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4. Data quality of Aeolus wind measurements

Author

Krisch, Isabell, Reitebuch, Oliver, von Bismarck, Jonas, Dabas, Alain, Fischer, Peggy, Huber, Dorit, de Kloe, Jos, Rennie, Michael, Lemmerz, Christian, Lux, Oliver, Marksteiner, Uwe, Masoumzadeh, Nafiseh, Weiler, Fabian, Witschas, Benjamin, Bracci, Fabio, Meringer, Markus, Schmidt, Karsten, Geiss, Alexander, Nikolaus, Ines, Vaughan, Michael, Fabre, Frederic, Flament, Thomas, Trapon, Dimitri, Lacour, Adrien, Abdalla, Saleh, Isaksen, Lars, Donovan, Dave, Marseille, Gert-Jan, Stoffelen, Ad, Zandelhoff, Gerd-Jan, Wang, Ping, Perron, Gaetan, Jupin-Ganglois, Sebastian, Veneziani, Marcella, Pijnacker-Hordijk, Bas, Bucci, Simone, Gostinicchi, Giacomo, Kanitz, Thomas, Straume, Anne-Grete, Ehlers, Frithjof, Wernham, Denny, Bley, Sebastian, Aprile, Stefano, De Laurentis, Marta, Parinello, Tommaso, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), DLR Institut für Physik der Atmosphäre (IPA), Deutsches Zentrum für Luft- und Raumfahrt [Oberpfaffenhofen-Wessling] (DLR), Agence Spatiale Européenne (ESA), European Space Agency (ESA), Royal Netherlands Meteorological Institute (KNMI), European Centre for Medium-Range Weather Forecasts (ECMWF), and ESA/AEOLUS

Subjects
Earth Explorer mission, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Doppler wind lidar, 13. Climate action, [SDE.IE]Environmental Sciences/Environmental Engineering, sattelite, wind, 7. Clean energy, Aeolus, lidar
Abstract

The European Space Agency (ESA)’s Earth Explorer Aeolus was launched in August 2018 carrying the world’s first spaceborne wind lidar, the Atmospheric Laser Doppler Instrument (ALADIN). ALADIN uses a high spectral resolution Doppler wind lidar operating at 355nm to determine profiles of line-of-sight wind components in near-real-time (NRT). ALADIN samples the atmosphere from 30km altitude down to the Earth’s surface or to the level where the lidar signal is attenuated by optically thick clouds.The global wind profiles provided by ALADIN help to improve weather forecasting and the understanding of atmospheric dynamics as they fill observational gaps in vertically resolved wind profiles mainly in the tropics, southern hemisphere, and over the northern hemisphere oceans. Since 2020, multiple national and international weather centres (e.g. ECMWF, DWD, Météo France, MetOffice) assimilate Aeolus observations in their operational forecasting. Additionally, the scientific exploitation of the Aeolus dataset has started.A main prerequisite for beneficial impact and scientific exploitation is data of sufficient quality. Such high data quality has been achieved through close collaboration of all involved parties within the Aeolus Data Innovation and Science Cluster (DISC), which was established after launch to study and improve the data quality of Aeolus products. The tasks of the Aeolus DISC include the instrument and platform monitoring, calibration, characterization, retrieval algorithm refinement, processor evolution, quality monitoring, product validation, and impact assessment for NWP.The achievements of the Aeolus DISC for the NRT data quality and the one currently available reprocessed dataset will be presented. The data quality of the Aeolus wind measurements will be described and an outlook on planned improvements of the dataset and processors will be provided.

Published
2021
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6. The Aeolus Data Innovation and Science Cluster

