Retrieval of air temperature profiles in the Venusian mesosphere from VIRTIS-M data: Description and validation of algorithms

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi:1.129/28je375, 28 Retrieval of air temperature profiles in the Venusian mesosphere from VIRTIS-M data: Description and validation of algorithms Davide Grassi, 1 P. Drossart, 2 G. Piccioni, 3 N. I. Ignatiev, 4 L. V. Zasova, 4 A. Adriani, 1 M. L. Moriconi, 5 P. G. J. Irwin, 6 A. Negrão, 1,7 and A. Migliorini 3 Received 14 January 28; revised 9 May 28; accepted 2 June 28; published 1 October 28. [1] We present here methods developed for the retrieval of air temperature profiles in the Venusian mesosphere from the absolute radiances measured by the Visual and Infrared Thermal Imaging Spectrometer (VIRTIS) on board the Venus Express satellite. The infrared M channel of the instrument acquires multispectral images between 1 and 5 nm. In nighttime measurements, radiance in the range 38 5 nm is dominated by the thermal emission and absorption by the clouds and carbon dioxide. Since the latter is the main atmospheric component, it is possible to exploit the strong variability of its opacity in this spectral range, as resolved by the instrument, to reconstruct the vertical air temperature profile as a function of pressure. In this context we decided to adopt the Twomey et al. (1977) relaxation scheme. The resulting code was extensively tested on a set of simulated VIRTIS-M data. Comparison of the known input conditions with the results of analysis code allowed us to evaluate the systematic and random errors affecting the retrievals procedures on a statistical basis. The code returns the vertical air temperature profile with an uncertainty of less than 1 K in the region between 7 and 7 mbar (66 and 77 km above the reference surface) and less than 4 K throughout the entire range 1.1 mbar (64 95 km). Finally, we present the first examples of the code applied to actual measured Venusian data, demonstrating its capability to achieve a satisfactory modeling of the observations and provide physically reasonable results. Citation: Grassi, D., P. Drossart, G. Piccioni, N. I. Ignatiev, L. V. Zasova, A. Adriani, M. L. Moriconi, P. G. J. Irwin, A. Negrão, and A. Migliorini (28), Retrieval of air temperature profiles in the Venusian mesosphere from VIRTIS-M data: Description and validation of algorithms, J. Geophys. Res., 113,, doi:1.129/28je Introduction [2] The Visual and Infrared Thermal Imaging Spectrometer (VIRTIS) is one of the experiments included in the scientific payload of the Venus Express ESA mission [Titov et al., 26]. Its design is largely based on the heritage of the twin instrument on board the ESA Rosetta satellite. The instrument actually consists of an ensemble of different 1 Istituto di Fisica dello Spazio Interplanetario, Istituto Nazionale di Astrofisica, Rome, Italy. 2 Laboratoire d Etudes Spatiales et d Instrumentation en Astrophysique, Observatoire de Paris, Université Pierre et Marie Curie, Université Paris 7, CNRS, Meudon, France. 3 Istituto di Astrofisica Spaziale e Fisica Cosmica, Istituto Nazionale di Astrofisica, Rome, Italy. 4 Space Research Institute, Russian Academy of Sciences, Moscow, Russia. 5 Istituto di Scienze dell Atmosfera e del Clima, Consiglio Nazionale delle Richerche, Rome, Italy. 6 Clarendon Laboratory, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK. 7 Faculdade de Engenharia da Universidade do Porto, Porto, Portugal. subsystems, with specific measuring capabilities. VIRTIS-H is a high-resolution grating spectrometer operating in the range 3 5 nm, with a typical resolution and sampling step of 1.5 nm (variable along the range and with spectral order). VIRTIS-M is a spectro-imager, able to acquire a stack of monochromatic images that allow the spectrum to be reconstructed for each pixel. VIRTIS-M operates simultaneously in the visible and infrared part of the spectrum. The ranges covered are and 1 5 nm, with sampling steps of 19 and 11 nm, respectively. The VIRTIS-M and -H instantaneous fields of view (IFOV) are mrad (for an individual pixel) and mrad. These figures, together with the Venus Express orbital parameters, lead to a horizontal resolution for individual pixels of and km, respectively, in the case of measurements acquired at the apocenter. A complete description of the instrument and its radiometric performances is given by Piccioni et al. [27a]. The noise equivalent radiance pertinent to each measurement depends on several factors such as the instrument temperature and exposure time. For the purpose of this study, a value of 51 4 erg/(sec. cm 2 ster. nm) at 43 nm was assumed. Copyright 28 by the American Geophysical Union /8/28JE375 1of12

