Collecte Localisation Satellites (France)

Abstract Medicanes are cyclones with tropical characteristics in the Mediterranean basin. They typically show an eye with neighboring spiraling bands, but the surrounding region of maximum winds is not well documented, especially near the sea surface, due to limited available observational data. Estimating such characteristics accurately is essential for a better understanding of the specific fine‐scale wind structures and their relationship with the development mechanisms of medicanes. Here we first demonstrate the capacity of synthetic aperture radar (SAR) spaceborne instruments to diagnose important medicane structural characteristics. Building on an unprecedented collection of SAR high‐resolution observations and subsequent sea‐surface wind‐speed estimates, the eye patterns, heavy rainfall, convective cells, and boundary‐layer roll vortices are evidenced, with attributes comparable with those of tropical cyclones of similar intensities. Motivated by this resemblance, we estimate parameters characterizing the cyclone surface wind structure (the intensity, inner and outer size, and Rossby number) from high‐resolution observations of medicanes. We show that their inner size is of magnitude comparable with that of tropical cyclones of similar intensities. Conversely, typical outer sizes and Rossby numbers are found to be smaller. The misrepresentation of these characteristics in medium‐resolution global reanalyses brings into question their suitability and the application of diagnostic tools to these data to monitor the life cycles of medicanes. This invites us to develop dedicated observation‐based monitoring frameworks. Medium‐resolution scatterometers provide more reliable structural estimates despite the small medicane size. Along with SAR surface wind‐speed estimates, and complemented by upper‐level measurements, they may support future reanalysis efforts. Further investigation is needed to understand how the high‐resolution features reported in our work connect with kilometer‐ and subkilometer‐scale physical processes.

Zenith Total Delay (ZTD) estimates derived from GNSS observations are essential for atmospheric and geodetic applications. When processed using Precise Point Positioning (PPP), ZTD time series exhibit enhanced stability compared to network-based approaches. However, occasional outliers - ranging from a few centimetres to several meters - still occur, potentially degrading product quality and impacting downstream applications. The mechanisms driving these anomalies remain poorly understood, and their characterisation is critical for improving PPP-based ZTD products. This study examines the nature, origins, and possible mitigation strategies for such outliers in order to enhance the reliability of GNSS-derived tropospheric parameters.We perform sensitivity tests using the CNES’s GINS software in PPP mode with integer ambiguity resolution, complemented by simplified PPP-like simulations, to identify the mechanisms underlying ZTD outliers. Particular attention is given to pre-processing procedures, which are critical for detecting and handling problematic observations and significantly impact ZTD accuracy. Building on this diagnostic phase, we explore parameter regularisation strategies aimed at mitigating the occurrence of ZTD outliers while preserving high processing quality. These analyses provide insights into both the origin of anomalies and practical approaches for improving the robustness of PPP-based tropospheric products.In addition, we investigate complementary post-processing screening methods based either on purely statistical approaches or on the comparison with independent atmospheric reanalysis ZTD data. Combined with the strategies described above, these methods aim to reduce ZTD outliers while preserving geophysical variability. This integrated approach enhances GNSS positioning performance and improves the reliability of long-term GNSS-derived tropospheric time series, supporting climate monitoring and other atmospheric applications.

