. Matricardi, C. Serio, D. Summa, and V. Cuomo, “Demonstration and validation of the φ-IASI inversion scheme with NAST-I data,” Q. J. R. Meteorol. Soc. 133(s3), 217–232 (2007), doi: [CrossRef] .

29.

C. Serio, A. M. Lubrano, F. Romano, and H. Shimoda, “Cloud detection over sea surface by use of autocorrelation functions of upwelling infrared spectra in the 800–900-cm−1window region,” Appl. Opt. 39(21), 3565–3572 (2000), doi: [CrossRef] .

30.

G. Masiello, M. Matricardi, R. Rizzi, and C. Serio, “Homomorphism between cloudy and clear spectral radiance in the 800–900-cm−1atmospheric window region,” Appl. Opt. 41(6), 965–973 (2002), doi: [CrossRef] . [PubMed]

31.

G. Masiello, C. Serio, and V. Cuomo, “Exploiting quartz spectral signature for the detection of cloud-affected satellite infrared observations over African desert areas,” Appl. Opt. 43(11), 2305–2315 (2004), doi: [CrossRef] . [PubMed]

32.

G. Masiello and C. Serio, “Simultaneous physical retrieval of surface emissivity spectrum and atmospheric parameters from infrared atmospheric sounder interferometer spectral radiances,” Appl. Opt. 52(11), 2428–2446 (2013), doi: [CrossRef] . [PubMed]

33.

http://airs.jpl.nasa.gov/AIRS_CO2_Data/

34.

http://www.pole-ether.fr/etherTypo/index.php?id=1577&L=1

35.

S. A. Clough, M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, “Atmospheric radiative transfer modeling: a summary of the AER codes,” J. Quant. Spectroscop. Ra. 91(2), 233–244 (2005), doi: [CrossRef] .

36.

http://rtweb.aer.com/

37.

G. Masiello and C. Serio, “An effective water vapor self-broadening scheme for look-up-table-based radiative transfer,” in Remote Sensing of Clouds and the Atmosphere VII. Proceedings of the SPIE, Volume 4882. (K. P. Schaefer, O. Lado-Bordowsky, A. Comeron, and R. H. Picard, eds.), 52–61 (2003), doi: [CrossRef] .

38.

FAAM, “Joint Airborne IASI Validation Experiment (JAIVEX),” http://badc.nerc.ac.uk/data/jaivex/.

39.

http://www.esrl.noaa.gov/gmd/ccgg/trends/.

40.

T. J. Blasing, “Recent Greenhouse Gas Concentrations,” (2011) doi: [CrossRef] .

41.

J. S. Lim, Two-Dimensional Signal and Image Processing (Prentice Hall, 1990), p. 548.

42.

http://ds.data.jma.go.jp/gmd/wdcgg/wdcgg.html

43.

A. Klonecki, M. Pommier, C. Clerbaux, G. Ancellet, J.-P. Cammas, P.-F. Coheur, A. Cozic, G. S. Diskin, J. Hadji-Lazaro, D. A. Hauglustaine, D. Hurtmans, B. Khattatov, J.-F. Lamarque, K. S. Law, P. Nedelec, J.-D. Paris, J. R. Podolske, P. Prunet, H. Schlager, S. Szopa, and S. Turquety, “Assimilation of IASI satellite CO fields into a global chemistry transport model for validation against aircraft measurements,” Atmos. Chem. Phys. 12, 4493–4512 (2012), doi: [CrossRef] .

44.

F. D’Ortenzio, D. Antoine, and S. Marullo, “Satellite-driven modeling of the upper ocean mixed layer and air-sea CO2flux in the Mediterranean Sea,” Deep-Sea Res . Pt. I 55(4), 405–434 (2008), doi: [CrossRef] .

OCIS Codes
(010.1280) Atmospheric and oceanic optics : Atmospheric composition
(030.5620) Coherence and statistical optics : Radiative transfer
(300.6340) Spectroscopy : Spectroscopy, infrared
(280.4991) Remote sensing and sensors : Passive remote sensing
(120.6085) Instrumentation, measurement, and metrology : Space instrumentation

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: July 5, 2013
Revised Manuscript: October 2, 2013
Manuscript Accepted: October 2, 2013
Published: October 9, 2013

Citation
Giuseppe Grieco, Guido Masiello, Marco Matricardi, and Carmine Serio, "Partially scanned interferogram methodology applied to IASI for the retrieval of CO, CO2, CH4 and N2O," Opt. Express 21, 24753-24769 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-21-24753