2 Figure 1. Example of a VIRTIS-M IR spectrum acquired on the Venus nightside. environment has been demonstrated by the wide literature derived from the data acquired by Pioneer Venus Infrared Radiometer and Venera 15 Fourier Transform Spectrometer (FTS). Some examples include a first study of air temperature fields in the mesosphere as a function of latitude and altitude [Taylor et al., 198; Moroz et al., 1986], aerosol vertical distributions [Zasova et al., 1999], and water vapor mapping [Ignatiev et al., 1999]. The experience accumulated by the corresponding scientific teams represents therefore the fundamental starting point for this kind of study. [5] The full exploitation of the VIRTIS data is a task well beyond the ambition of an individual article. In this paper we present the first successful retrievals derived from the VIRTIS- M IR subsystem data, aiming to determine the temperature structure of the Venusian atmosphere. Several concepts presented here have their roots in the previous studies by Grassi et al. [25], Zasova et al. [1999] and Roos-Serote et al. [1995]. For the sake of brevity, we will hereinafter use the term VIRTIS as synonymous of VIRTIS-M IR subsystem. [3] Once calibrated, the radiation field measured by VIRTIS in orbit around Venus is given by the radiative transfer equation [Hanel et al., 23, chap. 4]: I n A: B: C: D: t n;total ; m; f ¼ 1 m Zt n;total 1 4pm F n; 4p 1 4pm 1 v o;n t n ¼ t n e ðt n;total t n Þ=m B n T t n ¼ t n dt n þ Zt n;total Z 2p Z 1 Zt n;total 1 In t n ¼ t n ; m ; f dm df dt n þ e ð tn;total t n Þ=m ~p n t n ¼ t n ; m; f; m ; f ~p n t n ¼ t n ; m; f; m ; f Zt n;total Z 2p Z 1 1 e tn;total t n 2. Information Content of VIRTIS Data 2.1. Example of Venusian VIRTIS Spectra [6] Figure 1 presents an example of an actual VIRTIS Venusian nighttime observation. A visual inspection allows e ðt n;total t n Þ=m e ð tn;total t nþ=m dt n þ ð Þ=m S n t n ¼ t n ; m; f; m ; f dm df dt n ð1þ ð1þ where t n is the optical thickness, p n is the aerosol phase function, v, n is the aerosol single scattering albedo, B n is the Planck function and F,n is the solar flux density. [4] This expression includes the terms describing the thermal emission by the surface and atmosphere (A), as well as a modeling of the reflection of the solar radiation (C), multiple scattering phenomena (B) and other nonthermal emissions (D) such as radiation due to non-lte (local thermal equilibrium) phenomena. Term (B) makes (1) an integral equation, without analytical solutions. Even with a complete knowledge of the vertical distributions of aerosols and gases, air temperatures (driving thermal emission described by Planck function B) and optical properties of suspended materials, the analytical determination of the expected radiation field is not usually possible; consequently, it has to be estimated numerically. The inversion of (1), aiming to retrieve the above mentioned quantities, becomes therefore an extremely complex problem, often without unique solution [Rodgers, 1976]. Nevertheless, the scientific potential of infrared measurements in the study of Venusian us to identify the main spectral features and relate them with specific parameters of Venusian environment. [7] 1. For wavelengths less than 3 nm, the H 2 SO 4 droplets (the main cloud component) become much less absorbing and they behave, effectively, as conservative scatterers. Thermally emitted radiation from lower atmosphere (i.e., below the main cloud deck) and even from the surface may therefore escape to space in the windows such as those at 118, 174 and 23 nm that lie between strong atmospheric absorption bands. [8] 2. The signal level measured in the range nm follows roughly a Planckian shape (in its Wien tail), corresponding to the temperature of clouds where the optical thickness reaches a unity value. [9] 3. Two strong CO 2 absorptions bands are centered at 43 and 48 nm. [1] 4. The main CO band is evident as a continuum depression centered at 46 nm. [11] 5. Again in the range nm, deviations from an ideal Planckian shape, in the spectral intervals not 2of12

