Météo-France

Observations of Titan were performed on the 4 November 2022 with the Near Infra- Red Spectrometer (NIRSpec) onboard the James Webb Space Telescope (JWST). The observation is a spectro-image of Titan taken about two and a half years before the northern autumnal equinox. This spectro-image at low phase angle covers a spectral range between 0.95 to 5 . 27 μ m and has a spectral resolving power R= λ / Δ λ =2700. Titan was resolved with about eight pixels across the disk, corresponding to a spatial resolution of 643 km at the centre of Titan. However, the data was resampled to obtain a final spectro-image with thirty pixels across the disk. In this study, we retrieved the distribution of the photochemical haze (above 80 km) and the condensate mist layer (below 80 km) as a function of latitude and altitude. Due to the high NIRSpec spectral resolution, we can distinguish five mist sublayers. We found a haze distribution with extinctions increasing from the north to the south, consistent with the direction of stratospheric meridional winds. The distribution of the mist layer depends on both the circulation and the thermodynamic conditions relating to cloud formation. We found an upper mist layer, above 40 km, having a latitudinal distribution similar to the haze with an increase from north to south. The lower mist, below 40 km, has a minimum around 10 ° N and increases toward the north and the south. The retrieved mist layer also presents a significant increase in extinction that emerges from the background at an altitude of around 50 to 60 km in the southern hemisphere. Finally, we retrieved the surface reflectivity that varies with latitude but is always spectrally consistent with the reflectivity of water ice grains with radius 100 μ m . These results yield important constraints for climate models and, in return, climate models such as the Titan Planetary Climate Model (PCM) will help to fully understand the meaning of our results.

Abstract. Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Abstract. We describe an improved method and the associated package for estimating the statistics of temperature extremes in a Bayesian framework. Building on previous work, this method uses a range of climate model simulations to provide a prior of the real-world changes, and then considers observations to derive a posterior estimate of past and future changes. The new version described in this study makes it possible to process several scenarios simultaneously, while keeping one single counterfactual world (i.e., the world without human influence). We offer a free licensed, easy-to-use command-line tool called ANKIALE (ANalysis of Klimate with bayesian Inference: AppLication to extreme Events), which can be used to reproduce the analyses presented here, as well as to process user-defined events. ANKIALE is based on a python code, but is designed to be used from the command line interface. ANKIALE is natively parallel, enabling it to be used on a personal computer as well as on a supercomputer. To derive the posterior, ANKIALE uses state of art MCMC-methods to sample the posterior distribution. The potential of this method and tool is illustrated via an application to maximum temperature over Europe between 1850 and 2100 (the posterior is derived from ERA5, covering the period from 1940 to 2024), at a 0.25° resolution, for a range of four emission scenarios, including a particular focus on the city of Paris (France).

The study of atmospheric composition is a major priority for improving the understanding of future climate models; therefore, it is essential to analyze in detail the variations of certain key variables, particularly water vapor, which strongly influences meteorological phenomena and plays a major role in radiative forcing. During my doctoral project, my research focused on studying the variability of water vapor using radiosoundings, an instrumental method for obtaining precise and high-quality measurements down to the UT/LS zone, by improving the quality of measurements through instrumentation. In addition to internal tests aimed at obtaining higher-quality measurements, setting up observation campaigns is a top priority in order to validate the performance of the sondes under real-world conditions, identify seasonal biases, and thus be able to make various corrections if necessary. Therefore, the TRACIS (Tropospheric Research Campaign on Air Moisture Content by Ipral at SIRTA) comparison campaign, in which I participated and which took place at the SIRTA site (Atmospheric Remote Sensing Research Instrumental Site) [48.71331°N, 2.20901°E] from May 12 to June 12, 2025, made it possible to assess the consistency of the data between the different sondes, while comparing these datasets with auxiliary sources such as ground-based observations and satellite measurements. This multi-source approach notably includes measurements from the IPRAL LiDAR, as well as fields from the ERA5 reanalysis model, thus strengthening the comparison, identifying potential systematic biases, and improving the overall interpretation of the results. Preliminary analyses focus on comparisons between the combined working measurement standard (CWS; mean RH M10/RS41) and other convergent datasets (M20, ERA5, and IPRAL LiDAR), allowing an evaluation of the representativeness and consistency of the different observation sources.

