151 research outputs found
Training neural mapping schemes for satellite altimetry with simulation data
Satellite altimetry combined with data assimilation and optimal interpolation
schemes have deeply renewed our ability to monitor sea surface dynamics.
Recently, deep learning (DL) schemes have emerged as appealing solutions to
address space-time interpolation problems. The scarcity of real altimetry
dataset, in terms of space-time coverage of the sea surface, however impedes
the training of state-of-the-art neural schemes on real-world case-studies.
Here, we leverage both simulations of ocean dynamics and satellite altimeters
to train simulation-based neural mapping schemes for the sea surface height and
demonstrate their performance for real altimetry datasets. We analyze further
how the ocean simulation dataset used during the training phase impacts this
performance. This experimental analysis covers both the resolution from
eddy-present configurations to eddy-rich ones, forced simulations vs.
reanalyses using data assimilation and tide-free vs. tide-resolving
simulations. Our benchmarking framework focuses on a Gulf Stream region for a
realistic 5-altimeter constellation using NEMO ocean simulations and 4DVarNet
mapping schemes. All simulation-based 4DVarNets outperform the operational
observation-driven and reanalysis products, namely DUACS and GLORYS. The more
realistic the ocean simulation dataset used during the training phase, the
better the mapping. The best 4DVarNet mapping was trained from an eddy-rich and
tide-free simulation datasets. It improves the resolved longitudinal scale from
151 kilometers for DUACS and 241 kilometers for GLORYS to 98 kilometers and
reduces the root mean squared error (RMSE) by 23% and 61%. These results open
research avenues for new synergies between ocean modelling and ocean
observation using learning-based approaches
Scale-aware neural calibration for wide swath altimetry observations
Sea surface height (SSH) is a key geophysical parameter for monitoring and
studying meso-scale surface ocean dynamics. For several decades, the mapping of
SSH products at regional and global scales has relied on nadir satellite
altimeters, which provide one-dimensional-only along-track satellite
observations of the SSH. The Surface Water and Ocean Topography (SWOT) mission
deploys a new sensor that acquires for the first time wide-swath
two-dimensional observations of the SSH. This provides new means to observe the
ocean at previously unresolved spatial scales. A critical challenge for the
exploiting of SWOT data is the separation of the SSH from other signals present
in the observations. In this paper, we propose a novel learning-based approach
for this SWOT calibration problem. It benefits from calibrated nadir altimetry
products and a scale-space decomposition adapted to SWOT swath geometry and the
structure of the different processes in play. In a supervised setting, our
method reaches the state-of-the-art residual error of ~1.4cm while proposing a
correction on the entire spectral from 10km to 1000kComment: 8 pages, 7 figures, Preprin
A posteriori learning for quasi-geostrophic turbulence parametrization
The use of machine learning to build subgrid parametrizations for climate
models is receiving growing attention. State-of-the-art strategies address the
problem as a supervised learning task and optimize algorithms that predict
subgrid fluxes based on information from coarse resolution models. In practice,
training data are generated from higher resolution numerical simulations
transformed in order to mimic coarse resolution simulations. By essence, these
strategies optimize subgrid parametrizations to meet so-called criteria. But the actual purpose of a subgrid parametrization is to
obtain good performance in terms of metrics which imply
computing entire model trajectories. In this paper, we focus on the
representation of energy backscatter in two dimensional quasi-geostrophic
turbulence and compare parametrizations obtained with different learning
strategies at fixed computational complexity. We show that strategies based on
criteria yield parametrizations that tend to be unstable in
direct simulations and describe how subgrid parametrizations can alternatively
be trained end-to-end in order to meet criteria. We
illustrate that end-to-end learning strategies yield parametrizations that
outperform known empirical and data-driven schemes in terms of performance,
stability and ability to apply to different flow configurations. These results
support the relevance of differentiable programming paradigms for climate
models in the future.Comment: 36 pages, 14 figures, submitted to Journal of Advances in Modeling
Earth Systems (JAMES
Can we map the interannual variability of the whole upper Southern Ocean with the current database of hydrographic observations?