3 Figure 2. Levels of unit optical depth of different VIRTIS- M IR channels for typical Venusian conditions. The absorption of both clouds and CO 2 is included in the computations. severely affected by gases, are ascribed to variations of cloud optical properties Assessment of Retrieval Capabilities [12] The use of Bayesian formalism [Rodgers, 2] allows a precise assessment of retrieval capabilities offered by individual radiometric measurements, but its usage in the Venusian case is de facto precluded by the lack of reliable a priori information. The data sets collected by previous missions, despite their obvious scientific values, do not allow the compilation of a set of climatology statistics with adequate seasonal, longitudinal and local time coverage. Consequently, a heuristic analysis, to be confirmed by numerical tests, represents the most reasonable method to assess the information content offered by VIRTIS data. [13] At VIRTIS resolution, the CO nm bands are covered by about 9 sampling channels (hereinafter, the term channel will be used as a synonym for sampling position along the wavelength grid ). For typical Venusian temperatures and aerosol densities (Seiff [1983] and models references therein), the VIRTIS channels where S/N exceeds 3 reach a unity opacity in the pressure range between 1 and.1 mbar (64 95 km), when both aerosols and CO 2 are considered (Figure 2). These opacity values are derived from averages of monochromatic opacities weighted by the instrumental line shapes of individual channels. Our data may therefore carry information of the thermal structure of the atmosphere in the same pressure range, as far as term (A) in equation (1) dominates the emerging radiation field. [14] This condition is true only for nighttime data. During daytime, the 43-nm band is affected by the scattering of solar radiation by the Venusian clouds as well as by intense non-lte emission by CO 2 molecules in the higher atmosphere [Lopez-Valverde et al., 27]. The cumulative effects of these terms may easily reach a value of 4% of the total signal at 45 nm. Even if both effects could possibly be included in the modeling of observed radiances, the induced computational load is so heavy that we prefer to limit ourselves to the analysis of nighttime data in this first phase of data processing. [15] Analysis of the deep atmosphere temperature structure is also severely limited by current modeling issues. Radiances measured in the atmospheric windows commonly show variations of the order of 3 4%, which are interpreted as being due to variable thickness of the cloud decks [Taylor et al., 1997]. On the other hand, these lower clouds are mainly composed of mode 3 particles [Esposito et al., 1983], for which substantial uncertainties of optical properties, shape and composition exist. In this study, we considered the Venus aerosols model given by Crisp [1989]: (Mode 1: r eff =.49 mm, Mode 2 :r eff = 1.18 mm; Mode 2: r eff = 1.14 mm; Mode 3: r eff = 3.85 mm). [16] Absorption properties of CO 2 at the high temperatures and pressures of Venus lower atmosphere are poorly known at the present date [Taylor et al., 1997] and current data analysis attempts largely rely on empirical corrections. In these conditions, even if a temperature retrieval scheme may possibly be envisaged, its application to actual data processing would be questionable. Its development has therefore been postponed to later phases of our work and will not be described here. 3. Characteristics of Retrieval Code 3.1. Preliminary Considerations [17] According to equation (1), for nighttime observations, the observed radiance level depends on the behavior of atmospheric temperatures T as function of total opacity t, i.e., I = f(t(t)). For a given vertical distribution of optically active species, this is equivalent to the atmospheric temperatures as a function of altitude or pressure. As long as we neglect the CO band, the opacity between 35 and 51 nm is due to CO 2 and clouds. [18] 1. Carbon dioxide is the main constituent of Venus atmosphere, and has a constant mixing ratio of.965 in the mesosphere. Therefore, once a temperature profile is assumed, we can compute its density on a fixed pressure grid (and the related opacities) on the basis of hydrostatic equation and the perfect gas law. [19] 2. In the absence of CO 2 absorption, unity optical thickness would be reached in the spectral range of our interest in the upper cloud deck [Esposito et al., 1983]. Even though this structure appears less variable than its lower counterpart, variations of effective altitude and density decay scale heights have been reported by Zasova et al. [1999] on the basis of Venera 15 FTS data. [2] Since both air temperatures and aerosol densities are variable and contribute to total opacity, it appears that the two unknowns have to be retrieved simultaneously in order to achieve a self-consistent modeling of experimental data. Unfortunately, the optical properties of mode 2 particles (the main components of upper cloud deck) are almost constant in the VIRTIS spectral range and therefore it is extremely hard to discriminate between the effects of these two parameters. For example, in the case of a positive temperature lapse rate close to the cloud top, the same radiance level can be achieved either with a higher air temperature and high cloud or with a lower temperature and lower cloud. In numerical (noise-free) radiance simulations these two cases lead to different spectral intensities and shapes. 3of12