Amongst the Thematic Assembly Centers (TACs) of the Copernicus Marine Service, the Wind TAC is in charge of providing L3 and L4 sea surface wind and wind stress observation products at a global and regional scale. From 2015, the Wind TAC has been successful in providing data coming from scatterometer observations both Near Real Time (NRT) and reprocessed (Multi Year, MY) data. Since the new phase of the project, starting in 2025, high-resolution (i.e. 1 km) data from C-band Synthetic Aperture Radar (SAR) Satellite have been included in the Wind TAC. As the Copernicus Marine Service evolution is mainly user-driven, we take this opportunity to announce that L3 daily products of Sentinel-1A winds are operationally processed and delivered in Near Real Time since June 2024. The associated Multi Year database of L3 daily products has also been uploaded to the Copernicus Marine Service and covers data from 2018 to 2025. To obtain wind data from SAR observations the ocean surface roughness measured by the satellite is transformed into wind speed and direction using an empirical inversion method called the "dual-pol" (Mouche et al. 2017, Mouche et al. 2019) and an a priori wind helps constrain the possible values. To this purpose, the SAR Wind Production Unit uses the L2 files produced by ESA's Mission Performance Center for Sentinel-1 (MPC-SAR) both for NRT and MY processing. Then, the only difference between the two processing chains (NRT and MY) lies in the a priori wind used to constrain the computed wind speed: while the NRT chain uses wind model from the European Center for Medium-range Weather and Forecasting (ECMWF) operational Integrated Forecast System (IFS), the MY chain uses wind model from ECMWF ReAnalysis v5 (ERA5). To quantify the impact of the input a priori wind, the NRT and MY data are compared over their common period. More generally, the SAR wind data from Wind TAC has been qualified through comparisons against Numerical Weather Forecast (NWP) models, in situ measurements from buoys and co-located measurements from scatterometer. The results of these comparisons are shown here, illustrating some limitations in the methodology and ways to overcome them. Overall, these global comparisons highlight the complementarity of high-resolution SAR wind data with other wind observations. In the near future, the integration (in 2026 and 2027 respectively) in the SAR Wind TAC of the recently launched Sentinel-1C and Sentinel-1D satellites will allow for better coverage, and the complementarity between observation should be further investigated through triple-collocation between SAR, scatterometer and buoy data.

Abstract. This study introduces the new global Mesoscale Eddy Trajectory Atlases (META4.0), which provide eddy detections, trajectories, and interaction networks derived from satellite altimetry. Eddy detection relies on the pyeddytracker (PET) algorithm (Mason et al., 2014), further optimized by Pegliasco et al. (2022), and represents a substantial improvement over the previous META3.2 product (SSALTO/DUACS, distributed by AVISO+ with CNES support). The main advance of META4.0 is the explicit identification of eddy merging and splitting events. By combining grouping, which links detections across consecutive days, and segmentation, which tracks continuity through interaction events, trajectories are organized into networks of interconnected eddies. This network-based representation complements the classical single-trajectory view of eddy life cycles by explicitly accounting for eddy interactions. The paper presents both diagnostic tools designed to explore individual eddy networks (e.g., timelines, spatial trajectories, and eddy properties such as effective radius or shape error) and the results of a global statistical analysis over more than three decades. These analyses provide new insights into network properties, eddy lifetimes, and the spatial and temporal distribution of merging and splitting events. Clustering analyses reveal recurrent interaction patterns and identify regions where eddy networks are particularly active. Independent datasets, including sea surface temperature and chlorophyll concentration, are used for qualitative validation of selected events. Finally, Lagrangian advection of synthetic particles highlights coherent forward and backward transport signatures associated with interaction events, providing a physical validation of the reconstructed networks. Overall, META4.0 offers a novel and physically consistent framework to characterize mesoscale eddy interactions and to better understand their role in shaping ocean dynamics.

Abstract Since the 2010 launch of Cryosat‐2, a new generation of altimeters, referred to as SAR altimetry, has emerged and partially replaced the previous conventional altimeters known as Low Resolution Mode (LRM) altimetry. A surface slope correction has been previously developed for LRM altimetry. However, the differences in the way the two altimeters work, and in particular their radar footprint, make LRM altimeter slope correction inapplicable to SAR altimetry. Thus, in this paper, a slope correction model is provided for SAR altimetry, derived from the LRM‐based approach. The shape of the SAR footprint induces that height correction depends on each satellite mission. Consequently, a generic method allowing to generate global maps of height correction for distinct missions is provided. The maps are computed for the Sentinel‐6A mission and the importance of correcting this effect for SAR altimetry is highlighted by studying the sea surface height anomaly biases between Sentinel‐6A SAR and LRM measurements. Finally, it is shown that applying the slope correction to Sentinel‐6A SAR mode sea surface height anomaly measurements enhances their consistency with the latest Mean Sea Surface (MSS) model, reducing the root mean square error between the sea surface height anomaly and the MSS model by up to 1 cm.

