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It was brought to our attention that, due to a recent change of the Total Carbon Column Observing Network (TCCON) Data Use Policy, citation of the individual TCCON dataset references is now mandatory. With this addendum, an Appendix is added to the original manuscript containing the missing dataset references and coorperating institutions for TCCON and the Network for the Detection of Atmospheric Composition Change (NDACC), respectively.

Verification and validation are critical elements of any code development, and mandatory for the assessment of spaceborne remote sensing products. The objective was to perform intercomparisons of CO columns estimated from SCIAMACHY with temporal coincidented and co-located retrievals provided by ground-based Fourier transform infrared spectroscopy. More specifically, we used column averaged dry air mole fractions from several NDACC (Network for the Detection of Atmospheric Composition Change) and TCCON (Total Carbon Column Observing Network) stations. Like SCIAMACHY's channel 8 the TCCON instruments utilize the 2.3 mue band of carbon monoxide, whereas NDACC observes the CO mid infrared absorption. In most cases, satellite validation is based on statistical comparison (specifically true for SCIAMACHY with its large signal to noise ratio per observation (Gimeno Garcia et al. 2011)) with reference data. However, satellite and reference measurements do neither exactly match in time and space (mistime and misdistance) nor address the same volume of air (misintegration). Hence, the natural atmospheric variability leads to differences between both data sets and these differences must not be interpreted in terms of a satellites instrument malfunction (Verhoelst et al. 2015). The validation strategy presented here accounts for spatial (and in selected cases temporal) induced mismatches. The outcome of the study demonstrates that spatial (and temporal) averaging is required in order to minimize representation errors. Increased deviations of the spaceborne and ground-based columns in the later years of the mission clearly demonstrated the impact of the degrading channel 8 detector. Therefore, in order to perform a comprehensive full-mission (2003 - 2012) validation of the SCIAMACHY dataset, an approach providing more observations within a given time interval and sampling area had been utilized. The results reveal that the combination of these methods lead to acceptable agreement of SCIAMACHY CO data with most g-b reference sites (within the standard deviation). In addition the effect of whether using the mean or median of the datasets for comparison was investigated. The outcome demonstrates that the differences between the SCIAMACHY CO mean and median values are within 1-10 percent in most cases and within the standard deviation of the reference observations.

SCIAMACHY is a passive imaging spectrometer mounted on board ESA’s ENVISAT satellite to probe a large number of atmospheric trace gas species, such as methane, and their global distribution and evolution. Methane (CH4) is particularly interesting as it is one of the most abundant greenhouse gas in the Earth atmosphere. To analyze SCIAMACHY methane measurements, we used the DLR BIRRA (Beer InfraRed Retrieval Algorithm) to retrieve nadir methane concentrations from its infrared spectra in channel 6. By integrating the DLR BIRRA code into ESAs operational Level 2 processor, we expanded it to include atmospheric CH4 column measurements. We have therefore performed an extensive test and verification operation. Our tests are based on separate comparisons with existing space and ground-based obtained measurements of methane column density. We present here our strategy for quality check of this first version of a CH4 product. We will further discuss specific geographical areas we used to validate the products.

Line-by-line models have become mandatory for many atmospheric spectroscopy applications. A large variety of models has been developed in the past, but in general these codes work as a kind of 'black box' where intermediate quantities such as cross sections are difficult to access. Py4CAtS --- PYthon scripts for Computational ATmospheric Spectroscopy is a Python re-implementation of our Fortran infrared radiative transfer code GARLIC (Generic Atmospheric Radiation Line-by-line Infrared Code), where compute-intensive code sections utilize the Numeric/Scientific Python modules for highly optimized array-processing. The individual steps of an infrared or microwave radiative transfer computation are implemented in separate scripts to extract lines of relevant molecules in the spectral range of interest, to compute line-by-line cross sections for given pressure(s) and temperature(s), to combine cross sections to absorption coefficients and optical depths, and to integrate along the line-of-sight to transmission and radiance/intensity. Py4CAtS can be used in two ways, from the Unix/Linux (or Windows/Mac) console/terminal or inside the (i)python interpreter. The basic design of the package, numerical and computational aspects relevant for optimization, and a sketch of the typical workflow are presented.

