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The imprint of marine atmospheric boundary layer (MABL) dynamical structures on sea surface roughness, as seen from Sentinel-1 Synthetic Aperture Radar (SAR) acquisitions, is investigated. We focus on February 13th, 2020, a case study of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) field campaign. For suppressed conditions, convective rolls imprint on sea surface roughness is confirmed through the intercomparison with MABL turbulent organization deduced from airborne measurements. A discretization of the SAR wide swath into 25 x 25 km2 tiles then allows us to capture the spatial variability of the turbulence organization varying from rolls to cells. Secondly, we objectively detect cold pools within the SAR image and combine them with geostationary brightness temperature. The geometrical or physically-based metrics of cold pools are correlated to cloud properties. This provides a promising methodology to analyze the dynamics of convective systems as seen from below and above. Key Points Atmospheric coherent structures, rolls and cold pools are systematically detected and analyzed in a high-resolution SAR wide swath image Properties of rolls from SAR measurements are comparable with the circulation organization deduced from airborne data A diversity of cold pool geometrical and dynamical features is related to cloud life cycle provided by satellite brightness temperature Plain Language Summary We propose an innovative approach to investigate the marine atmospheric boundary layer dynamics by combining spaceborne Synthetic Aperture Radar (SAR) images, brightness temperature from the Geostationary Operational Environmental Satellite (GOES) and in situ turbulence airborne measurements. Focusing on February 13th, 2020, two types of atmospheric processes are investigated: trade wind boundary layer organizations and cold pools. The signature of coherent structures on sea surface roughness, especially convective rolls, is validated with respect

Two air‐sea interaction quantification methods are employed on synthetic aperture radar (SAR) scenes containing atmospheric‐turbulence signatures. Quantification performance is assessed on Obukhov length L, an atmospheric surface‐layer stability metric. The first method correlates spectral energy at specific turbulence‐spectrum wavelengths directly to L. Improved results are obtained from the second method, which relies on a machine‐learning algorithm trained on a wider array of SAR‐derived parameters. When applied on scenes containing convective signatures, the second method is able to predict approximately 80% of observed variance with respect to validation. Estimated wind speed provides the bulk of predictive power while parameters related to the kilometer‐scale distribution of spectral energy contribute to a significant reduction in prediction errors, enabling the methodology to be applied on a scene‐by‐scene basis. Differences between these physically based estimates and parameterized numerical models may guide the latter's improvement.

Tropical cyclone (TC) intensity fluctuations remain a challenge for TC forecasters. Occurring through a wide range of processes, such as vortex contraction, eyewall replacements, or emission of vortex Rossby waves, they are in-herently multiscale, transient, and asymmetric. In a recent study, estimates of surface wind field inner-core properties from high-resolution satellite observations were spotted as valuable for the improvement of intensity variations statistical pre-dictability. The present study evaluates how the temporal evolution of the vortex structure, at scales ranging from O(1) km to vortexwide, further provides insights on the modulation of intensity. The study is based on a set of seven realistic TC simulations with 1-km grid spacing. The surface wind field structure is studied through an original set of descriptors that characterize the radial profile, the azimuthal asymmetries, and their spectral distribution. While radial gradients evolve concurrently with intensity, the azimuthal variability of the inner core shows a stronger connection with shorter-scale inten-sity modulation. The increase of high-wavenumber asymmetries distributed around the ring of maximum winds is shown to precede phases of rapid (re)intensification by 5-6 h, while the concentration of asymmetry in wavenumbers 1 and 2 leads to intensity weakening. A machine learning classification finally highlights that the classification of intensification phases (i.e., intensification or weakening) can be improved by at least 11% (thus reaching ;75%) when accounting for the evolu-tion of the radial wind gradient and the variance distribution among scales in the ring of maximum wind, relative to the sole use of vortex-averaged parameters. SIGNIFICANCE STATEMENT: The purpose of this study is to relate changes in the surface wind field structure of tropical cyclones to their intensity variations. We design an original set of parameters to characterize the inner-and near-core contraction and as

The radius of maximum wind (Rmax), an important parameter in tropical cyclones (TCs) ocean surface wind structure, is currently resolved by only a few sensors, so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semi-empirical model relying on an outer wind radius, intensity and latitude was fit to best-track data. In this study we revise this semi-empirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, Rmax observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an inter-calibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010-2020 and yields Rmax reanalyses and trends more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model’s physical basis, which further shows that radial inflow, boundary layer depth and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on Rmax when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes.

