Trace gas concentration retrieval from short-wave infrared nadir sounding spaceborne spectrometers

The remote sensing of short wave infrared (SWIR) radiation reflected from the Earth allows to infer atmospheric trace gas concentrations by solving the inverse problem. The retrieval algorithm BIRRA (Beer InfraRed Retrieval Algorithm) has been developed at the DLR (Deutsches Zentrum f¨ur Luft- und Raumfahrt) Remote Sensing Technology Institute (IMF) since around 2005 and is one of multiple algorithms to infer molecular concentrations from calibrated radiance spectra. BIRRA’s forward model is based on the Generic Atmospheric Radiation Line-by-line Infrared Code (GARLIC) which has also been developed at the DLR-IMF. First, the BIRRA retrieved carbon monoxide (CO) columns from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) 2.3 μm observations from 2003–2011 were validated against eighteen stations from the ground-based networks TCCON (Total Carbon Column Observing Network) and NDACC (Network for the Detection of Atmospheric Composition Change). The BIRRA inferred CO concentrations were found to be ≈10% low biased which is in large agreement with other similar studies. Next, the latest updates from the radiative transfer code GARLIC were incorporated in BIRRA’s forward model and the physical results of both, the old (but validated) and the latest (updated) BIRRA algorithms were verified and found to be numerically consistent for SCIAMACHY input data. Subsequently, the forward model was extended by upgrading its capabilities with respect to spectroscopy, i.e., enhanced line models were incorporated in order to utilize latest spectroscopic information from line lists such as the SEOM–IAS (Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy). More specifically, ‘beyond Voigt’ line profiles were implemented and the impact of the SEOM–IAS spectroscopy was studied with respect to latest compilations of HITRAN (HIgh-resolution TRANsmission molecular absorption database) and GEISA (Gestion et Etude des Informations Spectroscopiques Atmosphériques) for a large set of SCIAMACHY measurements. It was found that the SEOM–IAS line data and corresponding line models have significant impact on the spectral fitting: the residuals become smaller and the retrieved CO concentrations are also slightly different. The same methodology was then applied to study the spectroscopic impact on CO from S5P/TROPOMI measurements. The impact of the SEOM–IAS spectroscopy revealed to be even more pronounced, in particular with respect to the fitting residuals and smaller retrieval errors (higher precision) of the CO and co-retrieved parameters. Overall, the TROPOMI results are in agreement with that found for SCIAMACHY. A subsequent part of the thesis examines instrument spectral response functions (ISRF), in particular appropriate parameterizations for the TROPOMI’s SWIR band responses. A first assessment with tabulated instrument profiles indicates that the parameterized variants can mimic the tabulated responses within ≈3–6 %, depending on the instrument model and spectral position. The positive impact of the SEOM–IAS spectroscopy on the spectral fitting residuals could also be identified with the parameterized response functions. Moreover, the presented instrument profiles are considered promising candidates for the description of responses from upcoming sensors due to their flexibility. Finally, the co-retrieval of aerosol parameters in the CO fit is presented. Based on a simple model for the aerosol optical thickness the feasibility to co-retrieve aerosol extinction was investigated. In this context two different inverse solvers, namely the ’classical’ nonlinear least squares and separable least squares, were examined with respect to convergence. First results show a stable CO retrieval for the separable least squares solver, however, the co-retrieved aerosol and reflectivity parameters indicate issues due to degeneracies. This thesis improved the retrieval of CO from SCIAMACHY observations. Moreover, the upgraded BIRRA algorithm successfully retrieved CO concentrations from cloud-free TROPOMI measurements. Many aspects investigated in this study are also relevant for the retrieval of other atmospheric constituents, such such CO2 or CH4. The study does hence provide a proven basis for further developments.Aus der Beobachtung reflektierter Sonnenstrahlung im kurzwelligen Infrarot (SWIR) können