Sounding the UTLS with MIPAS/ENVISATand SCIAMACHY/ENVISAT limb measurements using a tomographic approach Enzo Papandrea 1 , Massimo Carlotti 1 , Elisa Castelli 2 , Enrico Arnone 2 , Bianca Maria Dinelli 2 , Margherita Premuda 2 , Andrea Petritoli 2 , and Elisa Palazzi 2 1 Dipartimento di Chimica Fisica ed Inorganica, Universita’ di Bologna, Italy; 2 Istituto Scienze dell’Atmosfera e del Clima, ISAC-CNR, Bologna, Italy e-mail: enzo.papandrea@unibo.it R ATIONALE This study investigates current limb remote sensing observations applying tomographic methods to improve the description of the atmosphere and prepare the expertise needed for future higher performance missions. A tomographic approach is particularly important in regions characterized by high variability such as the Upper Troposphere-Lower Stratosphere (UTLS), the polar vortex or the day-night terminator where strong horizontal gradients can be poorly reproduced by common 1-D retrievals [1]. Key diagnostics describing the information available in the observations from two limb sounders are presented. T HE TOMOGRAPHIC APPROACH The tomographic retrieval approach consists in using simultaneously all the observations collected along a whole orbit and operating a 2-D discretization of the atmosphere thus enabling the horizontal atmospheric structures to be modelled [2]. In this approach each limb observation contributes to determine the unknown quantity at a number of different locations among those spanned by its line of sight rather than only at tangent point as in standard 1-D retrievals. MIPAS & SCIAMACHY The Michelson Interferometer for Passive Atmospheric Sound- ing (MIPAS) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) operating onboard ENVISAT can perform limb sounding measurements respectively in the IR and UV/VIS spectral range. For both instruments we devel- oped a tomographic retrieval code and are therefore able to compare their main advantages: MIPAS: the analysis code [3], called GMTR, is based on the Geo-fit approach [2] along with the Multi-Target Retrieval (MTR) func- tionality [4] and was extensively discussed in these studies. SCIAMACHY: a novel code was recently developed simulating the spectra with a Radiative Transfer Model (RTM) that takes into account single and multiple scattering processes. We used the DOAS technique that consist in obtaining at each tangent height (TH), the Slant Column Density (SCD) of the considered species, through a spectral fitting. The trace gas SCDs are then converted into number density by a tomographic approach that consists in one inversion applying 2-D box Airmass Factors (computed using the RTM) to the SCDs of all scanning sequences and THs simul- taneously [5, 6]. S ENSITIVITY We have developed diagnostics that quantify the sensitivity of the observations to different atmospheric locations for a specific species. For MIPAS the information load (Ω) has proved to be a meaningful quantifier [7, 8]. Ω measures the amount of information carried by that element with respect to a retrieval target and can be defined as: Ω(q,h)=(K T K) h =   l  i=1 m  j =1 n  k =1  ∂Y ijk ∂q h  2   1 2 - Ω(q,h) information load of clove h with respect to atmospheric parameter q - Y ijk spectral radiance of observation geometry i at wavenumber k of the analyzed MW j - l number of observation geometries that go through clove h - m number of analyzed MWs in observation geometry i - n number of spectral points in MW j K is the Jacobian matrix containing the derivatives of the observa- tions analyzed along the full orbit with respect to the elements of the state vector and S n is the variance-covariance matrix of vector n that contains the differences between each observation and the cor- responding simulation. If we take into account the correlations of the observations, Ω is weighted (W Ω ) and the formula becomes: W Ω (q,h)=(K T S -1 n K) h As an example in Fig. 1 are reported the W Ω distributions [7, 8] for temperature and ozone and for a selection of microwindows cur- rently adopted for the analysis of the MIPAS optimized resolution