Search Results

You are looking at 11 - 20 of 21 items for

  • Author or Editor: Bertrand Chapron x
  • Refine by Access: All Content x
Clear All Modify Search
Nicolas Rascle
,
Frederic Nouguier
,
Bertrand Chapron
,
Alexis Mouche
, and
Aurélien Ponte

Abstract

At times, high-resolution images of sea surface roughness can provide stunning details of submesoscale upper-ocean dynamics. As interpreted, transformations of short-scale wind waves by horizontal current gradients are responsible for those spectacular observations. Those observations could prove particularly useful to validate numerical ocean models that reach increasingly high resolutions. Focusing on surface roughness at optical wavelengths, two steps have recently been performed in that direction. First, it was shown in a previous paper by Rascle et al. that surface roughness variations not only trace surface current divergence but also other characteristics of the current gradient tensor, mainly the strain in the wind direction. The wind direction with respect to the current gradient thus stands out as an important interpretative parameter. The second step is the purpose of the present paper, where the effect of the viewing direction is investigated. To this end, the authors discuss pairs of quasi-simultaneous sun-glitter images, taken from different satellite positions, to provide different viewing configurations, namely, quasi-orthogonal azimuth angles at similar zenith angles. As evidenced, upwind and crosswind viewing observations can be markedly different. As further confirmed with idealized numerical simulations, this anisotropy well traces anisotropic surface current areas, while more isotropic contrasts likely trace areas dominated by surface divergence conditions. These findings suggest the potential to directly separate divergence from other deformations by using high-resolution roughness observations at multiple azimuth view angles.

Full access
Nicolas Rascle
,
Bertrand Chapron
,
Aurélien Ponte
,
Fabrice Ardhuin
, and
Patrice Klein

Abstract

Images of sea surface roughness—for example, obtained by synthetic aperture radars (SAR) or by radiometers viewing areas in and around the sun glitter—at times provide clear observations of meso- and submesoscale oceanic features. Interacting with the surface wind waves, particular deformation properties of surface currents are responsible for those manifestations. Ignoring other sources of surface roughness variations, the authors limit their discussion to the mean square slope (mss) variability. This study confirms that vortical currents and currents with shear in the wind direction shall not be expressed in surface roughness images. Only divergent currents or currents with no divergence but strained in the wind direction can exhibit surface roughness signatures. More specifically, nondivergent currents might be traced with a 45° sensitivity to the wind direction. A simple method is proposed in order to interpret high-resolution roughness images, where roughness variations are proportional to ∂u/∂x + αυ/∂y, a linear combination of the along-wind and crosswind current gradients. The polarization parameter α depends upon the sensor look direction and the directional properties of the surface waves selected by the sensor. The use of multiple look directions or possible acquisitions with different wind directions shall thus help to retrieve surface currents from surface roughness observations.

Full access
Jérôme Gourrion
,
Tanguy Szekely
,
Rachel Killick
,
Breck Owens
,
Gilles Reverdin
, and
Bertrand Chapron

Abstract

Realistic ocean state prediction and its validation rely on the availability of high quality in situ observations. To detect data errors, adequate quality check procedures must be designed. This paper presents procedures that take advantage of the ever-growing observation databases that provide climatological knowledge of the ocean variability in the neighborhood of an observation location. Local validity intervals are used to estimate binarily whether the observed values are considered as good or erroneous. Whereas a classical approach estimates validity bounds from first- and second-order moments of the climatological parameter distribution, that is, mean and variance, this work proposes to infer them directly from minimum and maximum observed values. Such an approach avoids any assumption of the parameter distribution such as unimodality, symmetry around the mean, peakedness, or homogeneous distribution tail height relative to distribution peak. To reach adequate statistical robustness, an extensive manual quality control of the reference dataset is critical. Once the data have been quality checked, the local minima and maxima reference fields are derived and the method is compared with the classical mean/variance-based approach. Performance is assessed in terms of statistics of good and bad detections. It is shown that the present size of the reference datasets allows the parameter estimates to reach a satisfactory robustness level to always make the method more efficient than the classical one. As expected, insufficient robustness persists in areas with an especially low number of samples and high variability.