Author

Krisch, Isabell, primary, Reitebuch, Oliver, additional, Von Bismarck, Jonas, additional, Parrinello, Tommaso, additional, Rennie, Michael, additional, Weiler, Fabian, additional, Huber, Dorit, additional, De Kloe, Jos, additional, Dabas, Alain, additional, Straume-Lindner, Anne Grete, additional, Abdalla, Saleh, additional, Aprile, Stefano, additional, Bley, Sebastian, additional, Bracci, Fabio, additional, Bucci, Simone, additional, Cardaci, Massimo, additional, Damman, Werner, additional, Donovan, Dave, additional, Ehlers, Frithjof, additional, Fabre, Frederic, additional, Fischer, Peggy, additional, Flament, Thomas, additional, Gostinicchi, Giacomo, additional, Isaksen, Lars, additional, Jupin-Langlois, Sebastian, additional, Kanitz, Thomas, additional, Lacour, Adrien, additional, De Laurentis, Marta, additional, Lemmerz, Christian, additional, Lux, Oliver, additional, Marksteiner, Uwe, additional, Marseille, Gert-Jan, additional, Masoumzadeh, Nafiseh, additional, Meringer, Markus, additional, Niemeijer, Sander, additional, Nikolaus, Ines, additional, Perron, Gaetan, additional, Pijnacker-Hordijk, Bas, additional, Reissig, Katja, additional, Savli, Matic, additional, Schmidt, Karsten, additional, Stoffelen, Ad, additional, Trapon, Dimitri, additional, Vaughan, Michael, additional, Veneziani, Marcella, additional, De Vincenti, Cristiano, additional, and Witschas, Benjamin, additional

Published
2021
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7. Assessment of the Aeolus performance and bias correction - results from the Aeolus DISC

Author

Reitebuch, Oliver, Krisch, Isabell, Lemmerz, Christian, Lux, Oliver, Marksteiner, Uwe, Masoumzadeh, Nafiseh, Weiler, Fabian, Witschas, Benjamin, Bracci, Fabio, Meringer, Markus, Schmidt, Karsten, Huber, Dorit, Nikolaus, Ines, Fabre, Frederic, Vaughan, Michael, Reisig, Katja, Dabas, Alain, Flament, Thomas, Lacour, Adrien, Mahfouf, Jean-Francois, Trapon, Dimitri, Savli, Matic, Abdalla, Saleh, Isaksen, Lars, Rennie, Michael, Donovan, Dave, de Kloe, Jos, Marseille, Gert-Jan, Stoffelen, Ad, Perron, Gaetan, Jupin-Ganglois, Sebastian, Smeets, Joost, Veneziani, Marcella, Bucci, Simone, Gostinicchi, Giacomo, Ehlers, Frithjof, Kanitz, Thomas, Straume, Anne-Grete, Wernham, Denny, von Bismarck, Jonas, Bley, Sebastian, Fischer, Peggy, De Laurentis, Marta, and Parinello, Tommaso

Subjects
Earth Explorer mission, Lidar, Doppler wind lidar, Atmosphärenprozessoren, Aeolus
Abstract

Already within the first weeks after the launch of ESA's Earth Explorer mission Aeolus on 22 August 2018, the spaceborne wind lidar ALADIN (Atmospheric LAser Doppler INstrument) provided atmospheric backscatter measurements on 5 September and wind profiles on 12 September 2018. This swift availability of observations from ALADIN after launch is considered as a great success for ESA, space industry and algorithm and processor developer teams. These teams from scientific institutes, numerical weather prediction (NWP) centres, companies and ESA continuously improved and tested the retrieval algorithms and processors using sophisticated end-to-end simulation tools and experience gained with the airborne demonstrator for Aeolus for more than 15 years before launch. This cooperation from the pre-launch phase of Aeolus was extended within a new framework for exploitation activities of Earth Explorer missions named Data Innovation and Science Cluster (DISC) starting in January 2019. The Aeolus DISC activities range from instrument monitoring including calibration to algorithm refinement resulting in updates of the complete processor chain for all product levels every 6 months. DISC teams perform continuous monitoring of the product quality and provide regular reports in supports of external validation teams and ESA. Finally, wind product monitoring and impact experiments with NWP models are building an essential activity within the Aeolus DISC in order to achieve the objective of the Aeolus mission. In order to cover the broad range of activities, a multi-disciplinary team of experts, institutes and companies was established for the Aeolus DISC coordinated by DLR with ECMWF, KNMI, CNRS/Météo-France, DoRIT, ABB, S&T and Serco. During the presentation the Aeolus instrument performance for wind products, the discovered causes of the systematic errors and their correction will be discussed. Main achievements in this area are related to the characterization and correction of enhanced dark signal levels for single "hot" pixels in June 2019, the identification of the harmonic error contribution caused by the varying telescope primary mirror temperature variation in September- October 2019, the error in the on-board computation of the satellite induced Doppler frequency shift, and finally the observed temporal drift of a constant bias caused by drifts in the internal reference path. An outlook to the implementation of these corrections for real-time and reprocessed data products will be given.

