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Besides their strong contribution to weather forecast improvement through data assimilation, thermal infrared sounders onboard polar-orbiting platforms are now playing a key role for monitoring atmospheric composition changes. The Infrared Atmospheric Sounding Interferometer (IASI) instrument developed by the French space agency (CNES) and launched by EUMETSAT onboard the Metop satellite series is providing essential inputs for weather forecasting and pollution/climate monitoring owing to its smart combination of large horizontal swath, good spectral resolution and high radiometric performance. EUMETSAT is currently preparing the next polar-orbiting program (EPS-SG) with the Metop-SG satellite series that should be launched around 2020. In this framework, CNES is studying the concept of a new instrument, the IASI-New Generation (IASI-NG), characterized by an improvement of both spectral and radiometric characteristics as compared to IASI, with three objectives: (i) continuity of the IASI/Metop series; (ii) improvement of vertical resolution; and (iii) improvement of the accuracy and detection threshold for atmospheric and surface components. In this paper, we show that an improvement of spectral resolution and radiometric noise fulfil these objectives by leading to (i) a better vertical coverage in the lower part of the troposphere, thanks to the increase in spectral resolution; and (ii) an increase in the accuracy of the retrieval of several thermodynamic, climate and chemistry variables, thanks to the improved signal-to-noise ratio as well as less interference between the signatures of the absorbing species in the measured radiances. The detection limit of several atmospheric species is also improved. We conclude that IASI-NG has the potential to strongly benefit the numerical weather prediction, chemistry and climate communities now connected through the European GMES/Copernicus initiative.

Validation of ozone profiles measured from a nadir looking satellite instrument over Antarctica is a challenging task due to differences in their vertical sensitivity with ozonesonde measurements. In this paper, ozone observations provided by the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard the polar-orbiting satellite MetOp are compared with ozone profiles collected between August and October 2010 at McMurdo Station, Antarctica, during the Concordiasi measurement campaign. The main objective of the campaign was the satellite data validation. With this aim 20 zero-pressure sounding balloons carrying ozonesondes were launched during this period when the MetOp satellite was passing above McMurdo. This makes the dataset relevant for comparison, especially because the balloons covered the entire altitude range of IASI profiles. The validation methodology and the collocation criteria vary according to the availability of global positioning system auxiliary data with each electro-chemical cell ozonesonde observation. The relative mean difference is shown to depend on the vertical range investigated. The analysis shows a good agreement in the troposphere (below 10 km) and middle stratosphere (25–40 km), where the differences are lower than 10%. However a significant positive bias of about 10–26% is estimated in the lower stratosphere at 10–25 km, depending on altitude. The positive bias in the 10–25 km range is consistent with previously reported studies comparing in situ data with thermal infrared satellite measurements. This study allows for a better characterization of IASI-retrieved ozone over the polar region during ozone depletion/recovery processes.

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).

Sulfur dioxide emitted during volcanic eruptions can be hazardous for aviation safety. As part of their activities, the Volcanic Ash Advisory Centres (VAACs) are therefore interested in the real-time atmospheric monitoring of this gas. A recent development aims at improving the forecasts of volcanic sulfur dioxide quantities made by the MOCAGE (Modèle de Chimie Atmosphérique à Grande Échelle) chemistry transport model. For this purpose, observations from both TROPOMI (Tropospheric Monitoring Instrument) and IASI (Infrared Atmospheric Sounding Interferometer; B and C) located on separate polar-orbiting satellites are assimilated into the model. These sulfur dioxide measurements are based on the eruption event of the La Soufrière Saint Vincent volcano in April 2021. Observations from OMI (Ozone Monitoring Instrument) are considered validation data. The resulting assimilation experiments show that the combined assimilation of IASI and TROPOMI observations always leads to a better forecast compared to the independent assimilation of data from each instrument. Sulfur dioxide atmospheric field forecasts are better when the available observations are numerous and cover a long time window.