Over the past couple of decades, thanks to the sustained development of Global Climate Models (GCMs) combined with dedicated downscaling strategies such as regional climate modelling or statistical downscaling, climate projections and associated services are now increasingly available across many regions, particularly in nations of the Global North like France. However, whereas this is the case for continental France, the country includes numerous overseas territories, most of them being small islands in the tropical Atlantic, Indian and Pacific Oceans, where this information was only partially available until recently, if at all.Here we present the recent development of ensembles of climate projections for most French tropical overseas territories (French West Indies and Guiana, Reunion Island, Mayotte, New Caledonia and French Polynesia), complemented with services in the form of climate information provided for different regional warming levels. The ensembles consist in the blending of data from global climate models (CMIP6), regional climate models (e.g. CORDEX), high-resolution global models and convection-permitting models where available. The models are evaluated against gridded and local observations with a focus on important regional climate processes, and selected accordingly for each domain. Fine-scale reference products for daily surface temperature and precipitation are developed for each territory. They combine long-term weather station observations with high-resolution data from either evaluation simulations driven by the ERA5 reanalysis or numerical weather prediction models. These products are then used to bias-correct and statistically downscale model fields, thereby providing kilometer-scale ensembles of transient climate simulations for each territory over both the historical and future periods, which are made freely available on national climate data portals (DRIAS, Climadiag Commune).In addition, local temperature observations are used to constrain warming projections from CMIP6 for each territory, in order to compute regional warming levels corresponding to global warming levels +1.5°C, +2°C and +3°C. Following the national reference warming trajectory for adaptation to climate change (TRACC), a framework that has been previously applied over continental France to guide adaptation policies, climate indices are computed for these regional warming levels from the aforementioned climate projections and also made freely available. In addition to generic indices (e.g. number of hot days/nights, of heavy precipitation days etc.), tailored indices for the agriculture, water resource, energy, public health and disaster management (wildfires, coastal hazards) sectors are being developed using local impact data from various stakeholders.Future extensions include regional climate model emulators previously developed for European domains. They show encouraging results for tropical islands and are expected to make key contributions to the characterization and understanding of climate projection uncertainties in these data-scarce regions.

Quantifying the evaporative flux from the land surface into the atmosphere is important for local irrigation management, regional water management and global weather prediction, but measuring evapotranspiration (ET) remains a challenge. Methods that directly measure ET, including eddy covariance and lysimeters, have high temporal resolution but limited spatial coverage, while indirect estimates, including remote sensing techniques, have broad spatial coverage but lower temporal frequency. Each principle for estimating ET comes with unique uncertainties, so a combined use of these methods under identical conditions, which is rarely done in field campaigns, should be promoted to assess its relative and overall performance. In order to quantify the relative uncertainty of different commonly-used ET estimation techniques, we performed an ET method intercomparison using data from the 2021 Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) experiment (Boone et al., 2025). Fifteen ET estimation methods were evaluated across six crop types, spanning footprint extents from individual fields to the regional scale and encompassing multiple measurement principles. We also include an upscaled “mixed-agriculture” estimate from the eddy-covariance data to match the footprints of methodologies that cover more than one field (e.g. satellite remote sensing data and long-path scintillometry) We find that the total sampling uncertainty in the eddy covariance measurements is between 10% and 20% of the latent heat flux regardless of time of day. Moreover, we find that standard deviation among different ET methods for a single crop type (between 2.0 and 3.7 mm day-1) is greater than the standard deviation among the different crop types (1.9 mm day-1) in the LIAISE domain.