Spurengaskonzentrationen in der Erdatmosphäre abgeleitet werden, wobei die Lösung des inversen Problems eine Schätzung des wahren Atmosphärenzustands liefert. Die Inversionsmethode BIRRA (Beer InfraRed Retrieval Algorithm) ist einer von mehreren am DLR (Deutsches Zentrum für Luft- und Raumfahrt) am Institut für Methodik der Fernerkundung (IMF) entwickelten Algorithmen zur Bestimmung von Molekülkonzentrationen aus spektroskopischen Messungen. Das Vorwärtsmodell von BIRRA basiert auf dem ebenfalls am DLR-IMF entwickelten Generic Atmospheric Radiation Line-by-line Infrared Code (GARLIC). Am Anfang stand die Validierung der mit BIRRA abgeleiteten Kohlenmonoxid (CO) Gesamtsäulen aus SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) Messungen im 2.3 μm Bereich. Dazu wurden die BIRRA Gesamtsäulen mit jenen der bodengebundenen Beobachtungsstationen der Netzwerke TCCON (Total Carbon Column Observing Network) and NDACC (Network for the Detection of Atmospheric Composition Change) im Zeitraum von 2003–2011 verglichen. Die mit BIRRA ermittelten CO Konzentrationen zeigen eine ≈10% negative Abweichung und stimmen mit den Ergebnissen ähnlicher Studien anderer Autoren weitgehend überein. Nach erfolgter Validierung wurden Neuerungen des Strahlungstransportmodells GARLIC in das BIRRA Vorwärtsmodell eingebaut und die Ergebnisse des aktualisierten Inversionsalgorithmus mit jenen des Vorgängers verglichen. Auf Basis von SCIAMACHY Daten wurde numerische Übereinstimmung der Ergebnisse festgestellt. Anschließend wurde das Vorwärtsmodell mit Blick auf die Verwendung neuester spektroskopischer Liniendaten, wie SEOM–IAS (Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy), erweitert. Um genauere Molekülabsorptionsquerschnitte berechnen zu können, musste das (klassische) Voigt-Absorptionslinienprofil erweitert werden. Der Einfluss der neuen Spektroskopie wurde zuerst auf Basis von SCIAMACHY Messungen untersucht und anhand von Vergleichsrechnungen auf Basis aktueller HITRAN (HIgh-resolution TRANsmission molecular absorption database) und GEISA (Gestion et Etude des Informations Spectroscopiques Atmosphériques) Daten bewertet. Es stellte sich heraus, dass die SEOM–IAS Liniendaten einen signifikanten Einfluss auf die Inversion haben: die Residuen werden kleiner und auch die abgeleiteten CO Konzentrationen unterscheiden sich leicht. Die gleiche Methodik wurde anschließend dazu verwendet, den Einfluss der Spektroskopie für das CO Retrieval aus TROPOMI Messungen zu bestimmen. Dabei zeigten sich die Auswirkungen noch deutlicher – signifikant kleinere Residuen und eine damit einhergehend höhere Genauigkeit (kleinere Fehler) der CO Säulen sowie der (mit-)abgeleiteten Parameter. Desweiteren besteht weitgehende Übereinstimmung mit den Resultaten der SCIAMACHY Studie. Ein weiterer Teil der Arbeit beschäftigt sich mit Instrumentenfunktionen (auch bekannt als Instrumentenprofile), speziell mit der Untersuchung einer passenden Parameterisierung der TROPOMI-Funktion im SWIR Band. Die tabellierten TROPOMI Instrumentenprofile können mit geeigneten Parameterisierungen gut modelliert werden. Darüber hinaus konnte der positive Einfluss der SEOM–IAS Spektroskopie auf die spektralen Residuen auch mit einem parameterisierten Instrumentenprofil nachgewiesen werden. Aufgrund der Flexibilität der vorgestellten Parameterisierungen könnten diese auch für zukünftige Sensoren zum Einsatz kommen. Abschließend wird der Einfluss von Aerosolen im CO Retrieval analysiert. Auf Basis einer einfachen Parameterisierung wurde versucht, die Extinktion bzw. die optische Tiefe (mit) zu bestimmen. In diesem Zusammenhang wurden auch der (klassische) nichtlineare Least Squares und der separierbare Least Squares hinsichtlich des Konvergenzverhaltens beim Lösen des inversen Problems untersucht. Erste Ergebnisse zeigen ein stabiles CO Retrieval unter Verwendung der separierbaren Least Squares Methode, wobei die (mit-)abgeleiteten Aerosol- und Reflektivitätsparameter auf Probleme durch Entartung hinweisen. Die vorliegende Arbeit hat gezeigt, wie das CO Retrieval aus SCIAMACHY Messungen verbessert werden kann. Mit dem weiterentwickelten BIRRA Code wurden darüberhinaus erfolgreich CO Konzentrationen aus wolkenfreien TROPOMI Messungen bestimmt. Viele Aspekte der Arbeit sind auch für die präzise Konzentrationsbestimmung anderer Moleküle wie CO2 oder CH4 von Bedeutung. Damit bietet die vorliegende Arbeit eine valide Grundlage für die Weiterentwicklung