Carbon monoxide and methane are key species of Earth's atmosphere. Accordingly, a large number of spaceborne sensors are observing these species in the microwave, thermal and near infrared. For the analysis of short wave infrared spectra measured by SCIAMACHY and similar instrument(s) we had developed the Beer InfraRed Retrieval Algorithm: BIRRA is a separable least squares fit of the measured radiance with respect to molecular column densities and auxiliary Parameters optional: surface albedo, baseline, slit function width, and wavenumber shift). BIRRA has been implemented in the operational SCIAMACHY L1 to 2 processor for the retrieval of CO and CH4 from channel 8 and 6, respectively; the validation study reported here is based on the BIRRA prototype version giving more flexibility. Verification and validation are critical elements of any code development, and mandatory for the assessment of spaceborne remote sensing products. In this poster we report on intercomparisons of CO columns estimated from SCIAMACHY with coincident and co-located retrievals provided by ground-based Fourier transform infrared spectroscopy. More specifically, we have used data from several NDACC (Network for the Detection of Atmospheric Composition Change) and TCCON (Total Carbon Column Observing Network) stations. Like SCIAMACHY's channel 8 the TCCON instruments utilize the 2.3 µm band of carbon monoxide, whereas NDACC observes the CO mid infrared absorption. The results indicate the importance of appropriate sampling areas around the station, calibration of level 1 data, and post-processing of the BIRRA retrievals (esp. filtering). Furthermore, the increased deviations of the spaceborne and ground-based columns in the later years of the mission clearly demonstrate the impact of the degrading channel 8 detector. Overall, the CO columns derived from SCIAMACHY and NDACC/TCCON largely agree within the error bars.

Clouds play an important role in the radiative transfer of planetary atmospheres: they are key elements of the climate system and influence the planet’s spectral appearance. Given the thousands of exoplanets discovered so far, including some dozens of Earth-sized exoplanets, the feasibility of remote sensing of exoplanet atmospheres is attracting increasing attention. Here we present a study of the thermal emission of cloud-covered Earth-like exoplanets orbiting in the habitable zone of F, G, K, and M-type stars. A line-by-line model for molecular absorption has been coupled to a discrete ordinate multiple scattering radiative transfer solver. Pressure, temperature, and molecular concentration profiles were taken from a consistent radiative-convective climate model including a parameterized cloud description (Kitzmann et al., A&A, 2010). The main focus of the current work is the impact of multi-layer clouds on emission spectra in the thermal infrared. The effects of low-level water clouds and high level ice clouds simultaneously on signatures of H2 O, CO2 , O3 , etc will be studied for various resolutions. Furthermore, comparisons with spectra resulting from a low-resolution code will be shown.

Despite of significant advances of computational power, line-by-line modeling of molecular absorption and radiative transfer remains a challenge. In case of Earth atmospheric remote sensing, the number of spectra to be processed operationally has grown substantially. In contrast the number of planetary spectra is quite limited, however, the number of spectroscopic transitions to be considered, esp. for hot Jupiters (or Venus in our solar system), has increased to millions or billions. In this contribution we present our approach to tackle this computational challenge in our lbl atmospheric radiative transfer code GARLIC (Generic Atmospheric Radiation Line-by-line Infrared Code, Fortran 2003). A highly optimized Voigt function algorithm along with a 'multigrid' approach is employed for computation of lbl molecular absorption cross sections. Multithreading by OpenMp allows to further speed-up the cross section, absorption coefficient, and atmospheric radiative transfer modules. Furthermore, we briefly discuss the option of hardware accelerators for the most demanding cross sections. Finally, Jacobians are required to solve the nonlinear inverse problems to be solved for the analysis of an increasing number of exoplanet observations and we present algorithmic differentiation as a technique to implement accurate and efficient derivatives.

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) was a scanning nadir and limb spectrometer covering the wavelength range from 212 nm to 2386 nm in 8 channels. It is a joint project of Germany, the Netherlands and Belgium and was launched in February 2002 on the ESA's ENVISAT platform. After the platform failure in April 2012, the space Agencies continue to support the operational product evolution in the frame of the SCIAMACHY Quality Working Group and Expert Support Laboratory.. Note that SCIAMACHYs originally specified in-orbit lifetime was twice the planned lifetime. The instrument was designed to measure column densities and vertical profiles of trace gas species in the troposphere, in the stratosphere and in the mesosphere (Bovensmann et al., 1999). It can detect a wide range of trace gases and can provide information about aerosols and clouds. For the next Level 1 processor several improvements that concern the SWIR products CH4 and CO are planned like a better dark correction, an improved spectral calibration and a better bad pixel mask. Using the BIRRA (the Beer InfraRed Retrieval Algorithm)retrieval scheme which is also the basis of the operational processing, we will analyse the impact of the various Level 1 improvements on the CH4 and CO products. The results of this sensitivity study will be shown in this contribution.