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References

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  11. http://www.ace.uwaterloo.ca/
  12. https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat/instruments/mipas
  13. A. M. Lubrano, G. Masiello, M. Matricradi, C. Serio, and V. Cuomo, “Retrieving N2O from nadir-viewing infrared spectrometers,” Tellus B56(3), 249–261 (2004), doi:. [CrossRef]
  14. M. T. Chahine, C. Barnet, E. T. Olsen, L. Chen, and E. Maddy, “On the determination of atmospheric minor gases by the method of vanishing partial derivatives with application to CO2,” Geophys. Res. Lett.32, L22803 (2005), doi:. [CrossRef]
  15. M. T. Chahine, L. Chen, P. Dimotakis, X. Jiang, Q. Li, E. T. Olsen, T. Pagano, J. Randerson, and Y. L. Yung, “Satellite remote sounding of mid-tropospheric CO2,” Geophys. Res. Lett.35, L17807 (2008), doi:. [CrossRef]
  16. W. W. Mc Millan, C. Barnet, L. Strow, M. T. Chahine, M. L. Mc Court, J. X. Warner, P. C. Novelli, S. Korontzi, E. S. Maddy, and S. Datta, “Daily global maps of carbon monoxide from NASA’s Atmospheric Infrared Sounder,” Geophys. Res. Lett.32, L11801 (2005), doi:. [CrossRef]
  17. C. Crevoisier, A. Chédin, H. Matsueda, T. Machida, R. Armante, and N. A. Scott, “First year of upper tropospheric integrated content of CO2from IASI hyperspectral infrared observations,” Atmos. Chem. Phys.9, 4797–4810 (2009), doi:. [CrossRef]
  18. P. Ricaud, J.-L. Attié, H. Teyssédre, L. El Amraoui, V.-H. Peuch, M. Matricardi, and P. Schluessel, “Equatorial total column of nitrous oxide as measured by IASI on MetOp-A: implications for transport processes,” Atmos. Chem. Phys.9, 3947–3956 (2009), doi:. [CrossRef]
  19. A. Boynard, C. Clerbaux, P.-F. Coheur, D. Hurtmans, S. Turquety, M. George, J. Hadji-Lazaro, C. Keim, and J. Mayer-Arnek, “Measurements of total and tropospheric ozone from the IASI instrument: comparison with satellite and ozonesonde observations,” Atmos. Chem. Phys.9, 6255–6271 (2009), doi:. [CrossRef]
  20. L. Clarisse, P.-F. Coheur, A. J. Prata, D. Hurtmans, A. Razavi, T. Phulpin, J. Hadji-Lazaro, and C. Clerbaux, “Tracking and quantifying volcanic SO2with IASI, the September 2007 eruption at Jebel at Tair,” Atm. Chem. Phys.8, 7723–7734 (2008), doi:. [CrossRef]
  21. L. Clarisse, C. Clerbaux, F. Dentener, D. Hurtmans, and P.-F. Coheur, “Global ammonia distribution derived from infrared satellite observations,” Nature Geosci.2, 479–483 (2009), doi: [CrossRef]
  22. C. Clerbaux, A. Boynard, L. Clarisse, M. George, J. Hadji-Lazaro, H. Herbin, D. Hurtmans, M. Pommier, A. Razavi, S. Turquety, C. Wespes, and P.-F. Coheur, “Monitoring of atmospheric composition using the thermal infrared IASI/Metop sounder,” Atmos. Chem. Phys.9, 6041–6054 (2009), doi:. [CrossRef]
  23. D. Hurtmans, P.-F. Coheur, C. Wespes, L. Clarisse, O. Scharf, C. Clerbaux, J. Hadji-Lazaro, M. George, and S. Turquety, “FORLI radiative transfer and retrieval code for IASI,” J. Quant. Spectroscop. Ra.113(11), 1391–1408 (2012), doi:. [CrossRef]
  24. T. Kerzenmacher, B. Dils, N. Kumps, T. Blumenstock, C. Clerbaux, P.-F. Coheur, P. Demoulin, O. Garcia, M. George, D. W. T. Griffith, F. Hase, J. Hadji-Lazaro, D. Hurtmans, N. Jones, E. Mahieu, J. Notholt, C. Paton-Walsh, U. Raffalski, T. Ridder, M. Schneider, C. Servais, and M. De Maziere, “Validation of IASI FORLI carbon monoxide retrievals using FTIR data from NDACC,” Atmos. Meas. Tech.5, 2751–2761 (2012), doi:. [CrossRef]