Free access
Clement Combot
,
Alexis Mouche
,
John Knaff
,
Yili Zhao
,
Yuan Zhao
,
Leo Vinour
,
Yves Quilfen
, and
Bertrand Chapron

Abstract

To produce more precise descriptions of air–sea exchanges under tropical cyclones (TCs), spaceborne synthetic aperture radar (SAR) instruments provide unique capabilities to probe the ocean surface conditions, at very high spatial resolution, and on synoptic scales. Using highly resolved (3 km) wind fields, an extensive database is constructed from RadarSat-2 and Sentinel-1 SAR acquisitions. Spanning 161 tropical cyclones, the database covers all TC intensity categories that have occurred in 5 different TC basins, and include 29 cases coincident with SFMR measurements. After locating the TC center, a specific methodology is applied to filter out areas contaminated by heavy precipitation to help extract, for each acquisition, the maximum wind speed (Vmax), its associated radius (Rmax), and corresponding outer wind radii (R34/50/64 kt). These parameters are then systematically compared with best track (BTK), and when available, SFMR airborne measurements. For collocated SFMR and SAR observations, comparisons yield root-mean-squares of 3.86 m s−1 and 3 km for ocean surface wind speeds and TC Rmax, respectively. High correlations remain for category-5 cases, with Vmax exceeding 60 m s−1. The largest discrepancies are found between BTK and SAR Rmax estimates, with Rmax fluctuations poorly captured by BTK, especially for rapidly evolving category-3, -4, and -5 TCs. In heavy precipitation (>35 mm h−1), the SAR C-band measurements may be impacted, with local ambiguities associated with rain features, as revealed by external rain measurements. Still, this large dataset demonstrates that SAR measurements have unique high-resolution capabilities, capturing the inner- and outer-core radial structure of the TC vortex, and provide independent and complementary measurements than those used for BTK estimates.

Free access
Aurelien L. Ponte
,
Patrice Klein
,
Xavier Capet
,
Pierre-Yves Le Traon
,
Bertrand Chapron
, and
Pascale Lherminier

Abstract

High-resolution numerical experiments of ocean mesoscale eddy turbulence show that the wind-driven mixed layer (ML) dynamics affects mesoscale motions in the surface layers at scales lower than O(60 km). At these scales, surface horizontal currents are still coherent to, but weaker than, those derived from sea surface height using geostrophy. Vertical motions, on the other hand, are stronger than those diagnosed using the adiabatic quasigeotrophic (QG) framework. An analytical model, based on a scaling analysis and on simple dynamical arguments, provides a physical understanding and leads to a parameterization of these features in terms of vertical mixing. These results are valid when the wind-driven velocity scale is much smaller than that associated with eddies and the Ekman number (related to the ratio between the Ekman and ML depth) is not small. This suggests that, in these specific situations, three-dimensional ML motions (including the vertical velocity) can be diagnosed from high-resolution satellite observations combined with a climatological knowledge of ML conditions and interior stratification.

Full access
Jamie D. Shutler
,
Peter E. Land
,
Jean-Francois Piolle
,
David K. Woolf
,
Lonneke Goddijn-Murphy
,
Frederic Paul
,
Fanny Girard-Ardhuin
,
Bertrand Chapron
, and
Craig J. Donlon

Abstract

The air–sea flux of greenhouse gases [e.g., carbon dioxide (CO2)] is a critical part of the climate system and a major factor in the biogeochemical development of the oceans. More accurate and higher-resolution calculations of these gas fluxes are required if researchers are to fully understand and predict future climate. Satellite Earth observation is able to provide large spatial-scale datasets that can be used to study gas fluxes. However, the large storage requirements needed to host such data can restrict its use by the scientific community. Fortunately, the development of cloud computing can provide a solution. This paper describes an open-source air–sea CO2 flux processing toolbox called the “FluxEngine,” designed for use on a cloud-computing infrastructure. The toolbox allows users to easily generate global and regional air–sea CO2 flux data from model, in situ, and Earth observation data, and its air–sea gas flux calculation is user configurable. Its current installation on the Nephalae Cloud allows users to easily exploit more than 8 TB of climate-quality Earth observation data for the derivation of gas fluxes. The resultant netCDF data output files contain >20 data layers containing the various stages of the flux calculation along with process indicator layers to aid interpretation of the data. This paper describes the toolbox design, which verifies the air–sea CO2 flux calculations; demonstrates the use of the tools for studying global and shelf sea air–sea fluxes; and describes future developments.

Full access
Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron

Abstract

Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.

Free access
Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron
Full access
Fabrice Ardhuin
,
Bertrand Chapron
,
Christophe Maes
,
Roland Romeiser
,
Christine Gommenginger
,
Sophie Cravatte
,
Rosemary Morrow
,
Craig Donlon
, and
Mark Bourassa
Open access
Arthur Avenas
,
Alexis Mouche
,
Pierre Tandeo
,
Jean-Francois Piolle
,
Dan Chavas
,
Ronan Fablet
,
John Knaff
, and
Bertrand Chapron

Abstract

The radius of maximum wind R max, an important parameter in tropical cyclone (TC) 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 semiempirical model relying on an outer wind radius, intensity, and latitude was fit to best-track data. In this study we revise this semiempirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, R max observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an intercalibrated 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 2010–20 and yields R max reanalyses and trends that are 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 R max when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes.

Open access