We present an observing simulated system experiment (OSSE) dedicated to evaluate the potential added value from the Sentinel-4 and the Sentinel-5P observations on tropospheric ozone composition. For this purpose, the ozone data of Sentinel-4 (Ultraviolet Visible Near-infrared) and Sentinel-5P (TROPOspheric Monitoring Instrument) on board a geostationary (GEO) and a low-Earth-orbit (LEO) platform, respectively, have been simulated using the DISAMAR inversion package for the summer 2003. To ensure the robustness of the results, the OSSE has been configured with conservative assumptions. We simulate the reality by combining two chemistry transport models (CTMs): the LOng Term Ozone Simulation – EURopean Operational Smog (LOTOS-EUROS) and the Transport Model version 5 (TM5). The assimilation system is based on a different CTM, the MOdèle de Chimie Atmosphérique à Grande Echelle (MOCAGE), combined with the 3-D variational technique. The background error covariance matrix does not evolve in time and its variance is proportional to the field values. The simulated data are formed of six eigenvectors to minimize the size of the dataset by removing the noise-dominated part of the observations. The results show that the satellite data clearly bring direct added value around 200 hPa for the whole assimilation period and for the whole European domain, while a likely indirect added value is identified but not for the whole period and domain at 500 hPa, and to a lower extent at 700 hPa. In addition, the ozone added value from Sentinel-5P (LEO) appears close to that from Sentinel-4 (GEO) in the free troposphere (200–500 hPa) in our OSSE. The outcome of our study is a result of the OSSE design and the choice within each of the components of the system.

The behavior of the extratropical transition layer (ExTL) is investigated using a chemistry transport model (CTM) and analyses derived from assimilation of MLS (Microwave Limb Sounder) O₃ and MOPITT (Measurements Of Pollution In The Troposphere) CO data. We firstly focus on a stratosphere–troposphere exchange (STE) case study that occurred on 15 August 2007 over the British Isles (50° N, 10° W). We evaluate the effect of data assimilation on the O₃–CO correlations. It is shown that data assimilation disrupts the relationship in the transition region. When MLS O₃ is assimilated, CO and O₃ values are not consistent between each other, leading to unphysical correlations at the STE location. When MLS O₃ and MOPITT CO assimilated fields are taken into account in the diagnostics the relationship happens to be more physical. We then use O₃–CO correlations to quantify the effect of data assimilation on the height and depth of the ExTL. When the free-model run O₃ and CO fields are used in the diagnostics, the ExTL distribution is found 1.1 km above the thermal tropopause and is 2.6 km wide (2σ). MOPITT CO analyses only slightly sharpen (by −0.02 km) and lower (by −0.2 km) the ExTL distribution. MLS O₃ analyses provide an expansion (by +0.9 km) of the ExTL distribution, suggesting a more intense O₃ mixing. However, the MLS O₃ analyses ExTL distribution shows a maximum close to the thermal tropopause and a mean location closer to the thermal tropopause (+0.45 km). When MLS O₃ and MOPITT CO analyses are used together, the ExTL shows a mean location that is the closest to the thermal tropopause (+0.16 km). We also extend the study at the global scale on 15 August 2007 and for the month of August 2007. MOPITT CO analyses still show a narrower chemical transition between stratosphere and troposphere than the free-model run. MLS O₃ analyses move the ExTL toward the troposphere and broaden it. When MLS O₃ analyses and MOPITT CO analyses are used together, the ExTL matches the thermal tropopause poleward of 50°.

Nitrous oxide (N${}_2$O) is a greenhouse gas difficult to estimate by satellite because of its weak spectral signature in the infra-red band and its low variability in the troposphere. Nevertheless, this study presents the evaluation of new tropospheric N${}_2$O observations from the Infrared Atmospheric Sounder Interferometer (IASI) on Metop-A using the Toulouse N${}_2$O Retrieval Version 2.0 tool. This tool is based on the Radiative Transfer for Tiros Operational Vertical sounder (RTTOV) model version 12.3 coupled to the Levenberg-Marquardt optimal estimation method enabling the simultaneous retrieval of methane, water vapour, temperature profiles together with surface temperature and emissivity within the 1240–1350 cm${}^{-1}$ window. In this study, we focused on the upper troposphere (300 hPa) where the sensitivity of IASI is significant. The IASI N${}_2$O data has been evaluated using aircraft N${}_2$O observations from the High-performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations (HIPPO) campaigns in 2009, 2010, and 2011 and from the National Oceanic and Atmospheric Administration’s (NOAA) Global Greenhouse Gas Reference Network (GGGRN) in 2011. In addition, we evaluated the IASI N${}_2$O using ground-based N${}_2$O measurements from 9 stations belonging to the Network for the Detection of Atmospheric Composition Change (NDACC). We found a total random error of ∼2 ppbv (0.6%) for one single retrieval at 300 hPa. Under favorable conditions, this error is also found in the vertical level pressure range 300–500 hPa. It decreases rapidly to ∼0.4 ppbv (0.1%) when we average on a 1° × 1° box. In addition, independent observations allows the estimation of bias with the IASI TN${}_2$OR v2.0 N${}_2$O. The bias between IASI and aircraft N${}_2$O data at 300 hPa is ∼1.0 ppbv (∼0.3%). We found an estimated random error of ∼2.3 ppbv (∼0.75%). This study also shows relatively high correlations between IASI data and aircraft in situ profiles but more varying correlations over the year 2011 depending on the location between IASI and NDACC remote sensing data. Finally, we present daily, monthly, and seasonal IASI N${}_2$O horizontal distributions in the upper troposphere as well as cross sections for different seasons that exhibit maxima in the Tropical band especially over Africa and South America.

