Design and description of the MUSICA IASI full retrieval product

IASI (Infrared Atmospheric Sounding Interferometer) is the core instrument of the currently three Metop (Meteorological operational) satellites of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites). The MUSICA IASI processing has been developed in the framework of the European Research Council project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). The processor performs an optimal estimation of the vertical distributions of water vapour (H$_{2}$O), the ratio between two water vapour isotopologues (the HDO/H$_{2}$O ratio), nitrous oxide (N$_{2}$O), methane (CH$_{4}$), and nitric acid (HNO$_{3}$) and works with IASI radiances measured under cloud-free conditions in the spectral window between 1190 and 1400 cm$^{-1}$. The retrieval of the trace gas profiles is performed on a logarithmic scale, which allows the constraint and the analytic treatment of ln [HDO]−ln [H$_{2}$O] as a proxy for the HDO/H$_{2}$O ratio. Currently, the MUSICA IASI processing has been applied to all IASI measurements available between October 2014 and June 2021 and about two billion individual retrievals have been performed

Design and description of the MUSICA IASI full retrieval product

We present atmospheric H2O, HDO / H2O ratio, N2O, CH4, and HNO3 data generated by the MUSICA IASI processor using thermal nadir spectra measured by the IASI satellite instrument. The data have global daily coverage and are available for the period between October 2014 and June 2021. Multiple possibilities of data reuse are offered by providing each individual data product together with information about retrieval settings and the products' uncertainty and vertical representativeness.This research has been supported by the European Research Council, FP7 Ideas (MUSICA (grant no. 256961)); the Deutsche Forschungsgemeinschaft (grant nos. 290612604, project MOTIV, and 416767181, project TEDDY); the Ministerio de Economía y Competitividad (grant no. CGL2016-80688- P, project INMENSE); the Bundesministerium für Bildung und Forschung (ForHLR supercomputer); and the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (ForHLR supercomputer)

Synergetic use of IASI and TROPOMI space borne sensors for generating a tropospheric methane profile product

The thermal infrared nadir spectra of IASI (Infrared Atmospheric Sounding Interferometer) are successfully used for retrievals of different atmospheric trace gas profiles. However, these retrievals offer generally reduced information about the lowermost tropospheric layer due to the lack of thermal contrast close to the surface. Spectra of scattered solar radiation observed in the near and/or short wave infrared, for instance by TROPOMI (TROPOspheric Monitoring Instrument) offer higher sensitivity near ground and are used for the retrieval of total column averaged mixing ratios of a variety of atmospheric trace gases. Here we present a method for the synergetic use of IASI profile and TROPOMI total column data. Our method uses the output of the individual retrievals and consists of linear algebra a posteriori calculations (i.e. calculation after the individual retrievals). We show that this approach is largely equivalent to applying the spectra of the different sensors together in a single retrieval procedure, but with the substantial advantage of being applicable to data generated with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors.This research has largely benefit from funds of the Deutsche Forschungsgemeinschaft (provided for the two projects MOTIV and TEDDY with IDs/Geschäftszeichen 290612604/GZ:SCHN1126/2-1 and 416767181/GZ:SCHN1126/5-1, respectively) and from support by the European Space Agency in the context the "Sentinel-5p+Innovation (S5p+I) - Water Vapour Isotopologues (H2O-ISO)" activities. Furthermore, we acknowledge funds from the Ministerio de Economía y Competividad from Spain for the project INMENSE (CGL2016-80688-P)

An examination of the long-term CO records from MOPITT and IASI: comparison of retrieval methodology

International audienceCarbon monoxide (CO) is a key atmospheric compound that can be remotely sensed by satellite on the global scale. Fifteen years of continuous observations are now available from the MOPITT/Terra mission (2000 to present). Another fifteen and more years of observations will be provided by the IASI/MetOp instrument series (2007–2023>). In order to study long term variability and trends, a homogeneous record is required, which is not straightforward as the retrieved products are instrument and processing dependent. The present study aims at evaluating the consistency between the CO products derived from the MOPITT and IASI missions, both for total columns and vertical profiles, during a six year overlap period (2008–2013). The analysis is performed by first comparing the available 2013 versions of the retrieval algorithms, and second using a dedicated reprocessing of MOPITT CO profiles and columns based on the IASI a priori constraints. MOPITT v5T total columns are generally slightly higher over land (bias ranging from 0 to 13%) than IASI v20100815 data. When IASI and MOPITT data are retrieved with the same a priori constraints, correlation coefficients are slightly improved. Large discrepancies (total column bias over 15%) observed in the Northern Hemisphere during the winter months are reduced by a factor of 2 to 2.5. The detailed analysis of retrieved vertical profiles compared with collocated aircraft data from the MOZAIC-IAGOS network, illustrates the advantages and disadvantages of a constant vs. a variable a priori. On one hand, MOPITT agrees better with the aircraft profiles for observations with persisting high levels of CO throughout the year due to pollution or seasonal fire activity (because the climatology-based a priori is supposed to be closer to the real atmospheric state). On the other hand, IASI performs better when unexpected events leading to high levels of CO occur, due to the less constrained variance-covariance matrix

