Towards a better assimilation of infrared satellite observations by coupling meteorological and chemical models

Le sondeur infrarouge hyperspectral IASI (Interféromètre Atmosphérique de Sondage Infrarouge) est l'instrument qui fournit le plus d'observations satellitaires au modèle de Prévision Numérique du Temps (PNT) ARPEGE (Action de Recherche Petite Échelle Grande Échelle) à Météo-France. Ce capteur a été développé conjointement entre le CNES (Centre National d'Études Spatiales) et EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) et est embarqué à bord des satellites défilants Metop-A, B et C. L'assimilation de ces observations requiert l'utilisation d'un Modèle de Transfert Radiatif (MTR) qui s'appelle RTTOV à Météo-France. Ce dernier utilise une connaissance a priori de l'état thermodynamique et chimique de l'atmosphère le plus probable pour simuler les observations IASI. À Météo-France, les champs d'ébauche thermodynamiques proviennent d'une prévision à courte échéance fournie par ARPEGE. Les ébauches de la composition chimique de l'atmosphère sont issues d'un unique profil vertical de référence pour chaque espèce chimique fourni par RTTOV. Cette approximation a un impact important sur la qualité des simulations et l'utilisation des observations satellitaires infrarouges pour la PNT. Les Modèles de Chimie Transport (MCT) sont capables de fournir des prévisions de la composition chimique de l'atmosphère. À Météo-France, ce MCT s'appelle MOCAGE. Ce travail de thèse propose une méthode permettant une meilleure assimilation des observations satellitaires infrarouges par un couplage des modèles météorologique et chimique. La première partie du travail consiste à évaluer la sensibilité des observations infrarouges à la chimie atmosphérique. Pour cela nous avons participé à la campagne de mesure APOGEE (Atmospheric Profiles Of GreenhousE gasEs) qui nous a permis de mesurer des profils in situ de CO2, CH4 et O3. Ces données ont été utilisées à la fois pour valider la qualité de nos simulations et comme données de vérification pour évaluer les prévisions de composition chimique atmosphérique issus de MCT. Nous avons par la suite encadré deux stagiaires de Master 1 pour réaliser une climatologie évolutive de CO2 afin d'améliorer l'utilisation des observations satellitaires infrarouges. De ces études, il ressort que la qualité des simulations dépend de la précision de l'ébauche chimique utilisée et que le constituant chimique ayant l'impact le plus important sur les simulations est l'ozone. La suite du travail de thèse s'est donc articulée autour de l'ozone. Une première étape a consisté à préparer l'assimilation de canaux IASI sensibles à l'ozone. [...]The Infrared Atmospheric Sounding Interferometer (IASI) is the instrument that provides the most satellite observations to the ARPEGE (Action de Recherche Petite Échelle Grande Scale) Numerical Weather Prediction (NWP) model at Météo-France. This sensor was developed jointly by CNES (Centre National d'Études Spatiales) and EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) and is carried on board the Metop-A, B and C polar orbiting satellites. The assimilation of these observations requires the use of a Radiative Transfer Model (RTM) called RTTOV at Météo-France. The latter uses an a priori knowledge of the most likely thermodynamic and chemical state of the atmosphere to simulate IASI observations. At Meteo-France, the background thermodynamic fields come from a short-term forecast provided by ARPEGE but the information on the chemical composition of the atmosphere comes from a single vertical reference profile for each chemical species, provided by RTTOV. However, this approximation has a significant impact on the quality of simulations and the use of infrared satellite observations for NWP. Chemistry Transport Models (CTM) are able to provide forecasts of the chemical composition of the atmosphere. At Météo-France, this CTM is called MOCAGE. This thesis therefore proposes a method toward a better assimilation of infrared satellite observations by coupling meteorological and chemical models. The first part of the work was to evaluate the sensitivity of infrared observations to some atmospheric compounds. To do so, we participated in the APOGEE (Atmospheric Profiles Of GreenhousE gases) measurement campaign, which enabled us to measure in situ profiles of CO2, CH4 and O3. These data were used both to validate the quality of our simulations and as verification data to assess atmospheric chemical composition derived from CTM. We then supervised two Master 1 trainees to carry out an evolving climatology of CO2 in order to improve the use of infrared satellite observations. From these studies, it appears that the quality of the simulations depends on the accuracy of the chemical information used and the chemical component with the greatest impact on the simulations is ozone. Thus, the rest of the thesis work was therefore focused on ozone. A first step was to prepare the assimilation of ozone-sensitive IASI channels. This work has shown both that the use of realistic ozone information from a CTM can better simulate ozone-sensitive observations and provide additional information, simultaneously improving temperature, humidity and ozone analyses [Coopmann et al., 2018]. [...