The aim of this paper is to study the transport of nitrous oxide (N₂O) from the Asian surface to the eastern Mediterranean Basin (MB). We used measurements from the spectrometer Thermal and Near infrared Sensor for carbon Observation Fourier transform spectrometer on board the Greenhouse gases Observing SATellite (GOSAT) over the period of 2010–2013. We also used the outputs from the chemical transport model LMDz‐OR‐INCA over the same period. By comparing GOSAT upper tropospheric retrievals to aircraft measurements from the High‐performance Instrumented Airborne Platform for Environmental Research Pole‐to‐Pole Observations, we calculated a GOSAT High‐performance Instrumented Airborne Platform for Environmental Research standard deviation (SD error) of ~2.0 ppbv for a single pixel and a mean bias of approximately −1.3 ppbv (approximately −0.4%). This SD error is reduced to ~0.1 ppbv when we average the pixels regionally and monthly over the MB. The use of nitrogen fertilizer coupled with high soil humidity during the summer Asian monsoon produces high N₂O emissions, which are transported from Asian surfaces to the eastern MB. This summertime enrichment over the eastern MB produces a maximum in the difference between the eastern and the western MB upper tropospheric N₂O (east‐west difference) in July in both the measurements and the model. N₂O over the eastern MB can therefore be considered as a footprint of Asian summertime emissions. However, the peak‐to‐peak amplitude of the east‐west difference observed by GOSAT (~1.4 ± 0.3 ppbv) is larger than that calculated by LMDz‐OR‐INCA (~0.8 ppbv). This is due to an underestimation of N₂O emissions in the model and to a relatively coarse spatial resolution of the model that tends to underestimate the N₂O accumulation into the Asian monsoon anticyclone.

The Antarctic atmosphere is rapidly changing, but there are few observations available in the interior of the continent to quantify this change due to few ground stations and satellite measurements. The Concordia station is located on the East Antarctic Plateau (75° S, 123° E, 3233 m above mean sea level), one of the driest and coldest places on Earth. Several remote sensing instruments are available at the station to probe the atmosphere, together with operational meteorological sensors. In order to observe in situ clouds, temperature, relative humidity and supercooled liquid water (SLW) at a high vertical resolution, a new project based on the use of an unmanned aerial vehicle (drone) vertical take-off and landing from the DeltaQuad Company has been set up at Concordia. A standard Vaisala pressure, temperature and relative humidity sensor was installed aboard the drone coupled to an Anasphere SLW sensor. A total of thirteen flights were conducted from 24 December 2022 to 17 January 2023: nine technology flights and four science flights (on 2, 10, 11 and 13 January 2023). Drone-based temperature and relative humidity profiles were compared to (1) the balloon-borne meteorological observations at 12:00 UTC, (2) the ground-based microwave radiometer HAMSTRAD and (3) the outputs from the numerical weather prediction models ARPEGE and AROME. No SLW clouds were present during the period of observations. Despite technical issues with drone operation due to the harsh environments encountered (altitude, temperature and geomagnetic field), the drone-based observations were consistent with the balloon-borne observations of temperature and relative humidity. The radiometer showed a systematic negative bias in temperature of 2 °C, and the two models were, in the lowermost troposphere, systematically warmer (by 2–4 °C) and moister (by 10–30%) than the drone-based observations. Our study shows the great potential of a drone to probe the Antarctic atmosphere in situ at very high vertical resolution (a few meters).

This paper highlights the advantages of an affordable multi-wavelength ground-based camera, called WaltRCam, for monitoring Aerosol Optical Depth (AOD) in a clear case over a peri-urban area. To simulate the performance of this low-cost camera, for which data are not yet available, we use data from an expensive hyperspectral camera (HSI) to mimic its characteristics. Our methodology is based on the construction of look-up tables using the DART (Discrete Anisotropic Radiative Transfer) 3D radiative transfer model. DART simulates the different spectra observed by the WaltRCam camera, which then provides the AODs for all image pixels in near-real-time. Moreover, DART is coupled to a 3D scale-model of the city of Toulouse (dating from 2014) to model complex urban geometries and to associate specific optical properties to the various objects that make up the environment. Moreover, we use a neural-network-based method to recognize the various objects in the image in order to take into account only pixels common between the observations. In this way, we take account of changes to the peri-urban area, such as vegetation growth, construction, demolition of buildings, etc. The results of this study show that the WaltRCam camera, by capturing eight wavelengths, can deliver convincing results compared with ground and satellite reference data, with a correlation coefficient of 0.9 and an average RMSE of less than 0.02.