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In the coming years, EUMETSAT's Meteosat Third Generation – Sounding (MTG-S) satellites will be launched with an instrument including valuable features on board. The MTG Infrared Sounder (MTG-IRS) will represent a major innovation for the monitoring of the chemical state of the atmosphere, since, at present, observations of these parameters mainly come from in situ measurements (geographically uneven) and from instruments on board polar-orbiting satellites (highly dependent on the scanning line of the satellite itself, which is limited, over a specific geographical area, to very few times per day). MTG-IRS will present a great deal of potential in the area of detecting different atmospheric species and will have the advantage of being based on a geostationary platform and acquiring data with a high temporal frequency (every 30 min over Europe), which makes it easier to track the transport of the species of interest. The present work aims to evaluate the potential impact, over a regional domain over Europe, of the assimilation of MTG-IRS radiances within a chemical transport model (CTM), Modèle de Chimie Atmosphérique de Grande Echelle (MOCAGE), operated by Météo-France. Since MTG-IRS is not yet in orbit, observations have been simulated using the observing system simulation experiment (OSSE) approach. Of the species to which MTG-IRS will be sensitive, the one treated in this study was ozone. The results obtained indicate that the assimilation of synthetic radiances of MTG-IRS always has a positive impact on the ozone analysis from MOCAGE. The relative average difference compared to the nature run (NR) in the ozone total columns improves from -30 % (no assimilation) to almost zero when MTG-IRS observations are available over the domain. Also remarkable is the reduction in the standard deviation of the difference with respect to the NR, which, in the area where MTG-IRS radiances are assimilated, reaches its lowest values (∼ 1.8 DU). When considering tropospheric columns, the improvement is also significant, from 15 %–20 % (no assimilation) down to 3 %. The error in the differences compared to the NR is lower than for total columns (minima ∼ 0.3 DU), due also to the lower concentrations of the tropospheric ozone field. Overall, the impact of assimilation is considerable over the whole vertical column: vertical variations are noticeably improved compared to what is obtained when no assimilation is performed (up to 25 % better). [ABSTRACT FROM AUTHOR]

Isoprene, a key biogenic volatile organic compound, plays a pivotal role in atmospheric chemistry. Due to its high reactivity, this compound contributes significantly to the production of tropospheric ozone in polluted areas and to the formation of secondary organic aerosols. The assessment of biogenic emissions is of great importance for regional and global air quality evaluation. In this study, we implemented the biogenic emission model MEGANv2.1 (Model of Emissions of Gases and Aerosols from Nature, version 2.1) in the surface model SURFEXv8.1 (SURface EXternalisée in French, version 8.1). This coupling aims to improve the estimation of biogenic emissions using the detailed vegetation-type-dependent treatment included in the SURFEX vegetation ISBA (Interaction between Soil Biosphere and Atmosphere) scheme. This scheme provides vegetation-dependent parameters such as leaf area index and soil moisture to MEGAN. This approach enables a more accurate estimation of biogenic fluxes compared to the stand-alone MEGAN model, which relies on average input values for all vegetation types. The present study focuses on the assessment of the SURFEX–MEGAN model isoprene emissions. An evaluation of the coupled SURFEX–MEGAN model results was carried out by conducting a global isoprene emission simulation in 2019 and by comparing the simulation results with other MEGAN-based isoprene inventories. The coupled model estimates a total global isoprene emission of 443 Tg in 2019. The estimated isoprene is within the range of results obtained with other MEGAN-based isoprene inventories, ranging from 311 to 637 Tg. The spatial distribution of SURFEX–MEGAN isoprene is consistent with other studies, with some differences located in low-isoprene-emission regions. Several sensitivity tests were conducted to quantify the impact of different model inputs and configurations on isoprene emissions. Using different meteorological forcings resulted in a ± 5 % change in isoprene emissions using MERRA (Modern-Era Retrospective analysis for Research and Applications) and IFS (Integrated Forecasting System) compared with ERA5. The impact of using different emission factor data was also investigated. The use of PFT (plant functional type) spatial coverage and PFT-dependent emission potential data resulted in a 12 % reduction compared to using the isoprene emission potential gridded map. A significant reduction of around 38 % in global isoprene emissions was observed in the third sensitivity analysis, which applied a parameterization of soil moisture deficit, particularly in certain regions of Australia, Africa, and South America. The significance of coupling the SURFEX and MEGAN models lies particularly in the ability of the coupled model to be forced with meteorological data from any period. This means, for instance, that this system can be used to predict biogenic emissions in the future. This aspect of our work is significant given the changes that biogenic organic compounds are expected to undergo as a result of changes in their climatic factors. [ABSTRACT FROM AUTHOR]