in the MIPAS2D database [9]. Figure 1: W Ω for temperature (top-) and ozone (bottom-panel) Through inspection of Ω , different microwindows can be selected in order to enhance the performance of a specific observation geometry in the altitude range of interest. The analysis can also be used to assess the retrieval potential and to select the retrieval grid. In the case of SCIAMACHY UV/VIS, inspection of the available information can be performed through the box airmass factors (Box- AMFs). An example is shown in Fig. 2 for ozone at 360 nm and 506 nm in a full limb orbit. The 2-D Box-AMFs measure the spatial sensitivity of the measurements to the gas present in the boxes in which the atmosphere has been stratified. They can be defined as follow: AMF gb = dSCD g dV CD b = - 1 h b d log I g dβ b - AMF gb Box-AMF for a certain box b at the geometry g - SCD g slant column densities for the geometry g - VCD b vertical column densities in the box b - I g intensity observed for the geometry g - β b absorption cross-section of the trace gas in the box b - h b vertical extension of the box b Calculation of the the Box AMFs were performed using the MOCRA 3-D backward Monte Carlo RTM [10]. Figure 2: 2-D Box-AMFs for ozone at 360 nm (top-) and 506 nm (bottom-panel) Decreasing the adopted wavelength to the visible region the atmo- sphere becomes less opaque and the sensitivity for lower atmo- spheric regions is enhanced. C ONCLUSIONS Through quantifiers that measure the information carried by mea- surements at different atmospheric locations, it is possible both to investigate the performance of an instrument operating in a certain spectral region and to support the definition of requirements for fu- ture Earth Explorers missions. We showed two example of instru- ments operating in infrared and in UV/VIS wavelengths introduc- ing the diagnostics information load for MIPAS and the box airmass factors distributions for SCIAMACHY. These diagnostics can be ex- ploited to support the selection of spectral points to be used for a specific target and altitude of interest. References [1] M. Kiefer et al. Impact of temperature field inhomogeneities on the retrieval of atmospheric species from MIPAS IR limb emission spectra. Atmos. Meas. Tech., 3:1487–1507, 2010. [2] M. Carlotti et al. Geo-fit approach to the analysis of satellite limb-scanning measurements. Appl. Opt., 40:1872–1885, 2001. [3] M. Carlotti et al. GMTR: Two-dimensional geo-fit multitarget retrieval model for Michel- son Interferometer for Passive Atmospheric Sounding/Environmental Satellite observations. Appl. Opt., 45:716–727, 2006. [4] B.M. Dinelli et al. Multi-Target Retrieval (MTR): the simultaneous retrieval of pressure, temperature and volume mixing ratio from limb-scanning atmospheric measurements. J. Quant. Spec. Radiat. Transfer, 84:141–157, 2004. [5] J. Puk ¸¯ ıte et al. Accounting for the effect of horizontal gradients in limb measurements of scattered sunlight. Atmos. Chem. Phys., 8:3045–3060, 2008. [6] E. Papandrea et al. Sounding the Upper Troposphere-Lower Stratosphere with Satellite mea- surements. Proc. ESA Living Planet Symposium, Bergen, Norway, 28/06-02/07 2010 (ESA SP-686). [7] M. Carlotti and L. Magnani. Two-dimensional sensitivity analysis of MIPAS observations. Opt. Expr., 17, No. 7, 2009. [8] M. Carlotti, E. Papandrea, and E. Castelli. Two-dimensional performance of MIPAS obser- vation modes in the upper-troposphere/lower-stratosphere. Atmos. Meas. Tech., 4:355–365, 2011. [9] B.M. Dinelli et al. The MIPAS2D database of MIPAS/ENVISAT measurements retrieved with a Multi-Target 2-dimensional tomographic approach. Atmos. Meas. Tech., 2:355–374, 2010. [10] M. Premuda et al. A Monte Carlo simulation of radiative transfer in the atmosphere applied to ToTaL-DOAS. in Remote Sensing of Clouds and the Atmosphere XIV, Proceedings of SPIE Vol. 7475, SPIE, Bellingham, WA 2009. Acknowledgements E. Papandrea, E. Arnone and E. Palazzi acknowledge support by ESA within the framework of the Changing Earth Science Network Initiative. View publication stats View publication stats