International audienceWith the advent of Argo floats, it now seems feasible to study the interannual variations of upper ocean hydrographic properties of the historically undersampled Southern Ocean. To do so, scattered hydrographic profiles often first need to be mapped. To investigate biases and errors associated both with the limited space-time distribution of the profiles and with the mapping methods, we colocate the mixed-layer depth (MLD) output from a state-of-the-art 1/12° DRAKKAR simulation onto the latitude, longitude, and date of actual in situ profiles from 2005 to 2014. We compare the results obtained after remapping using a nearest neighbor (NN) interpolation and an objective analysis (OA) with different spatiotemporal grid resolutions and decorrelation scales. NN is improved with a coarser resolution. OA performs best with low decorrelation scales, avoiding too strong a smoothing, but returns values over larger areas with large decorrelation scales and low temporal resolution, as more points are available. For all resolutions OA represents better the annual extreme values than NN. Both methods underestimate the seasonal cycle in MLD. MLD biases are lower than 10 m on average but can exceed 250 m locally in winter. We argue that current Argo data should not be mapped to infer decadal trends in MLD, as all methods are unable to reproduce existing trends without creating unrealistic extra ones. We also show that regions of the subtropical Atlantic, Indian, and Pacific Oceans, and the whole ice-covered Southern Ocean, still cannot be mapped even by the best method because of the lack of observational data
Stochastic variability of oceanic flows above topography anomalies
International audienceWe describe a stochastic variability mechanism which is genuinely internal to the ocean, i.e. not due to fluctuations in atmospheric forcing. % The key ingredient is the existence of closed contours of bottom topography surrounded by a stirring region of enhanced eddy activity. This configuration leads to the formation of a robust but highly variable vortex above the topography anomaly. The vortex dynamics integrates the white noise forcing of oceanic eddies into a red noise signal for the large scale volume transport of the vortex. The strong interannual fluctuations of the transport of the Zapiola anticyclone () in the Argentine basin are argued to be partly due to such eddy-driven stochastic variability, on the basis of a years long simulation of a comprehensive global ocean model run driven by a repeated-year forcing
The contribution of surface and submesoscale processes to turbulence in the open ocean surface boundary layer
The ocean surface boundary layer is a critical interface across which momentum, heat, and trace gases are exchanged between the oceans and atmosphere. Surface processes (winds, waves, and buoyancy forcing) are known to contribute significantly to fluxes within this layer. Recently, studies have suggested that submesoscale processes, which occur at small scales (0.1â10 km, hours to days) and therefore are not yet represented in most ocean models, may play critical roles in these turbulent exchanges. While observational support for such phenomena has been demonstrated in the vicinity of strong current systems and littoral regions, relatively few observations exist in the openâocean environment to warrant representation in Earth system models. We use novel observations and simulations to quantify the contributions of surface and submesoscale processes to turbulent kinetic energy (TKE) dissipation in the openâocean surface boundary layer. Our observations are derived from moorings in the North Atlantic, December 2012 to April 2013, and are complemented by atmospheric reanalysis. We develop a conceptual framework for dissipation rates due to surface and submesoscale processes. Using this framework and comparing with observed dissipation rates, we find that surface processes dominate TKE dissipation. A parameterization for symmetric instability is consistent with this result. We next employ simulations from an ocean frontâresolving model to reestablish that dissipation due to surface processes exceeds that of submesoscale processes by 1â2 orders of magnitude. Together, these results suggest submesoscale processes do not dramatically modify vertical TKE budgets, though such dynamics may be climatically important owing to their ability to remove energy from the ocean
C3PO: a Spontaneous and Ephemeral Social Networking Framework for a collaborative Creation and Publishing of Multimedia Contents
International audienceOnline social networks have been adopted by a large part of the population, and have become in few years essential communication means and a source of information for journalists. Nevertheless, these networks have some drawbacks that make people reluctant to use them, such as the impossibility to claim for ownership of data and to avoid commercial analysis of them, or the absence of collaborative tools to produce multimedia contents with a real editorial value. In this paper, we present a new kind of social networks, namely spontaneous and ephemeral social networks (SESNs). SESNs allow people to collaborate spontaneously in the production of multimedia documents so as to cover cultural and sport events
Challenges and Prospects in Ocean Circulation Models
We revisit the challenges and prospects for ocean circulation models following Griffies et al. (2010). Over the past decade, ocean circulation models evolved through improved understanding, numerics, spatial discretization, grid configurations, parameterizations, data assimilation, environmental monitoring, and process-level observations and modeling. Important large scale applications over the last decade are simulations of the Southern Ocean, the Meridional Overturning Circulation and its variability, and regional sea level change. Submesoscale variability is now routinely resolved in process models and permitted in a few global models, and submesoscale effects are parameterized in most global models. The scales where nonhydrostatic effects become important are beginning to be resolved in regional and process models. Coupling to sea ice, ice shelves, and high-resolution atmospheric models has stimulated new ideas and driven improvements in numerics. Observations have provided insight into turbulence and mixing around the globe and its consequences are assessed through perturbed physics models. Relatedly, parameterizations of the mixing and overturning processes in boundary layers and the ocean interior have improved. New diagnostics being used for evaluating models alongside present and novel observations are briefly referenced. The overall goal is summarizing new developments in ocean modeling, including: how new and existing observations can be used, what modeling challenges remain, and how simulations can be used to support observations.Peer reviewe
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