Keywords
 A3.1.8. Big data (production, storage, transfer)
 A3.4.1. Supervised learning
 A3.4.2. Unsupervised learning
 A3.4.5. Bayesian methods
 A3.4.7. Kernel methods
 A6.1.1. Continuous Modeling (PDE, ODE)
 A6.1.2. Stochastic Modeling
 A6.1.4. Multiscale modeling
 A6.1.5. Multiphysics modeling
 A6.2.1. Numerical analysis of PDE and ODE
 A6.2.4. Statistical methods
 A6.2.6. Optimization
 A6.2.7. High performance computing
 A6.3.1. Inverse problems
 A6.3.2. Data assimilation
 A6.3.4. Model reduction
 A6.3.5. Uncertainty Quantification
 A6.5.2. Fluid mechanics
 A6.5.4. Waves
 B3.2. Climate and meteorology
 B3.3.2. Water: sea & ocean, lake & river
 B3.3.4. Atmosphere
 B3.4.1. Natural risks
 B4.3.2. Hydroenergy
 B4.3.3. Wind energy
 B9.11.1. Environmental risks
1 Team members, visitors, external collaborators
Research Scientists
 Laurent Debreu [Team leader, Inria, Researcher, HDR]
 Eugene Kazantsev [Inria, Researcher]
 Florian Lemarié [Inria, Researcher]
 Gurvan Madec [CNRS, Researcher, HDR]
 Arthur Vidard [Inria, Researcher, HDR]
 Olivier Zahm [Inria, Researcher]
Faculty Members
 Elise Arnaud [Univ Grenoble Alpes, Associate Professor]
 Eric BlayoNogret [Univ Grenoble Alpes, Professor, HDR]
 Christine Kazantsev [Univ Grenoble Alpes, Associate Professor]
 FrançoisXavier Le Dimet [Univ Grenoble Alpes, Emeritus, until Sep 2020]
 Clémentine Prieur [Univ Grenoble Alpes, Professor, HDR]
PostDoctoral Fellows
 Matthieu Brachet [Inria, until Aug 2020]
 Anass El Aouni [Inria]
PhD Students
 Rishabh Bhatt [Inria]
 Simon Clement [Univ Grenoble Alpes]
 Clement Duhamel [Inria, from Oct 2020]
 Emilie Duval [Inria]
 Maria Belen Heredia Guzman [Institut national de recherche pour l'agriculture, l'alimentation et l'environnement, until Nov 2020]
 Adrien Hirvoas [IFPEN]
 Philomene Le Gall [Univ Grenoble Alpes]
 Long Long Li [Institut de technologie de Harbin  Chine, until March 2020]
 Arthur Macherey [École centrale de Nantes]
 Antoine Alexis Nasser [Inria]
 Emilie Rouzies [Institut national de recherche pour l'agriculture, l'alimentation et l'environnement]
 Sophie Thery [Univ Grenoble Alpes]
 Victor Trappler [Univ Grenoble Alpes]
Technical Staff
 Celine Acary Robert [Univ Grenoble Alpes, Engineer, from Dec 2020]
 Maurice Bremond [Inria, Engineer]
 Nicolas Ducousso [Inria, Engineer]
Interns and Apprentices
 Clement Duhamel [Inria, from Apr 2020 until Sep 2020]
 Manolis Perrot [École Normale Supérieure Paris, from Oct 2020]
 Camille Saint Martin [Inria, from Mar 2020 until Jul 2020]
 Sofiane Tanji [Univ Grenoble Alpes, from Feb 2020 until Jun 2020]
Administrative Assistant
 Annie Simon [Inria]
2 Overall objectives
The general scope of the AIRSEA projectteam is to develop mathematical and computational methods for the modeling of oceanic and atmospheric flows. The mathematical tools used involve both deterministic and statistical approaches. The main research topics cover a) modeling and coupling b) model reduction for sensitivity analysis, coupling and multiscale optimizations c) sensitivity analysis, parameter estimation and risk assessment d) algorithms for high performance computing. The range of application is from climate modeling to the prediction of extreme events.