4 However, in the case of actual data, the magnitude of these differences is comparable to the modeling errors due to uncertainties on aerosol optical properties, radiative transfer approximations or residual calibration offset. Therefore, a nonuniqueness-of-solution problem exists at altitudes where the cloud opacity reaches unity values. On the basis of analysis of aerosol densities retrieved from Venera 15 FTS data [Zasova et al., 1999], we can see that this level of unity opacity is located, for most cases, around 1 mb Initialization [21] The initialization of the state vector (i.e., input quantities for the radiative transfer computation) is performed in two main phases: (1) definition of the quantities to be assumed as known and (2) setting of a first guess for the quantities to be retrieved. [22] The following list describes the preferred initialization options for phase 1 adopted during our analysis of measured Venusian spectra: [23] 1. Clouds were assumed to consist of liquid droplets of a mixture composed of 75% of H 2 SO 4 and 25% H 2 O, whose complex refractive indices were taken from Palmer and Williams [1975]. [24] 2. Since the upper cloud is mostly composed by Mode 2 particles, only this aerosol component has been included in our simulations. Following Crisp [1989], a lognormal distribution with r eff = 1.14 mm and variance of.23 mm was adopted. [25] 3. Gaseous CO 2 lines data were extracted from the HITRAN 24 database [Rothman et al., 25]. These data were used to build an expansion of preconvolved transmittance profiles in the Venusian mesosphere, according to the methods described by Zasova et al. [1999]. [26] During phase 2 of initialization, we should keep in mind that owing to the nonlinearity of the radiative transfer equation, an initial guess as close as possible to the true value is highly desirable to avoid convergence toward a purely mathematical, but unphysical solution. The analysis of VIRTIS data currently conducted in our team adopts the following guidelines: [27] 1. A study carried out on a wide population of simulated spectra (see section 4.1) allowed us to assess the correlation coefficient between the air temperatures at different pressure levels of the atmosphere with the observed brightness temperature at the different VIRTIS sampling points. The maximum correlation level as a function of sampling point matches closely the level of unit optical depth, plotted in Figure 2. About fifteen sampling points outside the CO contaminated region were selected to provide initial guesses of air temperatures at corresponding levels. These estimates were then fitted with a third degree polynomial in the entire pressure range considered in our computations. [28] 2. The aerosol density as a function of pressure was initialized to the average values derived from Venera 15 FST data. In the version of the retrieval code presented here, no effort was made to provide a more refined guess on the basis of the data. Future improvements of our code could benefit from a correlation analysis on simulated radiances carried out during our validation tests. This study demonstrated that the ratios of brightness temperatures measured in symmetric positions of the 48 nm CO 2 band with respect to its center are highly sensitive to effective scale height and altitude of the cloud deck Retrieval Algorithms [29] In our code, air temperatures and aerosol densities are retrieved in two separate steps, nested inside an outer iterative loop. This design is justified by the different retrieval algorithms employed for the two unknowns. [3] 1. The computation of vertical air temperature profile has been widely discussed in literature for the case of the Earth s atmosphere as well as for other planets. A general introduction on these topics is given by Hanel et al. [23, chap. 7]. In our case we adopted the algorithm proposed by Twomey et al. [1977], in the formulation presented by Zasova et al. [1999]. In this scheme, the temperature update between two consecutive iterative cycles is given by T iþ1 l ¼ T i l P m TB ch I ð obs;ch Þ W ch;l ch¼1 TB ch I i exp ected;ch P m W ch;l ch¼1 where j, ch, and l are the cycle, sampling channel and pressure level indices, respectively, and TB are the brightness temperatures corresponding to the radiances observed by VIRTIS (I obs ) or modeled according the current temperature profile (I expected ). W is the weighting function, i.e., W