Trace gas concentration retrieval from short-wave infrared nadir sounding spaceborne spectrometers

The remote sensing of short wave infrared (SWIR) radiation reflected from the Earth allows to infer atmospheric trace gas concentrations by solving the inverse problem. The retrieval algorithm BIRRA (Beer InfraRed Retrieval Algorithm) has been developed at the DLR (Deutsches Zentrum für Luft- und Raumfahrt) Remote Sensing Technology Institute (IMF) since around 2005 and is one of multiple algorithms to infer molecular concentrations from calibrated radiance spectra. BIRRA's forward model is based on the Generic Atmospheric Radiation Line-by-line Infrared Code (GARLIC) which has also been developed at the DLR-IMF. First, the BIRRA retrieved carbon monoxide (CO) columns from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) 2.3 micrometer observations from 2003-2011 were validated against eighteen stations from the ground-based networks TCCON (Total Carbon Column Observing Network) and NDACC (Network for the Detection of Atmospheric Composition Change). The BIRRA inferred CO concentrations were found to be circa 10% low biased which is in large agreement with other similar studies. Next, the latest updates from the radiative transfer code GARLIC were incorporated in BIRRA's forward model and the physical results of both, the old (but validated) and the latest (updated) BIRRA algorithms were verified and found to be numerically consistent for SCIAMACHY input data. Subsequently, the forward model was extended by upgrading its capabilities with respect to spectroscopy, i.e., enhanced line models were incorporated in order to utilize latest spectroscopic information from line lists such as the SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy). More specifically, 'beyond Voigt' line profiles were implemented and the impact of the SEOM-IAS spectroscopy was studied with respect to latest compilations of HITRAN (HIgh-resolution TRANsmission molecular absorption database) and GEISA (Gestion et Etude des Informations Spectroscopiques Atmosphériques) for a large set of SCIAMACHY measurements. It was found that the SEOM-IAS line data and corresponding line models have significant impact on the spectral fitting: the residuals become smaller and the retrieved CO concentrations are also slightly different. The same methodology was then applied to study the spectroscopic impact on CO from S5P/TROPOMI measurements. The impact of the SEOM-IAS spectroscopy revealed to be even more pronounced, in particular with respect to the fitting residuals and smaller retrieval errors (higher precision) of the CO and co-retrieved parameters. Overall, the TROPOMI results are in agreement with that found for SCIAMACHY. A subsequent part of the thesis examines instrument spectral response functions (ISRF), in particular appropriate parameterizations for the TROPOMI's SWIR band responses. A first assessment with tabulated instrument profiles indicates that the parameterized variants can mimic the tabulated responses within circa 3-6%, depending on the instrument model and spectral position. The positive impact of the SEOM-IAS spectroscopy on the spectral fitting residuals could also be identified with the parameterized response functions. Moreover, the presented instrument profiles are considered promising candidates for the description of responses from upcoming sensors due to their flexibility. Finally, the co-retrieval of aerosol parameters in the CO fit is presented. Based on a simple model for the aerosol optical thickness the feasibility to co-retrieve aerosol extinction was investigated. In this context two different inverse solvers, namely the 'classical' nonlinear least squares and separable least squares, were examined with respect to convergence. First results show a stable CO retrieval for the separable least squares solver, however, the co-retrieved aerosol and reflectivity parameters indicate issues due to degeneracies. This thesis improved the retrieval of CO from SCIAMACHY observations. Moreover, the upgraded BIRRA algorithm successfully retrieved CO concentrations from cloud-free TROPOMI measurements. Many aspects investigated in this study are also relevant for the retrieval of other atmospheric constituents, such such CO2 or CH4. The study does hence provide a proven basis for further developments