Published
2020

9. Aeolus aerosol and cloud product

Author

Flament, Thomas, primary, Dabas, Alain, additional, Trapon, Dimitri, additional, Lacour, Adrien, additional, Ehlers, Frithjof, additional, Baars, Holger, additional, and Huber, Dorit, additional

Published
2021
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10. Optimization of Aeolus Optical Properties Products by Maximum-Likelihood Estimation.

Author

Ehlers, Frithjof, Flament, Thomas, Dabas, Alain, Trapon, Dimitri, Lacour, Adrien, Baars, Holger, and Grete Straume-Lindner, Anne

Subjects
OPTICAL properties, DOPPLER lidar, MAXIMUM likelihood statistics, ALGORITHMS, LIDAR
Abstract

The European Space Agency (ESA) Earth Explorer Mission, Aeolus, was launched in August 2018 and embarks the first Doppler Wind Lidar in space. Its primary payload, the Aeolus LAser Doppler INstrument (Aladin) is a Ultra Violet (UV) High Spectral Resolution Lidar (HSRL) measuring atmospheric backscatter from air molecules and particles in two separate channels. The primary mission product is globally distributed line-of-sight wind profile observations in the troposphere and lower stratosphere. Atmospheric optical properties are provided as a spin-off product. Being and HSRL, Aeolus is able to independently measure the particle extinction coefficients, co-polarized particle backscatter coefficients and the co-polarized lidar ratio. This way, the retrieval is independent of a-priori information. The optical properties are retrieved using the Standard Correct Algorithm (SCA), which is an algebraic inversion scheme to a (partly) ill-posed problem and therefore sensitive to measurement noise. In this work, we rephrase the SCA into a physically constrained Maximum Likelihood Estimation (MLE) problem and demonstrate predominantly positive impact and considerable noise suppression capabilities. These improvements originate from the use of all available information within the SCA in conjunction with the expected physical bounds concerning the expected range of the lidar ratio. The new MLE algorithm is equally evaluated against the SCA on end-to-end simulations of two homogeneous scenes and for real Aelous data collocated with measurements by a ground-based lidar and the CALIPSO satellite to consolidate and to illustrate the improvements. The largest improvements were seen in the retrieval of the extinction coefficients and lidar ratio ranging up to one order of magnitude or more in some cases due to an effective noise dampening. [ABSTRACT FROM AUTHOR]

Published
2021
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11. Disagreement among global cloud distributions from CALIOP, passive satellite sensors and general circulation models

Author

Noel, Vincent, Chepfer, Helene, Chiriaco, Marjolaine, Winker, David, Okamoto, Hajime, Hagihara, Yuichiro, Cesana, Gregory, Lacour, Adrien, Laboratoire d'aérologie - LA ( LA ), Université Paul Sabatier - Toulouse 3 ( UPS ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Observatoire Midi-Pyrénées ( OMP ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Météorologie Dynamique (UMR 8539) ( LMD ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -École polytechnique ( X ) -École des Ponts ParisTech ( ENPC ) -Centre National de la Recherche Scientifique ( CNRS ) -Département des Géosciences - ENS Paris, École normale supérieure - Paris ( ENS Paris ) -École normale supérieure - Paris ( ENS Paris ), Laboratoire Atmosphères, Milieux, Observations Spatiales ( LATMOS ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), NASA Langley Research Center [Hampton] ( LaRC ), Research Institute for Applied Mechanics, Kyushu University [Fukuoka], Laboratoire d'aérologie (LAERO), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), SPACE - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), NASA Langley Research Center [Hampton] (LaRC), Research Institute for Applied Mechanics [Fukuoka] (RIAM), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Kyushu University

Subjects
bepress|Physical Sciences and Mathematics, [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], EarthArXiv|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, CALIPSO, FOS: Physical sciences, climatologies, 02 engineering and technology, Oceanography and Atmospheric Sciences and Meteorology, clouds, 01 natural sciences, EarthArXiv|Physical Sciences and Mathematics, Physics - Atmospheric and Oceanic Physics, remote sensing, [ PHYS.PHYS.PHYS-AO-PH ] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], 13. Climate action, GEWEX, bepress|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology, Atmospheric and Oceanic Physics (physics.ao-ph), Physical Sciences and Mathematics, lidar, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