ch;l log 1 p p¼pl where T is the total atmospheric transmittance between space and a given pressure level. The Twomey et al. [1977] algorithm falls into the general class of relaxation methods, which are based on empirical corrections of a first guess profile, aiming mainly to achieve a perfect correspondence between the observed and synthetic spectrum, computed on the basis of the current value of the T(p) function. These methods are usually very robust, but suffer from weak theoretical basis. They are not able to take into account correlations between the temperatures at different levels of the atmosphere or the instrumental noise equivalent radiance (NER hereinafter). The latter point implies that they cannot provide an analytical estimation of the error on the retrieved solution, which shall therefore be assessed by means of numerical tests. Consequently, the only criteria to stop their iterations comes from the comparison between the NER, observed and modeled spectra. In our case, the atmosphere is modeled by a stack of 67 pressure levels logarithmically spaced between 12 and.5 mbar (49 16 km). The air temperature retrieval takes into account the VIRTIS radiances measured between 425 and 5 nm, excluding the spectral region dominated by CO centered roughly at 46 nm. The low-wavelength shoulder of the 43 nm CO 2 band proved very difficult to model, as observed by Roos-Serote et al. [1995]. These authors suggested line mixing as a possible cause of misfit. Since our radiative transfer model is not yet able to account for this effect, the nm region was not considered for our analysis purposes. ð2þ ð3þ 4of12

5 Figure 3. Distribution of cases considered for code validation in the latitude-local time space. Points, Venera 15 FTS measurements; triangles, selected cases considered for this study. [31] 2. The aerosol density profile is modeled as an exponential decay: nz ð l Þ ¼ nz ð Þe z l z ha where z is the nominal altitude of 12 mbar pressure level. With this assumption, n(z ) and h a become the two free parameters to be computed on the basis of the data. In this case, we adopted a classical Gauss-Newton retrieval scheme, where the partial derivatives of simulated radiances with respect to the two free parameters are evaluated by explicit differentiation at each iterative step. The iteration is stopped when variations of both free parameters between two successive iterative steps become less than 1%. The aerosol retrieval takes into account the VIRTIS radiances measured between 445 and 51 nm, but again excluding the spectral region dominated by CO. 4. Retrieval Code Performances [32] The retrieval code performances were assessed on statistical basis by applying the software on a wide population of simulated spectra. The comparison between the retrieval outcomes and the known input conditions allowed us to estimate the random and systematic components of the retrieval errors for Venus conditions Reference Simulated Spectra Set [33] Temperature and aerosol density profiles derived from analysis of the Venera 15 FTS were selected in order to achieve, as far as possible, a uniform coverage in the latitude and local time space. A total of 187 cases have been considered for the subsequent analysis (Figure 3). The low values of Venus orbit eccentricity and inclination of rotation axis ensure limited seasonal cycles as well as, to a first approximation, a symmetry in the atmospheric structures of the two hemispheres. Since the profiles are derived from actual data, we are confident that they retain the actual characteristics of Venus mesosphere and its variability. ð Þ ð4þ Figure 4. Random (dashed line) and systematic (solid line) components of air temperature retrieval errors for the developed algorithm. Values refer to VIRTIS-M data once spatial pixels have been averaged in 1 1 bins. [34] These data were used as input for a direct radiative transfer code with the same characteristics of that used in the retrieval algorithm, namely: [35] 1.CO 2 transmittances have been evaluated according to the preconvoluted transmittances approach. [36] 2. Aerosols have been assumed to be fully described by mode 2 particles. [37] 3. Scattering has been treated according to the TWOSTR algorithm by Kylling et al. [1995]. [38] For all cases, an emission angle equal to zero has been assumed. [39] The correspondence of the radiative transfer scheme in simulation and retrieval phases ensures that we are Figure 5. Correlation coefficients between retrieval errors at different altitudes. 5of12