Synergetic use of IASI profile and TROPOMI total-column level 2 methane retrieval products

The thermal infrared nadir spectra of IASI (Infrared Atmospheric Sounding Interferometer) are successfully used for retrievals of different atmospheric trace gas profiles. However, these retrievals offer generally reduced information about the lowermost tropospheric layer due to the lack of thermal contrast close to the surface. Spectra of scattered solar radiation observed in the near-infrared and/or shortwave infrared, for instance by TROPOMI (TROPOspheric Monitoring Instrument), offer higher sensitivity near the ground and are used for the retrieval of total-column-averaged mixing ratios of a variety of atmospheric trace gases. Here we present a method for the synergetic use of IASI profile and TROPOMI total-column level 2 retrieval products. Our method uses the output of the individual retrievals and consists of linear algebra a posteriori calculations (i.e. calculation after the individual retrievals). We show that this approach has strong theoretical similarities to applying the spectra of the different sensors together in a single retrieval procedure but with the substantial advantage of being applicable to data generated with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors. We demonstrate the method exemplarily for atmospheric methane (CH$_4$). We perform a theoretical evaluation and show that the a posteriori combination method yields a total-column-averaged CH$_4$ product (XCH$_4$) that conserves the good sensitivity of the corresponding TROPOMI product while merging it with the high-quality upper troposphere–lower stratosphere (UTLS) CH$_4$ partial-column information of the corresponding IASI product. As a consequence, the combined product offers additional sensitivity for the tropospheric CH$_4$ partial column, which is not provided by the individual TROPOMI nor the individual IASI product. The theoretically predicted synergetic effect is verified by comparisons to CH$_4$ reference data obtained from collocated XCH$_4$ measurements at 14 globally distributed TCCON (Total Carbon Column Observing Network) stations, CH$_4$ profile measurements made by 36 individual AirCore soundings, and tropospheric CH$_4$ data derived from continuous ground-based in situ observations made at two nearby Global Atmospheric Watch (GAW) mountain stations. The comparisons clearly demonstrate that the combined product can reliably detect the actual variations of atmospheric XCH$_4$, CH$_4$ in the UTLS, and CH$_4$ in the troposphere. A similar good reliability for the latter is not achievable by the individual TROPOMI and IASI products

Retrieval of Water Vapour Profiles from GLORIA Nadir Observations

We present the first analysis of water vapour profiles derived from nadir measurements by the infrared imaging Fourier transform spectrometer GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere). The measurements were performed on 27 September 2017, during the WISE (Wave driven ISentropic Exchange) campaign aboard the HALO aircraft over the North Atlantic in an area between 37°–50°N and 20°–28°W. From each nadir recording of the 2-D imaging spectrometer, the spectral radiances of all non-cloudy pixels have been averaged after application of a newly developed cloud filter. From these mid-infrared nadir spectra, vertical profiles of H2O have been retrieved with a vertical resolution corresponding to five degrees of freedom below the aircraft. Uncertainties in radiometric calibration, temperature and spectroscopy have been identified as dominating error sources. Comparing retrievals resulting from two different a priori assumptions (constant exponential vs. ERA 5 reanalysis data) revealed parts of the flight where the observations clearly show inconsistencies with the ERA 5 water vapour fields. Further, a water vapour inversion at around 6 km altitude could be identified in the nadir retrievals and confirmed by a nearby radiosonde ascent. An intercomparison of multiple water vapour profiles from GLORIA in nadir and limb observational modes, IASI (Infrared Atmospheric Sounding Interferometer) satellite data from two different retrieval processors, and radiosonde measurements shows a broad consistency between the profiles. The comparison shows how fine vertical structures are represented by nadir sounders as well as the influence of a priori information on the retrievals

Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions derived by means of the theory of reproducing kernel Hilbert spaces. We also review their connections to other methods for classification and statistical dependence estimation, and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets, and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite images for Earth and climate monitoring