This contribution explores a new approach to forecasting multivariate covariances for atmospheric chemistry through the use of the parametric Kalman filter (PKF). In the PKF formalism, the error covariance matrix is modellized by a covariance model relying on parameters, for which the dynamics are then computed. The PKF has been previously formulated in univariate cases, and a multivariate extension for chemical transport models is explored here. This contribution focuses on the situation where the uncertainty is due to the chemistry but not due to the uncertainty of the weather. To do so, a simplified two-species chemical transport model over a 1D domain is introduced, based on the non-linear Lotka–Volterra equations, which allows us to propose a multivariate pseudo covariance model. Then, the multivariate PKF dynamics are formulated and their 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.

Global warming has focused attention on the polar regions and recent changes in sea and land ice distribution. Accurate modeling of the future evolution of climate and weather in the Antarctic relies heavily on remote sensing observations. However, their reliable assimilation into numerical weather models and reanalyses is challenging because of the unique environment and sparsity of in‐situ observations for validation. We developed a stratospheric balloon‐borne GPS radio occultation system for the 2010 Concordiasi campaign to provide refractivity and derived temperature profiles for improving satellite data assimilation. The observed excess phase delay profiles agree with those simulated from model and dropsonde profiles. 711 occultations were recorded from two balloons, comparable to the number of profiles acquired by 13 driftsonde balloons. Of these profiles, 32% descended to 4 km above the surface, without open‐loop receiver tracking technology, demonstrating it is possible to retrieve useful information with relatively simple low cost instruments. Key Points The first time radio occultation measurements have been made from a balloon Allows continuous monitoring of an air mass Potential for significant improvement of reanalyses in the Antarctic

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 chemistry transport model MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle). The method used to calculate observation errors is a diagnostic based on the observation and analysis residual statistics already adopted in many numerical weather prediction centres. We used a subset of 280 channels covering the spectral range between 980 and 1100 cm−1 to estimate the observation-error covariance matrix. This spectral range includes ozone-sensitive and atmospheric window channels. We computed hourly 3D-Var analyses and compared the resulting O3 fields against ozonesondes and the measurements provided by the Microwave Limb Sounder (MLS) and by the Ozone Monitoring Instrument (OMI). 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 matrix is employed in the assimilation system, by reducing the number of iterations needed for the minimizer to converge.

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.

The Infrared Atmospheric Sounding Interferometer (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.

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 deep) supercooled liquid water (SLW) clouds have been detected and analysed using remotely sensed observations at the station (tropospheric depolarization lidar, the H2O Antarctica Microwave Stratospheric and Tropospheric Radiometer (HAMSTRAD), net surface radiation from the Baseline Surface Radiation Network (BSRN)), radiosondes, and satellite observations (CALIOP, Cloud-Aerosol LIdar with Orthogonal Polarization/CALIPSO, Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) combined with a specific configuration of the numerical weather prediction model: ARPEGE-SH (Action de Recherche Petite Echelle Grande Echelle – Southern Hemisphere). The analysis shows that SLW clouds were present from November to March, with the greatest frequency occurring in December and January when ∼50 % of the days in summer time exhibited SLW clouds for at least 1 h. 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, which 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 with the liquid water path (LWP) strongly underestimated by a factor of 1000 compared to observations. 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 the BSRN longwave downward surface radiation being 50 W m−2 greater than that of ARPEGE-SH. The second case study took place on 20 December 2018, when a warm and wet episode impacted the PBL with no clear diurnal cycle of the PBL top. SLW cloud appearance within the entrainment and capping inversion zones coincided with the warm and wet event. 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 case, reaching +50 W m−2, mainly attributable to the downwelling longwave surface radiation from BSRN being 100 W m−2 greater than that of ARPEGE-SH. The model was then run with a new partition function favouring liquid water for temperatures below −20 down to −40 ∘C. In this test mode, ARPEGE-SH has been able to generate SLW clouds with modelled LWP and net surface radiation consistent with observations during the typical case, whereas, during the perturbed case, the modelled LWP was 10 times less than the observations and the modelled net surface radiation remained lower than the observations by ∼50 W m−2. Accurately modelling the presence of SLW clouds appears crucial to correctly simulate the surface energy budget over the Antarctic Plateau.