6 systematic error component in our retrieval scheme, while the standard deviation gives a measure of its random part. Both are presented in Figure 4. Cumulative errors remain below 4 K in the entire range 1.1 mbar (64 95 km), and below 1 K in the indicative span 7 7 mbar (66 77 km). [42] Further tests, not shown here for shake of brevity, allowed us to identify the main causes of limitation in retrieval performance. [43] 1. At the higher altitudes, the limiting factor is represented by the instrumental noise. The higher part of the atmosphere is probed by the channels with stronger CO 2 absorption located at the center of the band, which have a very low signal level. A test carried out using the VIRTIS NER pertinent to individual spectra (i.e., not averaged on 1 1 bins) demonstrated that the upper boundary of region effectively probed by retrieval shrinks downward from.1 to 1 mbar (from 95 to 85 km). Figure 6. (a) Comparison between the retrieved and true value of cloud deck altitude, defined as the pressure level where unity optical thickness at 5 nm is achieved. Code performances are heavily affected by the nonuniqueness of the solution once we try to determine simultaneously air temperatures in the same pressure range. (b) The same as Figure 6a, but assuming the true air temperature profile during retrieval. actually investigating the performances of the algorithm itself and not other possible sources of error due to the required assumptions. [4] Random noise, with the same statistic of instrumental NER, was finally added to the simulated data. In actual operative conditions, VIRTIS-M monochromatic images are spatially degraded by averaging pixels on 1 1 bins, to achieve acceptable processing times. To account for that, the actual instrumental NER has been divided by 1 in order to simulate the increase in signal-to-noise ratio derived from the averaging process Performance Test Results [41] The case-by-case comparison between the retrieved and input temperature profiles allowed us to build, for each pressure level, a statistical population of retrieval errors. The average value of this population is interpreted as a Figure 7. Examples of the retrieval code capability to properly model the simulated observations. In the bottom panels, the solid line is the true spectrum, and the dashed line is the spectrum as modeled by retrieval code. A positive offset of was added to the latter curve for clarity. The top panels show the difference between true and modeled spectra. 6of12

7 retrieved temperature profiles were compared on a separated test and demonstrated to be less than 1 K above the reference level of 1 mbar. [48] The code, in its current implementation, performs a tentative retrieval of aerosol density profile, where actual free parameters are n(z ) and h a (see section 3.3 and equation (4)). This modeling is, however, intended only to define a continuum level for the CO 2 absorption. Numerical tests on simulated radiances actually demonstrated that the code is unable to retrieve properly the altitude of cloud deck; retrieved and true values of pressure level where unity optical thickness is achieved appear completely uncorrelated (Figure 6a). This fact has to be related with the nonuniqueness of solution discussed in section 3.1; assuming for each test case the corresponding true temperature profile allows us to properly retrieve aerosol densities in a variety of situations (Figure 6b). Similar numerical tests demonstrated Figure 8. Statistics of c 2 values computed from the fit between observed (simulated) and modeled spectra in the wavelength range [425, 445] nm. [44] 2. Errors in the retrieved aerosol densities are the main cause of temperature retrieval errors in the lower mesosphere. 1 mbar (64 km) is the indicative level where aerosol clouds reach a unity optical thickness at 45 nm. Assuming for each test case the corresponding true aerosol density (i.e., retrieving only the temperature profile) leads to a decrease from 1 to 3 mbar (from 64 to 59 km) of the lower boundary of the region effectively probed by retrieval. [45] Different options of the temperature profile initialization have a very limited impact on the retrieval performance, affecting mainly the behavior of systematic components outside the reference 1.1 mbar pressure range. Inside these boundaries, differences in retrieval performance with respect to the reference case of Figure 4 are less than.3 K. [46] Further insights on code behavior are provided by correlation studies on retrieval errors at different altitudes (Figure 5). This plot (1) shows how errors at different levels may compensate each other (regions of negative correlations) and (2) illustrates the vertical resolution of retrieval scheme. Actually, nonzero correlations between different levels indicate the occurrence of systematic