At high altitudes and latitudes, snow has a large influence on hydrological processes. Large fractions of these regions are covered by forests, which have a strong influence on snow accumulation and melting processes. Trees absorb a large part of the incoming shortwave radiation and this heat load is mostly dissipated as longwave radiation. Trees shelter the snow surface from wind, so sub-canopy snowmelt depends mainly on the radiative fluxes: vegetation attenuates the transmission of shortwave radiation but enhances longwave irradiance to the surface. 13 pyranometers and 11 pyrgeometers were deployed on the snow surface below a coniferous forest at the CEN-MeteoFrance Col de Porte station in the French Alps (1325m asl) during the winters 2016-17 and 2017-18 in order to investigate spatial and temporal variabilities of solar and infrared irradiances in different meteorological conditions. Sky view factors measured with hemispherical photographs at each radiometer location ranged from 1.5 to 3.5. In clear sky conditions, the attenuation of solar radiation by the canopy reached 96% and its spatial variability exceeded 100 W.m-2. Longwave irradiance varied by 30 W.m-2 from dense canopy to gap areas. In overcast conditions, the spatial variabilities of solar and infrared irradiances were reduced and remained closely related to the sky view factor. Comparing the measurements at different radiometer locations, we investigated the dependence of surface net radiation on the overlying canopy density. Of particular interest were the atmospheric conditions that favor an offset between shortwave energy attenuation and longwave irradiance enhancement by the canopy, such that net radiation does not decrease with increasing forest density (situations of “radiation paradox”). It was found that cloud effects on the shortwave transmissivity and longwave emissivity factors of the canopy have a strong impact on the subcanopy radiation fluxes: canopy largely counteracts the effects of clouds on the incoming radiation fluxes. As a result, variations in net surface radiation due to forest cover appear to depend largely on meteorological conditions: “radiative paradox” conditions were more frequent during the winter of 2017 than in 2018, which was cloudier and colder. As a result, variations in surface net surface radiation by canopy cover appear to be largely dependent on weather conditions: “radiative paradox” conditions were more prevalent during the winter of 2017 than in 2018, which was cloudier and colder.

ACCORD is a consortium made up of 26 Meteorological Services (https://www.accord-nwp.org/ ). The primary objective is to provide the consortium's member services with state-of-the-art numerical weather prediction (NWP) limited-area model codes. A substantial part of the ACCORD codes are shared with IFS-ARPEGE.During phase 1 of ACCORD (2021-2025), collaborative working methods have been drastically modernized. Code management has greatly benefited from the implementation of the ACCORD software forge (Github) as well as from the expanded use of a testing tool which enables component-wise testing of new code versions. The adaptation of the codes to new HPC architectures (CPU-GPU accelerators) has largely progressed in close collaboration with MF (ARPEGE) and ECMWF (IFS).Research on the current (semi-implicit semi-Lagrangian spectral) dynamical kernel of ACCORD models continues, notably through an extensive reformulation of the semi-implicit operator. In addition, the alternative FVM (Finite Volume Model) code, initially developed at ECMWF, is being studied. SURFEX (https://www.umr-cnrm.fr/surfex/ ) has become the main code infrastructure for modeling surface processes and the surface-atmosphere interface. Efforts devoted to developing new options for very high-resolution modeling are increasing: dynamical kernel, 3D aspects of turbulence and radiation, refined surface characteristics, all for models at the hectometer scale. This trend is largely driven by user needs.In data assimilation, a major advance is the near-operational status of flow-dependent algorithms (EnVar-type algorithms coded in the OOPS software framework). ACCORD has maintained first-rate expertise in preprocessing observations for assimilation. This applies both to satellite data (infrared or microwave, in polar orbit or geostationary orbit) and to ground-based networks of various types (radar, surface networks, citizen observations) or aircraft (Mode-S). In probabilistic forecasting and ensemble methods (EPS), scientific collaboration focuses (among other aspects) on ensemble perturbation methods. Several approaches for model perturbations have been studied, such as tendency perturbations (SPPT), model parameter perturbations (SPP or RP), and surface field perturbations.A new scientific strategy was approved by the Assembly of Directors in December 2024 for the next Programme phase (2026-2030). The main objectives include:Continue modernizing the collaborative working methods.Continue adapting the codes for CPU-GPU architectures.Continue research on the various model components with an increased focus on very high resolution and high-impact weather forecasting.Develop a research infrastructure enabling process-oriented meteorological evaluation of models using specialized observations.Continue to develop DA algorithms with flow dependence, leveraging observations from the coming years (MTG etc.).Pursue and strengthen scientific collaboration on ensemble forecasting systems.In connection with the rapid evolution of data driven forecast tools, ACCORD members want to be proactive in AI initiatives in Europe (within EUMETNET and with ECMWF). The scientific strategy foresees exploring hybrid "AI-physical NWP" solutions.In the presentation, a few keynote features of the ACCORD scientific strategy will be further addressed.