GARLIC has been developed with emphasis on efficient and reliable numerical algorithms and a modular approach appropriate for simulation and/or retrieval in a variety of applications. It is suitable for arbitrary observation geometry, instrumental field-of-view, and line shape. The core of GARLIC's subroutines constitutes the basis of forward models used to implement inversion codes to retrieve atmospheric state parameters from limb and nadir sounding instruments. In view of the computational burden of lbl modeling considerable effort has been devoted to optimized algorithms. Collision induced absorption has been implemented as a complement of the H2O, CO2, etc continua. Furthermore, the DISORT multiple-scattering solver has been added to GARLIC for an assessment of the impact of clouds on exoplanet spectra. As a recent upgrade Jacobians with respect to temperature have been implemented utilizing algorithmic differentiation. In this contribution we summarize GARLIC's main features and give some examples from Earth and planetary science.

The SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) aboard Envisat observed the Earth’s atmosphere in nadir, limb and solar/lunar occultation geometries covering the UV-Visible to NIR (240-2380 nm) spectral range with a moderate spectral resolution of 0.2-1.5nm. The instrument provided decade long coverage (2002-2012) of various atmospheric parameters from the troposphere up to the mesosphere. These decadal datasets are important building blocks for long term assessments of climate relevance. The SCIAMACHY Quality Working Group (SQWG), formed in 2007, aims at improving the quality of the operational data products. University of Bremen (IUP), BIRA, DLR-IMF, SRON and KNMI are the members providing expertise in this group. Since the establishment of SQWG, the ESA operational Level 2 processor was significantly improved w.r.t. data quality and the product list was substantially enhanced with new parameters. The current project builds on the heritage of previous SQWG projects and has the following objectives: update the Level 0-2 processing chain, deliver the processor baseline enabling the generation of a quality controlled Level 1b and Level 2 dataset for the whole mission and to ensure long-term usability of the data. The SQWG Level 2 team is working on the improvement and implementation of processor Version 6 and the future Version 7. Under the SQWG framework, the main focus is on the following products: in nadir mode, total columns of O3, NO2, CO, IO and the tropospheric columns of NO2, and O3 (from limb-nadir matching) and the tropospheric columns of BrO and HCHO and, in limb, the aerosol and water vapour profiles and cloud flagging improvement and implementation. Here we present the key results of SCIAMACHY Level 2 Version 6 and Version 7 implementation and verification activities.

More than one thousand exoplanets have been discovered in the past two decades, with some dozen of them in the host stars’ habitable zone and with size and mass similar to Earth. Furthermore, spectra of exoplanets become available with reasonable quality (resolution and noise) that trigger the question of remote sensing of the planet’s atmosphere. The objective of this sensitivity study is to identify the optimal state vector representing the atmosphere in the inverse problem. Solving the inverse problem will ultimately allow to characterize the planet and determine its habitability. Using a high resolution infrared radiative transfer code with a line-by-line molecular absorption model, we calculate synthetic spectra of exoplanets orbiting dwarf stars. Key parameters describing the atmosphere (i.e., molecular abundances, temperature, pressure) are identified and the Jacobians (i.e. partial derivatives of the spectra) are evaluated to investigate the feasibility to retrieve the state of the planetary atmosphere.

Among the various input data required for retrieval of atmospheric state parameters from infrared remote sensing observations molecular spectroscopy line data have a central role, because the data quality is critical for the quality of the retrieval product. Here we discuss the impact of the line parameters on vertical column densities (VCD) estimated from short wave infrared nadir observations. Using BIRRA (the Beer InfraRed Retrieval Algorithm) comprising a line-by-line radiative transfer code (forward model) and a separable nonlinear least squares solver for inversion we retrieve carbon monoxide from observations of SCIAMACHY aboard Envisat. Retrievals using the two recent versions of HITRAN (2008 and 2012) and GEISA 2011 have been performed and the results are compared in terms of residual norms, molecular density scaling factors, their corresponding errors, and the final VCD product. The retrievals turn out to be quite similar for all three databases, so a definite recommendation in favor of one of these databases is difficult for the considered spectral range around 2.3mue. Nevertheless, HITRAN 2012 appears to be advantageous when evaluating the different quality criteria.