A three-state global estimator of marine surface layer atmospheric stratification is demonstrated using more than 600,000 Sentinel-1 synthetic aperture radar wave mode images at incidence angle ≈ 36.8°. Stratification is quantified using a bulk Richardson number, Ri, derived from collocated ERA5 surface analyses. The three stratification states are defined as unstable: Ri < − 0.012, near-neutral: − 0.012 < Ri < + 0.001 and stable: Ri > + 0.001. These boundaries are identified by the characteristic boundary layer coherent structures that form in these regimes and modulate the surface roughness imaged by the radar. An automated machine learning algorithm identifies the coherent structures impressed on the images. Data from 2016-2019 are used to examine spatial and temporal variation in these state estimates in terms of expected wind and thermal forcing. This new satellite-based approach for detecting air-sea stratification has implications for weather modelling and air-sea flux products. Key Points Common atmospheric boundary layer coherent structures are classified using high-resolution global satellite sea surface radar roughness data Coherent structures partition into separate unstable, near-neutral and stable stratification regimes 3-regime coherent structure and stratification classification can aid air-sea flux estimation and atmosphere boundary layer parameterization Plain Language Summary The air-sea fluxes of momentum, heat, and water vapor are crucial climate data records because they represent lower boundary conditions on the atmospheric circulation and upper boundary conditions of ocean waves and currents. Global measurements of these fluxes using conventional fixed-station systems are impractical making satellite observations attractive. It is very difficult to measure two crucial parameters: the temperature and humidity of the air near the sea surface. The study’s motivation is that external information about the air-sea stratification should improve the

Tropical cyclones (TC) transfer kinetic energy to the upper ocean and thus accelerate the ocean mixed layer (OML) currents. However, the quantitative link between near-surface currents and high wind speeds, under extreme weather conditions, remains poorly understood. In this study, we use multi-mission satellites and drifting-buoy observations to investigate the connections between TC surface winds and currents, including their spatial distribution characteristics. Observed ageostrophic current speeds in the OML increase linearly with wind speeds (for the range 20–50 m/s). The ratios of the ageostrophic current speeds to the wind speeds are found to vary with TC quadrants. In particular, the mean ratio is around 2% in the left-front and left-rear quadrants with relatively small variability, compared to between 2% and 4% in the right-front and right-rear quadrants, with much higher variations. Surface winds and currents both exhibit strong asymmetric features, with the largest wind speeds and currents on the TC right side. In the eyewall region of Hurricane Igor, high winds (e.g. about 47 m/s) induce strong currents (about 2 m/s). The directional rotations of surface winds and currents are resonant and dependent on the location within the storm. Wind directions are approximately aligned with current directions in the right-front quadrant; a difference of about 90o occurs in the left-front and left-rear quadrants. The directional discrepancy between winds and currents in the right-rear quadrant is smaller. Reliable observations of the wind-current relation, including asymmetric features, support published theories developed in idealized numerical experiments to explain the upper ocean response to TCs. Plain Language Summary Observations of surface winds and currents under extreme weather conditions are essential to characterize upper ocean responses to tropical cyclones. To better understand air-sea coupled processes, it is necessary to quantify the connections betwee

SWIM on board CFOSAT is the first spaceborne, low incidence, rotating scatterometer, aiming at measuring in near-real time ocean waves spectra. With 5 off-nadir beams at incidences between 2° and 10° plus one nadir beam, it covers the Earth in 13 days, including polar regions thanks to its polar orbit. This work aims at exploiting the SWIM data over ice regions with two objectives. An off-nadir data-based sea-ice flag is here proposed that allows, first, to eliminate sea-ice polluted echoes for improving the wave spectrum retrieval, and second, to open perspectives for application of sea-ice monitoring with near nadir Ku-band active sensors. To this end, the signature of both open water and sea-ice radar backscatter is parameterized into Geophysical Model Functions. Then, comparisons with observed profiles through a Bayesian scheme provide a probability of sea-ice presence. After comparison with both model (ECMWF-IFS) and radiometer (SSMI) derived reference data sets, the proposed flag is found to be ready for operational use. At latitudes greater than 40° in absolute value, the proposed flagging algorithm exhibits accuracies of approximately 98% for all beams compared to SSMI data. Beam to beam performances are characterized and show potential for the characterization of sea-ice at Ku band.

Synthetic Aperture Radar (SAR) in wave mode is a powerful tool for monitoring sea states in terms of long-period ocean swells of a specific wave directional partition. Since 2016, SARs aboard Sentinel-1A/B operating in wave mode have provided ocean swell spectra dataset as Level-2 Ocean products on a continuous and global basis over open oceans. Furthermore, Level-3 swell products are processed by Copernicus Marine Environment Monitoring Services (CMEMS) taking the benefit of the unique “fireworks” analysis. In this paper, swell wave heights from Sentinel-1A/B wave mode during the period from June 2016 to June 2020 are evaluated. The reference data include the collocated in situ measurements from directional wave buoys and WaveWatch III (WW3) hindcasts. Assessment results show systematic overestimation of approximately 0.2 m in terms of the partitioned swell heights for Sentinel-1A/B Level-2 products compared to the directional buoy observations in eastern Pacific and the western Atlantic. Based on the reliable SAR-WW3 collocations after quality-controls, empirical corrections have been proposed for Sentinel-1 Level-2 swell heights. Independent comparisons against WW3 hindcasts and buoy observations demonstrate the validity of our postprocessing correction for both Level-2 and Level-3 swell heights by eliminating the biases and reducing the root mean square errors. The consistency between CMEMS Level-3 swells and buoy in situ is also examined and discussed by case studies.