Cloud detection is the first step of any complex satellite-based cloud retrieval. No instrument detects all clouds, and analyses that use a given satellite climatology can only discuss a specific subset of clouds. We attempt to clarify which subsets of clouds are detected in a robust way by passive sensors, and which require active sensors. To do so, we identify where retrievals of Cloud Amounts (CAs), based on numerous sensors and algorithms, differ the most. We investigate large uncertainties, and confront retrievals from the CALIOP lidar, which detects semitransparent clouds and directly measures their vertical distribution, whatever the surface below. We document the cloud vertical distribution, opacity and seasonal variability where CAs from passive sensors disagree most. CALIOP CAs are larger than the passive average by +0.05 (AM) and +0.07 (PM). Over land, the +0.1 average difference rises to +0.2 over the African desert, Antarctica and Greenland, where large passive disagreements are traced to unfavorable surface conditions. Over oceans, CALIOP retrievals are closer to the average of passive retrievals except over the ITCZ (+0.1). Passive CAs disagree more in tropical areas associated with large-scale subsidence, where CALIOP observes a specific multi-layer cloud population: optically thin, high-level clouds and opaque (z>7km), shallow boundary layer clouds (z

Published
2018

13. Les nuages du Groenland observés par CALIPSO

Author

Lacour, Adrien, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École des Ponts ParisTech (ENPC)-École polytechnique (X)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Université Pierre et Marie Curie - Paris VI, Hélène Chepfer, Vincent Noël, STAR, ABES, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Station sol de Summit, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, Clouds, Greenland, Calotte polaire, Modèle de climat, Summit ground station, Nuages, Effet radiatif, Groenland
Abstract

Over 80% of Greenland is covered by ice. Melting of this ice contributes to the sea level rise. By modulating the radiation reaching the surface, clouds can accelerate or slow down the melting. Through this thesis, we use CALIPSO satellite measurements (GOCCP product) to document clouds over Greenland, including their vertical structure, and understand their role in surface melting.We compare these observations with radar and lidar measurement taken from the Summit ground station in the middle of Greenland. The comparison shows that GOCCP does not include optically thin ice clouds (τ < 0.3). Extending this analysis over all Greenland shows that cloudiness follows different cloud annual cycles in North and South regions, and that Summit is one of the cloudiest regions of the Greenland especially for the liquid cloud cover.To understand the atmospheric conditions favorable to cloud formation, we follow two weather regime classification approaches. We do not find a clear relationship between cloud variability and atmospheric circulation. These results show the complexity of the interactions between clouds and synoptic circulation and highlight the need to accumulate more data over long time periods.Finally, we evaluate cloud representation over Greenland in simulated lidar profiles over output from CMIP5 climate models. We identify several biases that lead to models being unable to simulate surface melting. Models underestimate the surface temperature and the cloud cover. Also when clouds are simulated they are either too opaque or too thin to affect surface melting., Plus de 80% du Groenland est recouvert de glace. Sa fonte contribue à l’augmentation du niveau des océans. Cette fonte peut être accélérée ou ralentie par les nuages qui modulent le rayonnement qui atteint la surface. Dans cette thèse, nous avons utilisé les mesures du satellite CALIPSO (produit GOCCP) pour documenter les nuages au-dessus du Groenland et éclaircir leur rôle sur la fonte de surface.Comparer ces observations avec des mesures radar et lidar réalisées à la station sol de Summit, au centre du Groenland, a montré que dans GOCCP les nuages optiquement très fins (τ < 0.3) ne sont pas détectés. Nous avons ensuite étendu l’analyse sur l’ensemble du Groenland et mis en évidence que la région nord est moins recouverte de nuages que la région sud en hiver et qu’en été, Summit, est l’une des régions les plus nuageuses en nuages liquides notamment.Pour comprendre cette particularité et les conditions favorables à la formation de nuages, nous avons utilisé des classifications en régime de temps. Cependant cette étude n’a pas mis à jour de liens entre la variabilité des nuages et la circulation atmosphérique ce qui montre la complexité de ces interactions et la nécessité d’accumuler plus d’observations sur des périodes de temps longues.Enfin nous avons évalué la représentation des nuages dans des observations lidar synthétiques, simulées à partir des sorties de modèles de climat CMIP5. Plusieurs biais qui empêchent les modèles de reproduire l’influence des nuages sur la fonte ont été identifiés. Les modèles sous estiment les températures de surface et les couvertures nuageuses. Les nuages simulés sont soit trop opaques soit trop fins pour accélérer la fonte.

Published
2016

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