3 Research program
Recent events have raised questions regarding the social and economic implications of anthropic alterations of the Earth system, i.e. climate change and the associated risks of increasing extreme events. Ocean and atmosphere, coupled with other components (continent and ice) are the building blocks of the Earth system. A better understanding of the ocean atmosphere system is a key ingredient for improving prediction of such events. Numerical models are essential tools to understand processes, and simulate and forecast events at various space and time scales. Geophysical flows generally have a number of characteristics that make it difficult to model them. This justifies the development of specifically adapted mathematical methods:
 Geophysical flows are strongly nonlinear. Therefore, they exhibit interactions between different scales, and unresolved small scales (smaller than mesh size) of the flows have to be parameterized in the equations.
 Geophysical fluids are non closed systems. They are openended in their scope for including and dynamically coupling different physical processes (e.g., atmosphere, ocean, continental water, etc). Coupling algorithms are thus of primary importance to account for potentially significant feedback.
 Numerical models contain parameters which cannot be estimated accurately either because they are difficult to measure or because they represent some poorly known subgrid phenomena. There is thus a need for dealing with uncertainties. This is further complicated by the turbulent nature of geophysical fluids.
 The computational cost of geophysical flow simulations is huge, thus requiring the use of reduced models, multiscale methods and the design of algorithms ready for high performance computing platforms.
Our scientific objectives are divided into four major points. The first objective focuses on developing advanced mathematical methods for both the ocean and atmosphere, and the coupling of these two components. The second objective is to investigate the derivation and use of model reduction to face problems associated with the numerical cost of our applications. The third objective is directed toward the management of uncertainty in numerical simulations. The last objective deals with efficient numerical algorithms for new computing platforms. As mentioned above, the targeted applications cover oceanic and atmospheric modeling and related extreme events using a hierarchy of models of increasing complexity.
3.1 Modeling for oceanic and atmospheric flows
Current numerical oceanic and atmospheric models suffer from a number of wellidentified problems. These problems are mainly related to lack of horizontal and vertical resolution, thus requiring the parameterization of unresolved (subgrid scale) processes and control of discretization errors in order to fulfill criteria related to the particular underlying physics of rotating and strongly stratified flows. Oceanic and atmospheric coupled models are increasingly used in a wide range of applications from global to regional scales. Assessment of the reliability of those coupled models is an emerging topic as the spread among the solutions of existing models (e.g., for climate change predictions) has not been reduced with the new generation models when compared to the older ones.
Advanced methods for modeling 3D rotating and stratified flows The continuous increase of computational power and the resulting finer grid resolutions have triggered a recent regain of interest in numerical methods and their relation to physical processes. Going beyond present knowledge requires a better understanding of numerical dispersion/dissipation ranges and their connection to model fine scales. Removing the leading order truncation error of numerical schemes is thus an active topic of research and each mathematical tool has to adapt to the characteristics of three dimensional stratified and rotating flows. Studying the link between discretization errors and subgrid scale parameterizations is also arguably one of the main challenges.
Complexity of the geometry, boundary layers, strong stratification and lack of resolution are the main sources of discretization errors in the numerical simulation of geophysical flows. This emphasizes the importance of the definition of the computational grids (and coordinate systems) both in horizontal and vertical directions, and the necessity of truly multi resolution approaches. At the same time, the role of the small scale dynamics on large scale circulation has to be taken into account. Such parameterizations may be of deterministic as well as stochastic nature and both approaches are taken by the AIRSEA team. The design of numerical schemes consistent with the parameterizations is also arguably one of the main challenges for the coming years. This work is complementary and linked to that on parameters estimation described in 3.3.
Ocean Atmosphere interactions and formulation of coupled models Stateoftheart climate models (CMs) are complex systems under continuous development. A fundamental aspect of climate modeling is the representation of airsea interactions. This covers a large range of issues: parameterizations of atmospheric and oceanic boundary layers, estimation of airsea fluxes, timespace numerical schemes, non conforming grids, coupling algorithms ...Many developments related to these different aspects were performed over the last 1015 years, but were in general conducted independently of each other.