Abstract The term “Mediterranean tropical-like cyclone” or “medicane” has been used in different ways by different authors. Identification of medicanes has been based on features observed from satellite imagery or on diagnostics applied to numerical weather prediction model outputs. In the absence of an official definition, medicanes are generally considered to be cyclones over the Mediterranean sharing physical processes with tropical cyclones. Nevertheless, recent studies on the dynamics of several systems widely recognized as medicanes show different underlying development mechanisms. A commonly agreed definition is critical and necessary to assess their climatology in past and future climates, as well as to consistently identify such systems in weather forecasts. The scientific community working on Mediterranean cyclones hereby proposes a definition, which is based solely on Earth observations: “A medicane is a mesoscale cyclone that develops over the Mediterranean Sea and displays tropical-like cyclone characteristics: a warm core extending into the upper troposphere, an eye-like feature in its center with spiral cloud bands around, an almost windless center surrounded by nearly-symmetric sea surface wind circulation with maximum wind speed within a few tens of km from the center.” Significance Statement Nearly two decades have passed since the term “medicane,” short for “Mediterranean hurricane,” has become commonly accepted to represent Mediterranean vortices with satellite-observed characteristics typical of tropical cyclones. The absence of an official or commonly accepted definition has created some confusion within the scientific and operational community. To overcome this ambiguity, here, we propose a phenomenological definition that can serve as a reference for future scientific research and operational applications, to be applied to any cyclonic system in the Mediterranean that bears structural similarities to tropical cyclones. This simple definition will assure consistency and, at the same time, can be used with the outputs from numerical models, working as a reference for future scientific research or operational applications.

The wind field is a critical factor in the study of global and local climate dynamics, as well as marine environmental processes. It is a key component in understanding ocean thermohaline circulation, land-sea interactions, tropical cyclones, storm surges, and climate forecasting. To this end, multiple solutions have been proposed to provide technical support for obtaining global sea surface wind field information. However, both algorithmic and deep learning-based frameworks have limitations in terms of prior mandatory information or estimation accuracy. In this paper, we propose a transformer-based deep learning model, namely WindFormer, to accurately retrieve the absolute value of the wind speed, without any prior assumption about local wind patterns. Our approach surpasses the deployed Sentinel-1 Ocean Wind Fields (OWI) algorithm with 0.49 m/s, as well as other state-of-the-art deep learning methods, on our developed data set.

Abstract. The Infrared Atmospheric Sounder Interferometer (IASI) is a Fourier Transform Spectrometer onboard the Eumetsat MetOp (Meteorological Operational) polar orbit satellite series. The three MetOp satellites (A, B, and C) were launched in October 2006, September 2012 and November 2018, respectively. IASI-B and IASI-C are still operational, while IASI-A was decommissioned in November 2021. IASI’s mission is to provide accurate atmospheric spectra primarily for meteorological and climate applications, and as such a high measurement precision is required. Furthermore, the estimation of the measurement uncertainty is a valuable element, especially concerning climate studies and the establishment of long climate series. This study presents the global radiometric uncertainty budget as estimated for the IASI instruments. Four major contributors, the correction of the analog non-linearity, the black body characterization, the scan mirror reflectivity, and the background radiance instability, have been identified and their impact is presented alongside other minor contributors. For black body or earth view scenes under investigation, the global budget was found to be lower than 0.2 K for a reference temperature of 280 K, when all uncertainties were considered fully correlated. These estimates are expected to remain relatively stable throughout the instrument lifetime.