In order to guarantee high signal-to-noise ratios, the design of spectrometers aboard spaceborne platforms is a tradeoff between spectral and spatial resolution. Since molecular absorption is highly spectrally dependent, atmospheric composition instruments favor spectral over spatial resolution. The TROPOMI instrument aboard Sentinel 5 Precursor (S5P) will have a spatial resolution of about 7x7 km2 at nadir, which clearly outperforms the resolution of previous atmospheric missions (320x40 km2 for OME/ERS-2, ~120x30 km2 for SCIAMACHY/ENVISAT and 80x40 km2 for GOME-2/Metop-A,B). However, inside a TROPOMI ground pixel there may still be a considerable amount of unresolved cloud variability. In this work, we present a sensitivity study of measured reflectivities as a function of the unresolved cloud variability. For this purpose, we simulate virtual measurements of a TROPOMI-like instrument in cloudy scenes at different spatial resolutions by means of the three-dimensional (3D) radiative transfer model MoCaRT (Monte Carlo Radiative Transfer). The reference inside-pixel cloud microphysical properties are provided by the PArallelized Large-Eddy-Simulation Model (PALM) at a spatial resolution of 10x10 m2 in a 6.4x6.4 km2 domain. The retrieval algorithms of both, atmospheric molecules and cloud properties rely on one-dimensional radiative transfer (RT) models which do not account for neither cloud variability nor 3D RT effects. Moreover, the quality of atmospheric gas retrievals strongly depends on the accuracy of the cloud information. Accordingly, we first analyze the 3D effects on the cloud products by ingesting the 3D simulated spectra (around the oxygen A-band) to the ROCINN_CAL cloud algorithm. In a second step, the impact of the cloud variability on the ozone product is assessed (which also include the indirect impact on cloud properties).

Nadir infrared (IR) sounding can be used to derive information on trace gases relevant for climate and air quality. For vertical column density retrievals using SCIAMACHY near IR nadir observations, the BIRRA (Beer InfraRed Retrieval Algorithm) code has recently been implemented in the operational level 1 – 2 processor. For analysis of thermal IR nadir observations of AIRS, GOSAT, IASI, or TES, a closely related code CERVISA (Column EstimatoR Vertical Infrared Sounding of the Atmosphere) has been developed. Both codes share a large portion of modules, e.g., for line-by-line absorption and the nonlinear least squares solver. The essential difference is the part of the forward model devoted to radiative transfer through the atmosphere, i.e., Beer’s law for the near IR versus Schwarzschild’s equation for the thermal IR. For the ongoing validation of the BIRRA carbon monoxide CO and methane CH4 products intercomparisons with thermal IR sounding data are performed. CERVISA retrieval results are compared both to the operational products of the IR sounder considered and to SCIAMACHY products retrieved with BIRRA.

The SCIAMACHY near infrared (NIR) channels 6-8 are suitable to retrieve vertical column densities (VCDs) of atmospheric gases such as CO, CH4, CO2 or H2O. BIRRA will be used in the coming version 5 of the offline Level 1b to 2 SCIAMACHY processor to retrieve CO values operationally. The quality of VCD retrievals depends on theoretical, computational and observational issues. Realistic atmospheric data as well as efficient yet accurate, robust radiation transfer models and inversion methods have to be used. However, especially operational applications require to keep computational time within strict limits and the best possible models are chosen according to this restriction. The quality of the measured spectra greatly affects the accuracy of the retrievals. Since model parameters are optimally varied during the inversion process to mimic the measured values, errors in the input spectra will lead to wrong retrievals. SCIAMACHY spectra are spectrally and radiometrically calibrated and corrected for several effects, namely: Leakage current, pixel-to-pixel gain, non-linear response, straylight, polarisation. Reflectances are calculated using inflight sun diffuser spectra. Additionally, the degradation of the instrument is being monitored and the quality of the individual spectral pixels assessed. It turned out that the effect of the bad/dead pixel mask (BDPM) has a major impact on the retrievals. Focussing on carbon monoxide, this work aims at studing the effect of different calibration steps on the retrieval of atmospheric gas VCDs.