components in retrieval errors along the vertical grid. These components have their ultimate cause in the finite width of weighting functions in equation (2), or, in other words, in the limited vertical resolution of retrieval. [47] The latter point is quite ambiguous owing to the presence of negative lobes, but it provides at least in the lower atmosphere an indicative figure similar to a gaseous scale height (of the order of 1 km for typical Venus conditions) and slightly less above 2 mbar (83 km). The error estimate shown in Figure 4 is considered as a minimum, since other potential sources of errors such as (1) radiative transfer approximations, (2) possible systematic calibration uncertainties or artifacts, and (3) uncertainties in the HITRAN parameters were not considered in this validation test. The effects of different radiative transfer schemes (namely, two streams and multi streams) on Figure 9. Examples of retrieval code capability to properly model the actual Venusian VIRTIS observations. In the bottom panels, the solid line is the true spectrum, and the dashed line is the spectrum as modeled by retrieval code. A multiplicative factor of was applied to the latter curve for clarity. The top panels show the difference between true and modeled spectra. Both examples are extracted from the VI38_2 frame. 7of12

8 Figure 1. Two-dimensional air temperature maps at (a) 9 and (b) 35 mbar levels (65 and 7 km), derived from the analysis of the VI38_2 frame. that in most cases, errors in the aerosol retrieval do not add errors in the temperature retrieval greater than 3 K above the 1 mbar level. [49] The code has been demonstrated to be capable of providing satisfactory modeling of simulated data in a variety of situations (Figures 7a and 7b). Nevertheless, the percentage of cases where the code was not able to reproduce observations within instrumental error is far from being negligible (Figure 8). A direct inspection shows how the most problematic cases are those related to weak thermal inversion and low altitude of cloud deck. In these conditions, the initial guess of aerosol density was demonstrated to be particularly poor and the code was not able to converge toward a realistic solution, oscillating until the maximum number of iteration was achieved. In these cases, however, systematic differences between observed and modeled radiances well above NER level appear around 5 nm, allowing the results to be effectively filtered. 5. Examples of Temperature Mapping From Venus Data [5] The purpose of this section is to provide to the reader an idea of the actual behavior of the code once applied to real Venus measurements. The modeling of Venus spectra provides usually very satisfactory fits (Figure 9). Systematic effects can be appreciated in the following spectral regions: (1) main CO band around 46 nm, not included in our simulations, (2) above 5 nm, in a region with weak CO 2 absorption, probably related to the approximate aerosol models adopted during retrieval or radiative transfer algorithms not being completely adequate, and (3) around the radiance local maximum at 427 nm, likely to be ascribed to residual registration errors in the VIRTIS wavelength sampling grid or to approximate values for instrumental resolution of individual sampling channels. [51] Figure 1 shows two typical two-dimensional maps of air temperature at two different pressure levels, derived from an individual frame acquired during orbit 38. The maps present quite smooth behavior, despite the fact that the analysis of each pixel was carried out completely independently from the surrounding ones. This fact demonstrates the stability of the retrieval code and its robustness against occasional calibration issues (dead columns, caused mainly by impinging cosmic rays). This fact is further confirmed by the analysis of partially overlapping frames that, when available and acquired within small time intervals, show Figure 11. Average temperature field along the meridian 18, as derived from frames VI63_1 to VI63_7. Local time is about 19. 8of12

9 Figure 12. Comparison of a VIRTIS frame documenting (a) the occurrence of the polar dipole (l =5mm) and the air temperature fields derived from the same frame at (b) 9 mbar and (c) 35 mbar. Temperature fields were derived from the VI38_2 frame and represent higher-resolution versions of Figure 1. 9of12

10 Figure 13. Sequence of air temperature fields along the meridian. The arrows highlight the patterns of the air temperature isosurface that appears to propagate between consecutive frames (cubes VI38_1 to VI38_4). The time interval between frames is exactly 1 hour. 1 of 12