Through the Synthetic Aperture Radar (SAR) embarked on the satellites Sentinel-1A and Sentinel-1B of the Copernicus program, a large quantity of observations is routinely acquired over the oceans. A wide range of features from both oceanic (e.g., biological slicks, icebergs, etc.) and meteorologic origin (e.g., rain cells, wind streaks, etc.) are distinguishable on these acquisitions. This paper studies the semantic segmentation of ten metoceanic processes either in the context of a large quantity of image-level groundtruths (i.e., weakly-supervised framework) or of scarce pixel-level groundtruths (i.e., fully-supervised framework). Our main result is that a fully-supervised model outperforms any tested weakly-supervised algorithm. Adding more segmentation examples in the training set would further increase the precision of the predictions. Trained on 20 × 20 km imagettes acquired from the WV acquisition mode of the Sentinel-1 mission, the model is shown to generalize, under some assumptions, to wide-swath SAR data, which further extents its application domain to coastal areas.

Simple Summary Multi-satellite observations to jointly analyze extreme surface wind and wave properties can now be readily obtained to study tropical cyclone (TC) events. Developing over the Philippine Sea, TC Goni was one of the most powerful TCs in 2020. Constrained by Sentinel-1/RadarSat-2 SAR and CFOSAT SCAT satellite data, wind field in the intense TC region is reconstructed. Significant wave height measurements are further obtained from altimeters on-board the CFOSAT, Jason-3, and Sentinel-3A, B satellites. The directional wave spectrum information can be derived from the CFOSAT SWIM off-nadir radar measurements. Using a simplified 2D parametric model and derived self-similar analytical solutions, the measured surface wave amplification in the right-hand TC sector, relative to its propagation direction, is well captured and interpreted. For TC Goni, observed and predicted waves reach 8 m and wavelengths larger than 200 m, leaving the TC inner region in the forward and forward-left direction. In the far TC zone, swell attenuates and superposes with wind waves, not necessarily aligned, to give observed significant wave height values. Multi-satellite data together with simplified parametric model outputs open new perspectives to more precisely study and predict surface waves generated by moving and rapidly evolving TCs for different scientific and practical purposes. Abstract Over the Philippine Sea, the tropical cyclone (TC) Goni reaches category 5 on 29–31 October 2020. Multi-satellite observations, including CFOSAT SWIM/SCAT and Sentinel-1 SAR data, are jointly analyzed to assess the performances of a parametric model. Recently developed to provide a fast estimation of surface wave developments under rapidly evolving TCs, this full 2D parametric model (KYCM) and its simplified self-similar solutions (TC-wave geophysical model function (TCW GMF)) are thoroughly compared with satellite observations. TCW GMF provides immediate first-guess estimates, at any locati

Spaceborne synthetic aperture radar (SAR) has been used for years to estimate high-resolution surface wind field from the ocean surface backscattered signal. Current SAR platforms have one single fixed antenna, and traditional inversion/retrieval schemes rely on one copolarized channel, leading to an unconstrained optimization problem for providing independent estimates of wind speed and direction. For routine application, this is generally solved with a priori information from the numerical weather prediction (NWP) model, inducing severe limitations for rapidly evolving meteorological systems where discrepancies can be significant between model and measurements. In this study, we investigate the benefit of having two simultaneous acquisitions with phase-preserving information in copolarization and cross polarization provided by Sentinel-1 (S-1). A comprehensive analysis of the co-cross-polarization coherence (CCPC) is performed to adequately estimate and calibrate CCPC values from S-1 interferometric wide (IW) mode images acquired over the ocean. A new polarimetric calibration (PolCAL) methodology based on least-squares (LS) criterion and direct matrix inversion is proposed yielding crosstalk estimates. We document CCPC odd symmetry with respect to relative wind direction for light to medium wind speeds (up to 14 m/s) and incidence angle from 30x00B0; to 45x00B0;. The azimuthal modulation is found to increase with both wind speed and incidence angle. An analytical model C-band polarimetric geophysical model function (CPGMF) is provided. The synergy of the CCPC with other radar parameters, such as backscattering coefficients or Doppler, to further constrain the inversion scheme is assessed, opening new perspectives for SAR-based wind field retrieval independent of any NWP model information.

We study the shape of convective rolls in the Marine Atmospheric Boundary Layer from Synthetic Aperture Radar images of the ocean. We propose a multiscale analysis with structure functions which allow an easy generalization to analyse high-order statistics and so to finely describe the shape of the rolls. The two main results are 1) second-order structure function characterizes the wavelength and direction of rolls just like correlation or power spectrum do, 2) high-order statistics can be studied with skewness and flatness which characterize the asymmetry and intermittency of rolls, respectively. This is the first time that the asymmetry and intermittency of rolls are shown from radar images of the ocean surface.