Impact of Molecular Spectroscopy on Carbon Monoxide Abundances from SCIAMACHY

High-quality observations have indicated the need for improved molecular spectroscopy for accurate atmospheric characterization. Line data provided by the new SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy) database in the shortwave infrared (SWIR) region were used to retrieve CO total vertical columns from a selected set of nadir SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY) observations. In order to assess the quality of the retrieval results, differences in the spectral fitting residuals with respect to the HITRAN 2016 (High-resolution TRANsmission molecular absorption) and GEISA 2015 (Gestion et Etude des Informations Spectroscopiques Atmosphériques) line lists were quantified and column-averaged dry-air CO mole fractions were compared to NDACC (Network for the Detection of Atmospheric Composition Change) and TCCON (Total Carbon Column Observing Network) ground-based measurements. In general, it was found that using SEOM-IAS line data with corresponding line models improve the spectral quality of the retrieval (smaller residuals) and increase the fitted CO columns, thereby reducing the bias to both ground-based networks

Computational Aspects of Speed-Dependent Voigt and Rautian Profiles

For accurate line-by-line modeling of molecular cross sections several physical processes "beyond Voigt" have to be considered. For the speed-dependent Voigt and Rautian profiles (SDV, SDR) and the Hartmann-Tran profile the difference of two complex error functions (essentially Voigt functions) has to be evaluated where the function arguments z± are given by the sum and difference of two square roots. These two terms describing z± can be huge and the default implementation of the difference can lead to large cancellation errors. First we demonstrate that these problems can be avoided by a simple reformulation of z-. Furthermore we show that a single rational approximation of the complex error function valid in the whole complex plane (e.g. by Humlicek, 1979 or Weideman, 1994) allows an evaluation of the SDV and SDR with four significant digits or better. Our benchmarks indicate that the SDV and SDR function evaluations are about a factor 2.2 slower compared to the Voigt function, but for evaluation of molecular cross sections this time lag does not significantly prolong the overall program execution because speed-dependent parameters are available only for a fraction of strong lines

Atmospheric methane with SCIAMACHY: Operational Level 2 data analysis and verification

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

Impact of Molecular Spectroscopy on Carbon Monoxide Abundances from TROPOMI

The impact of SEOM-IAS (Scientific Exploitation of Operational Missions-Improved Atmospheric Spectroscopy) spectroscopic information on CO columns from TROPOMI (Tropospheric Monitoring Instrument) shortwave infrared (SWIR) observations was examined. HITRAN 2016 (High Resolution Transmission) and GEISA 2015 (Gestion et Etude des Informations Spectroscopiques Atmosphériques 2015) were used as a reference upon which the spectral fitting residuals, retrieval errors and inferred quantities were assessed. It was found that SEOM-IAS significantly improves the quality of the CO retrieval by reducing the residuals to TROPOMI observations. The magnitude of the impact is dependent on the climatological region and spectroscopic reference used. The difference in the CO columns was found to be rather small, although discrepancies reveal, for selected scenes, in particular, for observations with elevated molecular concentrations. A brief comparison to Total Column Carbon Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC) also demonstrated that both spectroscopies cause similar columns; however, the smaller retrieval errors in the SEOM with Speed-Dependent Rautian and line-Mixing (SDRM) inferred CO turned out to be beneficial in the comparison of post-processed mole fractions with ground-based references

The CO2Image mission: retrieval studies and performance analysis

The CO2Image satellite mission, led by the German Aerospace Center (DLR), aims to demonstrate the feasibility of quantifying carbon dioxide (CO2) and methane (CH4) emissions from medium-size point sources. Several DLR institutes are currently working on the reliminary design phase (Phase B) of the mission. Here we present a performance analysis based on the current instrument specifications. The Beer InfraRed Retrieval Algorithm (BIRRA), the line-by-line radiative transfer model Py4CAtS (Python for Computational ATmospheric Spectroscopy) and a COSIS (Carbon dioxide Sensing Imaging Spectrometer) instrument model are employed to infer CO2 and CH4 concentrations from synthetic COSIS spectra. We evaluate the instrument's performance and determine if it meets the intended requirements. The study assesses uncertainties in the retrieved concentrations as well as errors in point source emission estimates caused by instrument noise. First results suggest that the detection and quantification limits stated in the mission requirements document are justified. The analysis also demonstrates that retrieval errors tend to increase when the signal-to-noise ratio is low, complicating the distinction between emission sources and background concentrations. Furthermore, we discuss non-instrumental effects and demonstrate that the fit quality significantly improves if a low-level plume is scaled instead of a background reference profile that covers the atmosphere's full vertical extent. The analysis on heterogeneous scenes (high albedo contrast) reveals that the various instrument setups perform similarly for both molecules