  25. D. Wunch, P. O. Wennberg, G. C. Toon, B. J. Connor, B. Fisher, G. B. Osterman, C. Frankenberg, L. Mandrake, C. O’Dell, P. Ahonen, S. C. Biraud, R. Castano, N. Cressie, D. Crisp, N. M. Deutscher, A. Eldering, M. L. Fisher, D. W. T. Griffith, M. Gunson, P. Heikkinen, G. Keppel-Aleks, E. Kyro, R. Lindenmaier, R. Macatangay, J. Mendonca, J. Messerschmidt, C. E. Miller, I. Morino, J. Notholt, F. A. Oyafuso, M. Rettinger, J. Robinson, C. M. Roehl, R. J. Salawitch, V. Sherlock, K. Strong, R. Sussmann, T. Tanaka, D. R. Thompson, O. Uchino, T. Warneke, and S. C. Wofsy, “A method for evaluating bias in global measurements of CO2 total columns from space,” Atmos. Chem. Phys.11, 12317–12337 (2011), doi:. [CrossRef]
  26. U. Amato, M. F. Carfora, V. Cuomo, and C. Serio, “Objective algorithms for the aerosol problem,” Appl. Opt.34(24), 5442–5452 (1995), doi:. [CrossRef] [PubMed]
  27. G. Masiello and C. Serio, “Dimensionality-reduction approach to the thermal radiative transfer equation inverse problem,” Geophys. Res. Lett.31, L11105 (2004) doi:. [CrossRef]
  28. G. Grieco, G. Masiello, M. Matricardi, C. Serio, D. Summa, and V. Cuomo, “Demonstration and validation of the φ-IASI inversion scheme with NAST-I data,” Q. J. R. Meteorol. Soc.133(s3), 217–232 (2007), doi:. [CrossRef]
  29. C. Serio, A. M. Lubrano, F. Romano, and H. Shimoda, “Cloud detection over sea surface by use of autocorrelation functions of upwelling infrared spectra in the 800–900-cm−1window region,” Appl. Opt.39(21), 3565–3572 (2000), doi:. [CrossRef]
  30. G. Masiello, M. Matricardi, R. Rizzi, and C. Serio, “Homomorphism between cloudy and clear spectral radiance in the 800–900-cm−1atmospheric window region,” Appl. Opt.41(6), 965–973 (2002), doi:. [CrossRef] [PubMed]
  31. G. Masiello, C. Serio, and V. Cuomo, “Exploiting quartz spectral signature for the detection of cloud-affected satellite infrared observations over African desert areas,” Appl. Opt.43(11), 2305–2315 (2004), doi:. [CrossRef] [PubMed]
  32. G. Masiello and C. Serio, “Simultaneous physical retrieval of surface emissivity spectrum and atmospheric parameters from infrared atmospheric sounder interferometer spectral radiances,” Appl. Opt.52(11), 2428–2446 (2013), doi:. [CrossRef] [PubMed]
  33. http://airs.jpl.nasa.gov/AIRS_CO2_Data/
  34. http://www.pole-ether.fr/etherTypo/index.php?id=1577&L=1
  35. S. A. Clough, M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, “Atmospheric radiative transfer modeling: a summary of the AER codes,” J. Quant. Spectroscop. Ra.91(2), 233–244 (2005), doi:. [CrossRef]
  36. http://rtweb.aer.com/
  37. G. Masiello and C. Serio, “An effective water vapor self-broadening scheme for look-up-table-based radiative transfer,” in Remote Sensing of Clouds and the Atmosphere VII. Proceedings of the SPIE, Volume 4882. (K. P. Schaefer, O. Lado-Bordowsky, A. Comeron, and R. H. Picard, eds.), 52–61 (2003), doi:. [CrossRef]
  38. FAAM, “Joint Airborne IASI Validation Experiment (JAIVEX),” http://badc.nerc.ac.uk/data/jaivex/ .
  39. http://www.esrl.noaa.gov/gmd/ccgg/trends/ .
  40. T. J. Blasing, “Recent Greenhouse Gas Concentrations,” (2011) doi:. [CrossRef]
  41. J. S. Lim, Two-Dimensional Signal and Image Processing (Prentice Hall, 1990), p. 548.
  42. http://ds.data.jma.go.jp/gmd/wdcgg/wdcgg.html
  43. A. Klonecki, M. Pommier, C. Clerbaux, G. Ancellet, J.-P. Cammas, P.-F. Coheur, A. Cozic, G. S. Diskin, J. Hadji-Lazaro, D. A. Hauglustaine, D. Hurtmans, B. Khattatov, J.-F. Lamarque, K. S. Law, P. Nedelec, J.-D. Paris, J. R. Podolske, P. Prunet, H. Schlager, S. Szopa, and S. Turquety, “Assimilation of IASI satellite CO fields into a global chemistry transport model for validation against aircraft measurements,” Atmos. Chem. Phys.12, 4493–4512 (2012), doi:. [CrossRef]
  44. F. D’Ortenzio, D. Antoine, and S. Marullo, “Satellite-driven modeling of the upper ocean mixed layer and air-sea CO2flux in the Mediterranean Sea,” Deep-Sea Res. Pt. I 55(4), 405–434 (2008), doi:. [CrossRef]

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