In the coming years, EUMETSAT's Meteosat Third Generation – S (MTG-S) satellites will be launched with an instrument of valuable features on board. The MTG – Infrared Sounder (IRS) will represent a major innovation for the monitoring of the chemical state of the atmosphere, since, at present, observations of these parameters mainly come from in situ measurements (geographically uneven) and from instruments on board of polar-orbiting satellites (highly dependent on the scanning line of the satellite itself, which is limited, over a specific geographical area, to very few times per day). MTG-IRS will present many potentialities in the area of detecting different atmospheric species and will have the advantage of being based on a geostationary platform and to acquire data with a high temporal frequency (every 30 minutes over Europe), which makes easier to track the transport of the species of interest. The present work aims to evaluate the potential impact, over a regional domain over Europe, of the assimilation of IRS radiances within a chemical transport model (CTM) Modεave;le de Chimie Atmosphérique de Grande Echelle (MOCAGE), operational in Météo-France. Since IRS is not yet in orbit, observations have been simulated using the Observing System Simulation Experiment (OSSE) approach. Of the species to which IRS will be sensitive, the one treated along this study was the ozone. The results obtained indicate that the assimilation of synthetic radiances of IRS always has a positive impact on the ozone analysis from the model MOCAGE. The relative average difference compared to the NR in the ozone total columns improves from -30 % (no assimilation) to almost zero when IRS observations are available over the domain. When considering tropospheric columns the improvement is also significant, from 15–20 % (no assimilation) down to 3 %. [ABSTRACT FROM AUTHOR]

Isoprene, a key biogenic volatile organic compound, plays a pivotal role in atmospheric chemistry. Due to its high reactivity, this compound contributes significantly to the production of tropospheric ozone in polluted areas, and to the formation of secondary organic aerosols. The assessment of biogenic emissions is of great importance for regional and global air quality evaluation. In this study, we have implemented the biogenic emissions model MEGANv2.1 (Model of Emission of Gases and Aerosols from Nature, ver- sion 2.1) in the surface model SURFEXv8.1 (SURface EXternalisée in french, version 8.1). This coupling aims to improve the estimation of biogenic emissions using the detailed vegetation type-dependent treatment included in the SURFEX vegetation ISBA scheme. This scheme provides to MEGAN vegetation-dependent parameters allowing a more precise estimation of biogenic fluxes (e.g., leaf area index, soil moisture, wilting point data). The present study focuses on the assessment of the SURFEX-MEGAN model isoprene emissions. The evaluation of the cou- pled SURFEX-MEGAN model results was carried out by conducting a global isoprene emissions simulation in 2019 and comparing the simulation results with other MEGAN-based isoprene inventories. The coupled model estimates a total global isoprene emission of 442Tg in 2019. The estimated isoprene is within the range of results obtained with other MEGAN-based isoprene inventories, ranging from 311Tg to 637Tg. The spatial distribution of SURFEX-MEGAN isoprene is consistent with other studies, with some differences located in low isoprene emission regions. Several sensitivity tests were conducted to quantify the impact of different model inputs and configurations on isoprene emissions. Using different meteorological forcings resulted in a +/-5% change in isoprene emission using MERRA and IFS, respectively, compared with ERA5. The impact of using different emission factors data was also investigated. The use of PFT spatial coverage and PFT-dependent emission potential data resulted in a 14% reduction compared to using the isoprene emission potential gridded map. A significant reduction of around 38% in global isoprene emissions, was observed in the third sensitivity analysis, which applied a parameterization of soil moisture deficit, particularly in certain regions of Australia, Africa and South America. The significance of coupling the SURFEX and MEGAN models lies particularly in the ability of the coupled model to be able to be forced with meteorological data from any time period. This means, for instance, that this system can be used to predict biogenic emissions in the future. This aspect of this work is significant given the changes that biogenic organic compounds are expected to undergo as a result of changes in their climatic factors. [ABSTRACT FROM AUTHOR]