Since the foundation of the SCIAMACHY Quality Working Group (SQWG) in a joint ESA-DLR-NIVR inter-agency effort in late 2006, the ESA operational Level 2 processor was significantly improved w.r.t. data quality and product range. During the last two years the product list was substantially enhanced by new (total columns of SO2, BrO, OClO, H2O, CO, Limb BrO profiles, Limb cloud flags) and improved products (total columns of O3, NO2, Absorbing Aerosol Index, Limb O3 profiles, Limb NO2 profiles). For example, important improvements were achieved in the O3 and NO2 profile calculation by implementing an upgraded retrieval scheme and using now Level 1b version 7.0 data with an improved pointing correction. Nadir products of total column O3 and Absorbing Aerosol Index were improved by applying a radiometric degradation correction (m-factors) in the Level 1 to 2 processing step.

Regularization methods for the inversion of infrared nadir sounding observations are currently investigated. An iterative Runge-Kutta type method for nonlinear ill-posed problems has been implemented and its performance has been studied using synthetic measurements. Comparisons with Tikhonov type inversion with a priori regularization parameter selection indicate that both methods are of similar accuracy; however, the Runge-Kutta method is less sensitive to regularization parameter variations. Furthermore, vertical column density retrieval from nadir infrared sounders such as AIRS will be used for validation of column densities retrieved from near infrared SCIAMACHY observations. Two closely related retrieval codes are used for L2 processing of SCIAMACHY near infrared and AIRS mid infrared spectra. First results of this intercomparison are shown.

The practical handling and analysis of satellite data is outlined using the programming language Python. The limb sounding technique of the SABER instrument on board of the TIMED satellite delivers vertical profiles of kinematic temperature from the stratosphere (∼30 km) up to the lower thermosphere (∼120 km). The procedure may be summarised as follow: In the first step the level 2 data for one month are extracted from the netCDF format and arranged into a new altitude-latitude grid for the ascending and descending orbits. The longitudinal structure is rearranged applying the decomposition into zonal harmonics. Various cross sections of the data give a good overview of the thermal structure and dynamics of the atmosphere up to 120 km. The monthly values of the zonal averaged temperature are compared to the available data from stratospheric reanalyses up to 60 km as well as the initialized background climatology of general circulation models for the middle atmosphere. In diesem Artikel soll der praktische Umgang mit Satellitendaten und deren Auswertung unter Verwendung der Programmiersprache Python skizziert werden. Auf der Basis der Horizontsondierungen des SABER Instruments auf dem TIMED Satelliten werden vertikale Profile wie die kinetischen Temperatur von der Stratosphäre (∼30 km) bis zur unteren Thermosphäre (∼120 km) gewonnen. Die Arbeitsschritte bei der Analyse lassen sich wie folgt gliedern: Als erstes werden die Level 2 Produkte eines Monats aus dem netCDF Format extrahiert und an ein neues Höhen-Breiten Gitter für jeden auf- und absteigenden Orbit angepasst. Die Längenstruktur wird mit Hilfe einer Zerlegung in harmonische Funktionen regularisiert. Diverse Querschnitte der Daten geben ein guten Überblick über die thermischen Struktur und Dynamik der Atmosphäre bis 120 km. Die Monatswerte des Zonalmittels der Temperatur werden mit denen aus operationellen Reanalysedaten (∼60 km) sowie der Hintergrundklimatologie von Zirkulationsmodellen der mittleren Atmosphäre verglichen.

Nadir observations in the shortwave infrared channels of SCIAMACHY onboard the ENVISAT satellite can be used to derive information on CO, CH4, N2O, CO2, and H2O. BIRRA (Beer InfraRed Retrieval Algorithm) is a least squares fit of the measured radiance with respect to molecular column densities and auxiliary parameters, nb. surface albedo, baseline and slit function width. Here special features of the code are shown along with results of carbon monoxide retrievals from SCIAMACHY near infrared nadir observations. In particular intercomparisons with other SCIAMACHY Infrared Nadir retrievals and with AIRS (Atmospheric Infrared Sounder onboard NASA-EOS-Aqua) will be presented.

Nadir observations in the shortwave infrared channels of SCIAMACHY onboard the ENVISAT satellite can be used to derive infor- mation on CO, CH4, N2O, CO2, and H2O. BIRRA (Beer InfraRed Retrieval Algorithm) is a least squares fit of the measured radiance with respect to molecular column densities and auxiliary parameters, nb. surface albedo, baseline and slit function width. Here special features of the code are shown along with results of carbon monoxide retrievals from SCIAMACHY near infrared nadir observations. In particular intercomparisons with other SCIAMACHY Infrared Nadir retrievals and with AIRS (Atmospheric Infrared Sounder onboard NASA-EOS-Aqua) will be presented.