Vers une meilleure assimilation des observations satellitaires infrarouges par lecouplage des modèles météorologique et chimique

The Infrared Atmospheric Sounding Interferometer (IASI) is the instrument that provides the mostsatellite observations to the ARPEGE (Action de Recherche Petite Échelle Grande Scale) NumericalWeather Prediction (NWP) model at Météo-France. This sensor was developed jointly by CNES(Centre National d’Études Spatiales) and EUMETSAT (European Organisation for the Exploitationof Meteorological Satellites) and is carried on board the Metop-A, B and C polar orbiting satellites.The assimilation of these observations requires the use of a Radiative Transfer Model (RTM) calledRTTOV at Météo-France. The latter uses an a priori knowledge of the most likely thermodynamicand chemical state of the atmosphere to simulate IASI observations. At Meteo-France, thebackground thermodynamic fields come from a short-term forecast provided by ARPEGE but theinformation on the chemical composition of the atmosphere comes from a single vertical referenceprofile for each chemical species, provided by RTTOV. However, this approximation has asignificant impact on the quality of simulations and the use of infrared satellite observations forNWP. Chemistry Transport Models (CTM) are able to provide forecasts of the chemicalcomposition of the atmosphere. At Météo-France, this CTM is called MOCAGE. This thesistherefore proposes a method toward a better assimilation of infrared satellite observations bycoupling meteorological and chemical models. The first part of the work was to evaluate thesensitivity of infrared observations to some atmospheric compounds. To do so, we participated inthe APOGEE (Atmospheric Profiles Of GreenhousE gases) measurement campaign, which enabledus to measure in situ profiles of CO2, CH4 and O3. These data were used both to validate the qualityof our simulations and as verification data to assess atmospheric chemical composition derivedfrom CTM. We then supervised two Master 1 trainees to carry out an evolving climatology of CO2in order to improve the use of infrared satellite observations. From these studies, it appears that thequality of the simulations depends on the accuracy of the chemical information used and thechemical component with the greatest impact on the simulations is ozone. Thus, the rest of thethesis work was therefore focused on ozone. A first step was to prepare the assimilation of ozonesensitiveIASI channels. This work has shown both that the use of realistic ozone information froma CTM can better simulate ozone-sensitive observations and provide additional information,simultaneously improving temperature, humidity and ozone analyses [Coopmann et al., 2018]. Thena new channel selection of IASI ozone-sensitive highlighted 15 channels that also improvethermodynamic and chemical analyses. Finally, this channel selection was used in the fourdimensionaldata assimilation system (4D-Var) and a coupling was performed between theARPEGE and MOCAGE models for ozone fields. The results show that the use of ozone fromMOCAGE allows a better use of infrared satellite observations and has a positive impact on thequality of thermodynamic and ozone analyses but also on weather forecasts.Le sondeur infrarouge hyperspectral IASI (Interféromètre Atmosphérique de Sondage Infrarouge) est l’instrument qui fournit le plus