This contribution explores a new approach to forecast multivariate covariances for atmospheric chemistry through the use of the parametric Kalman filter (PKF). In the PKF formalism, the error covariance matrix is modelized by a covariance model relying on parameters, for which the dynamics is then computed. The PKF has been formulated in univariate cases, and a multivariate extension for chemical transport models is explored here. To do so, a simplified two-species chemical transport model over a 1D domain is introduced, based on the nonlinear Lotka-Volterra equations, which allows to propose a multivariate pseudo covariance model. Then, the multivariate PKF dynamics is formulated and its results are compared with a large ensemble Kalman filter (EnKF) in several numerical experiments. In these experiments, the PKF accurately reproduces the EnKF. Eventually, the PKF is formulated for a more complex chemical model composed of six chemical species (Generic Reaction Set). Again, the PKF succeeds at reproducing the multivariate covariances diagnosed on the large ensemble. [ABSTRACT FROM AUTHOR]

In atmospheric chemistry retrievals and data assimilation systems, observation errors associated with satellite radiances are chosen empirically and generally treated as uncorrelated. In this work, we estimate inter-channel error covariances for the Infrared Atmospheric Sounding Interferometer (IASI) and evaluate their impact on ozone assimilation with the chemical transport model MOCAGE (MOdèle de Chime Atmospheric à Grand Echelle). The method used to calculate observation errors is a diagnostic based on the observation and analysis residual statistics already adopted in numerical weather prediction centers. We used a subset of 280 channels covering the spectral range between 980 and 1100 cm-1 to estimate the observation error covariance matrix. We computed hourly 3D-Var analyses and compared the resulting O3 fields against ozonesondes and the measurements provided by the Microwave Limb Sounder (MLS). The results show significant differences between using the estimated error covariance matrix with respect to the empirical diagonal matrix employed in previous studies. The validation of the analyses against independent data reports a significant improvement especially in the tropical stratosphere. The computational cost has also been reduced when the estimated covariance is employed in the assimilation system. [ABSTRACT FROM AUTHOR]

A comprehensive analysis of the water budget over the Dome C (Concordia, Antarctica) station has been performed during the austral summer 2018-2019 as part of the Year of Polar Prediction (YOPP) international campaign. Thin (~ 100-m) supercooled liquid water (SLW) clouds have been detected and analysed using remotely sensed observations at the station (tropospheric depolarization LIDAR, microwave radiometer HAMSTRAD, net surface radiation from Baseline Surface Radiation Network, BSRN), radiosondes and using satellite observations (CALIOP/CALIPSO) combined with a specific configuration of the Numerical Weather Prediction model: ARPEGE-SH. Two case studies are used to illustrate this phenomenon. On 24 December 2018, the atmospheric planetary boundary layer (PBL) evolved following a typical diurnal variation, that is to say with a warm and dry mixing layer at local noon thicker than the cold and dry stable layer at local midnight. Our study showed that the SLW clouds were observed at Dome C within the entrainment and the capping inversion zones at the top of the PBL. ARPEGE-SH was not able to correctly estimate the ratio between liquid and solid water inside the clouds. The SLW content was always strongly underestimated in the studied cases. The lack of simulated SLW in the model impacted the net surface radiation that was 20-30 W m-2 higher in the BSRN observations than in the ARPEGE-SH calculations, mainly attributable to longwave downward surface radiation from BSRN being 50 W m-2 greater than that of ARPEGE-SH. On 20 December 2018, a warm and wet episode impacted the PBL with no clear diurnal cycle of the PBL top height. SLW cloud appearance coincided with the warm and wet event within the entrainment and capping inversion zones. The amount of liquid water measured by HAMSTRAD was ~ 20 times greater in this perturbed PBL than in the typical PBL. Since ARPEGE-SH was not able to accurately reproduce these SLW clouds, the discrepancy between the observed and calculated net surface radiation was even greater than in the typical PBL period, reaching + 50 W m-2, mainly attributable to longwave downward surface radiation from BSRN being 100 W m-2 greater than that of ARPEGE-SH. The absence of SLW clouds in NWPs over Antarctica may indicate an incorrect simulation of the radiative budget of the polar atmosphere. [ABSTRACT FROM AUTHOR]