11 Figure 14. (a) Standard deviation of the air temperature fields, as derived from the time sequence of Figure 13, local time 4. (b) The same as Figure 14a, but sequence VI63_1 to VI63_7, local time 19. differences in the temperature fields in the order of retrieval errors described in section Preliminary Analysis of Air Temperature Fields [52] A complete scientific analysis of results is currently being undertaken by the VIRTIS team, but cannot be considered as conclusive, owing to the limited numbers of observations processed so far. Moreover, owing to the orbital characteristics of the Venus Express orbiter, the available temperature fields document mainly the atmosphere conditions close to the southern pole. Preliminary findings are as follows: [53] 1. The main features of the air temperature fields (Figure 11) are in quantitative agreement with the previous results of Pioneer Venus Infrared Radiometer and Venera 15 FTS for the northern hemisphere [Taylor et al., 198; Zasova et al., 1999]. Namely, the occurrence of a cold collar at 1 mbar (64 km) centered at 65, and the rise of temperature toward the pole for a wide range of altitudes are observed. Further discussion about the general characteristics of the atmospheric temperature fields, their local time dependences and the derived winds are presented in the companion paper by Piccialli et al. [28]. [54] 2. The pattern of the polar dipole, as observed in VIRTIS frames at 5 mm [Piccioni et al., 27b], has an immediate correspondence in the thermal fields only in the lowest parts of probed range (around 9 mbar, 65 km), and is already barely visible at 35 mbar, i.e., 7 km (Figure 12). This evidence may put constraints on the vertical scale at which the dipole develops. The relevant pressure range is characterized by moderate to negative lapse rates. The higher part of probed pressure range is usually much less rich in detail. [55] 3. Comparison of temperature fields derived from frames closely spaced in acquisition times shows the occurrence of constant patterns, with a consistent evolution in time (Figure 13), which strongly suggest a wavelike activity. Related temperature variations are of the order of 5 K. Bearing in mind the retrieval errors of Figure 4, these values are considered as statistically meaningful. [56] 4. The standard deviation of air temperature derived from these sequences of frames allows us to assess the region of the atmosphere characterized by the strongest variability on timescales of an hour (Figure 14). The region around 1 mbar (86 km) is apparently characterized by the strongest variability, well above the random retrieval errors given in Figure 4. Notably, the variability of the atmosphere appears higher just after sunset than before dawn. 7. Conclusions [57] This paper describes the retrieval code in the form that is currently adopted by our team for the analysis of Venus VIRTIS-M data. The extensive validation and error characterization described here make it suitable for routine data processing for scientific purposes. Planned or inprogress studies include (1) phenomenological characterization of air temperature fields as a function of season, (2) their correlations with topography, airglow emissions, cloud altitudes and UV markings, (3) mesosphere energy budget and, in a longer-term perspective, (4) validation and assimilation in Venus global circulation models. Moreover, several improvements are already under development or can be envisaged for the near future: (1) testing of an adding/ doubling algorithm for radiative transfer, (2) extension of the algorithm concept to VIRTIS-H, (3) better initialization of the aerosol densities on the basis of thermal brightness ratios and statistics derived from Venera 15 FTS data, and (4) use of more realistic initial guesses for retrievals, as derived from the analysis of the measurements by other Venus Express instruments (SPICAV and VeRA). These improvements should allow a better characterization of the thermal status and aerosol densities in the lower mesosphere and therefore a more complete exploitation of the information content offered by the VIRTIS data set. 11 of 12

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Zasova, Space Research Institute, Russian Academy of Sciences, Profsojuznaja 84/32, , Moscow, Russia. P. G. J. Irwin, Clarendon Laboratory, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK. A. Migliorini and G. Piccioni, Istituto di Astrofisica Spaziale e Fisica Cosmica, Istituto Nazionale di Astrofisica, Via del Fosso del Cavaliere 1, I-133 Rome, Italy. M. L. Moriconi, Istituto di Scienze dell Atmosfera e del Clima, Consiglio Nazionale delle Richerche, Via del Fosso del Cavaliere 1, I-133 Rome, Italy. 12 of 12