d’observations satellitaires au modèle de Prévision Numériquedu Temps (PNT) ARPEGE (Action de Recherche Petite Échelle Grande Échelle) à Météo-France.Ce capteur a été développé conjointement entre le CNES (Centre National d’Études Spatiales) etEUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) et estembarqué à bord des satellites défilants MetOp-A, B et C. L’assimilation de ces observationsrequiert l’utilisation d’un Modèle de Transfert Radiatif (RTM) qui s’appelle RTTOV à Météo-France. Ce dernier utilise une connaissance a priori de l’état thermodynamique et chimique del’atmosphère le plus probable pour simuler les observations IASI. À Météo-France, les champs apriori thermodynamiques proviennent d'une prévision à courte échéance fournie par ARPEGE maisles informations sur la composition chimique de l’atmosphère sont issues de profils chimiques deréférence invariants dans le temps et l’espace fournit par RTTOV. Or, cette approximation a unimpact important sur la qualité des simulations et l’utilisation des observations satellitairesinfrarouges pour la PNT. Les Modèles de Chimie Transport (CTM) sont capables de fournir desprévisions de la composition chimique de l’atmosphère. À Météo-France, ce CTM s’appelleMOCAGE. Ce travail de thèse propose donc une méthode permettant une meilleure assimilation desobservations satellitaires infrarouges par un couplage entre les modèles météorologique etchimique. La première partie du travail a été d’évaluer la sensibilité des observations infrarouges àla chimie atmosphérique. Pour cela nous avons participé à la campagne de mesure APOGEE(Atmospheric Profiles Of GreenhousE gasEs) qui nous a permis de mesurer des profils in situ deCO2, CH4 et O3. Ces données ont été utilisées à la fois pour valider la qualité de nos simulations etcomme de données de vérification pour évaluer les a priori de composition chimique atmosphériqueissus de CTM. Nous avons par la suite encadré deux stagiaires de Master 1 pour réaliser uneclimatologie évolutive de CO2 afin d'améliorer l’utilisation des observations satellitairesinfrarouges. De ces études, il ressort que la qualité des simulations dépend de la précision del’information chimique utilisée et le constituant chimique ayant l’impact le plus important sur lessimulations est l’ozone. Ainsi, la suite du travail de thèse s'est donc articulée autour de l’ozone. Unepremière étape a consisté à préparer l’assimilation de canaux IASI sensibles à l’ozone. Ce travail amontré à la fois que l’utilisation d’une information réaliste d’ozone issue d’un CTM permet demieux simuler les observations sensibles à l’ozone et d’apporter de l’information supplémentaire,améliorant simultanément les analyses de température, d’humidité et d’ozone [Coopmann et al.,2018]. Puis une nouvelle sélection de canaux IASI sensibles à l’ozone a mis évidence 15 canauxpermettant également d’améliorer les analyses thermodynamiques et chimiques. Enfin cettesélection de canaux a été utilisée dans le système d’assimilation de données quadri-dimensionnelle(4D-Var) et un couplage a été réalisé entre les modèles ARPEGE et MOCAGE pour les champsd’ozone. Les résultats montrent que l’utilisation de l’ozone de MOCAGE permet une meilleureutilisation des observations satellitaires infrarouges et a un impact positif sur la qualité des analysesthermodynamiques et d’ozone mais également sur les prévisions météorologiques

Analysis of MTG‐IRS observations and general channel selection for Numerical Weather Prediction models

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Assimilation of IASI observations to enhance the coupling between Numerical Weather Prediction and Chemistry Transport Models

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4D-Var assimilation of IASI ozone-sensitive radiances in operational global model ARPEGE

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Use of variable ozone in a radiative transfer model for the global Météo‐France 4D‐Var system.

International audienceNowadays, the assimilation of satellite observations, particularly radiances from infrared sounders, into numerical weather prediction (NWP) models plays a dominant role in improving weather forecasts. One of the keys to make optimal use of radiances is to simulate them with a radiative transfer model (RTM). At Météo-France, the RTTOV RTM is used for NWP models. Currently, simulations are carried out taking into account single chemical profiles. However, neglecting the spatial and temporal variability of these gases can affect the accuracy of the simulations and thus the quality of the subsequent analyses and forecasts

IMPROVING THERMODYNAMIC RETRIEVALS FROM IASI DATA USING REALISTIC OZONE AND OZONE-SENSITIVE CHANNELS

International audienceHyperspectral infrared sensors onboard polar-orbiting satellites provides 70% of measures used in the Numerical Weather Prediction (NWP) global model ARPEGE (Action de Recherche Petite Échelle Grande Échelle) of Météo-France, where IASI (Infrared Atmospheric Sounding Interferometer) represents 46%. The infrared passive sounding is sensitive to surface parameters and numerous atmospheric constituents. The atmospheric temperature information is retrieved from the channels which are sensitive to gases the distribution of which is known. Most of algorithms for infrared satellites measures use carbon-dioxyde (CO 2 ) sensitive channels to retrieve the temperature information. Part of the infrared spectrum are also sensitive to ozone (O 3 ) but are not currently used in the NWP models of Météo-France. In the current version of the assimilation in the ARPEGE model, the gase concentrations used for the radiance simulations are constant in space and in time. A study conducted in 2015 showed that using realistic ozone information from the Chemistry Transport Model (CTM) MOCAGE (Modèle de Chimie Atmosphérique A Grande Echelle) of Météo-France as input of the radiative transfer model improved the temperature retrievals from the infrared satellite measures. This presentation will describe the channel selection of IASI ozone sensitive channels to improve the retrievals of temperature and humidity profiles in the NWP model. There are several methods to select a set of channels to improve the atmospheric profiles such as the Jacobian and iterative methods (Rabier and Fourrié 2001). In our case, we have used DFS and sum of relative error reduction for temperature and humidity. Several settings of observation error covariance matrix have been evaluated such as the iterative Desroziers methods. We have also used different variances from operational NWP and calculated from the simulations in order to set-up the observation error covariance matrix. Results are very promising with sum of relative error reduction method, especially using the Desroziers technique (Desroziers, 2005)

Update of Infrared Atmospheric Sounding Interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP)

International audienceThe Infrared Atmospheric Sounding Interferome-ter (IASI) is an essential instrument for numerical weather prediction (NWP). It measures radiances at the top of the atmosphere using 8461 channels. The huge amount of observations provided by IASI has led the community to develop techniques to reduce observations while conserving as much information as possible. Thus, a selection of the 300 most informative channels was made for NWP based on the concept of information theory. One of the main limitations of this method was to neglect the covariances between the observation errors of the different channels. However, many centres have shown a significant benefit for weather forecasting to use them. Currently, the observation-error covariances are only estimated on the current IASI channel selection, but no studies to make a new selection of IASI channels taking into account the observation-error covariances have yet been carried out. The objective of this paper was therefore to perform a new selection of IASI channels by taking into account the observation-error covariances. The results show that with an equivalent number of channels, accounting for the observation-error covariances, a new selection of IASI channels can reduce the analysis error on average in temperature by 3 %, humidity by 1.8 % and ozone by 0.9 % compared to the current selection. Finally, we go one step further by proposing a robust new selection of 400 IASI channels to further reduce the analysis error for NWP

Preparing the assimilation of the future MTG‐IRS sounder into the mesoscale NWP AROME model

International audienceThe IRS (InfraRed Sounder) instrument is an infrared Fourier transform spectrometer that will be on board the Meteosat Third Generation series of the future EUMETSAT geostationary satellites. It will measure the radiance emitted by the Earth at the top of the atmosphere using 1960 channels. IRS will provide high spatial and temporal frequency 4D information on atmospheric temperature and humidity, winds, clouds, surfaces, as well as on the chemical composition of the atmosphere. The assimilation of these new observations represents a great challenge and opportunity for the improvement of Numerical Weather Prediction (NWP) forecast skill, especially for mesoscale models such as AROME at Météo‐France (Brousseau et al. 2016). The objectives of this study are to prepare for the assimilation of IRS in this system and to evaluate its impact on the forecasts when added to the currently assimilated observations. By using an Observing System Simulation Experiment (OSSE) constructed for a mesoscale NWP model. This OSSE framework makes use of synthetic observations of both IRS and the currently assimilated observing systems in AROME, constructed from a known and realistic state of the atmosphere. The latter, called the Nature Run, is derived from a long and uninterrupted forecast of the mesoscale model. These observations were assimilated and evaluated using a 1 h update cycle 3D‐Var data assimilation system over two‐month periods, one in the summer and one in the winter. This study demonstrates the benefits that can be expected from the assimilation of IRS observations into AROME NWP system. The assimilation of only 75 channels over oceans increases the total amount of observations used in the AROME 3D‐Var by about 50 %. The IRS impact in terms of forecast scores was evaluated and compared for the summer and winter periods. The main findings are that (i) over both periods the assimilation of these observations lead to statistically improved forecasts over the whole atmospheric column, (ii) for the summer season experiment, the forecast ranges up to +48h are improved, (iii) for the winter season experiment, the impact on the forecasts is globally positive but is smaller compared to the summer period and extends only to 24 h. Based on these results, it is foreseen that the addition of future IRS observations in the AROME NWP systems will significantly improve mesoscale weather forecasts

Ongoing developments on satellite radiance assimilation at Météo-France

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