The aim of our work is to revisit and enrich several aspects of the representation of airsea interactions in CMs, paying special attention to their overall consistency with appropriate mathematical tools. We intend to work consistently on the physics and numerics. Using the theoretical framework of globalintime Schwarz methods, our aim is to analyze the mathematical formulation of the parameterizations in a coupling perspective. From this study, we expect improved predictability in coupled models (this aspect will be studied using techniques described in 3.3). Complementary work on spacetime nonconformities and acceleration of convergence of Schwarzlike iterative methods (see 8.1.2) are also conducted.
3.2 Model reduction / multiscale algorithms
The high computational cost of the applications is a common and major concern to have in mind when deriving new methodological approaches. This cost increases dramatically with the use of sensitivity analysis or parameter estimation methods, and more generally with methods that require a potentially large number of model integrations.
A dimension reduction, using either stochastic or deterministic methods, is a way to reduce significantly the number of degrees of freedom, and therefore the calculation time, of a numerical model.
Model reduction Reduction methods can be deterministic (proper orthogonal decomposition, other reduced bases) or stochastic (polynomial chaos, Gaussian processes, kriging), and both fields of research are very active. Choosing one method over another strongly depends on the targeted application, which can be as varied as realtime computation, sensitivity analysis (see e.g., section 8.4) or optimisation for parameter estimation (see below).
Our goals are multiple, but they share a common need for certified error bounds on the output. Our team has a 4year history of working on certified reduction methods and has a unique positioning at the interface between deterministic and stochastic approaches. Thus, it seems interesting to conduct a thorough comparison of the two alternatives in the context of sensitivity analysis. Efforts will also be directed toward the development of efficient greedy algorithms for the reduction, and the derivation of goaloriented sharp error bounds for non linear models and/or non linear outputs of interest. This will be complementary to our work on the deterministic reduction of parametrized viscous Burgers and Shallow Water equations where the objective is to obtain sharp error bounds to provide confidence intervals for the estimation of sensitivity indices.
Reduced models for coupling applications Global and regional highresolution oceanic models are either coupled to an atmospheric model or forced at the airsea interface by fluxes computed empirically preventing proper physical feedback between the two media. Thanks to highresolution observational studies, the existence of airsea interactions at oceanic mesoscales (i.e., at $\mathcal{O}\left(1km\right)$ scales) have been unambiguously shown. Those interactions can be represented in coupled models only if the oceanic and atmospheric models are run on the same highresolution computational grid, and are absent in a forced mode. Fully coupled models at highresolution are seldom used because of their prohibitive computational cost. The derivation of a reduced model as an alternative between a forced mode and the use of a full atmospheric model is an open problem.
Multiphysics coupling often requires iterative methods to obtain a mathematically correct numerical solution. To mitigate the cost of the iterations, we will investigate the possibility of using reducedorder models for the iterative process. We will consider different ways of deriving a reduced model: coarsening of the resolution, degradation of the physics and/or numerical schemes, or simplification of the governing equations. At a mathematical level, we will strive to study the wellposedness and the convergence properties when reduced models are used. Indeed, running an atmospheric model at the same resolution as the ocean model is generally too expensive to be manageable, even for moderate resolution applications. To account for important finescale interactions in the computation of the airsea boundary condition, the objective is to derive a simplified boundary layer model that is able to represent important 3D turbulent features in the marine atmospheric boundary layer.
Reduced models for multiscale optimization The field of multigrid methods for optimisation has known a tremendous development over the past few decades. However, it has not been applied to oceanic and atmospheric problems apart from some crude (nonconverging) approximations or applications to simplified and low dimensional models. This is mainly due to the high complexity of such models and to the difficulty in handling several grids at the same time. Moreover, due to complex boundaries and physical phenomena, the grid interactions and transfer operators are not trivial to define.
Multigrid solvers (or multigrid preconditioners) are efficient methods for the solution of variational data assimilation problems. We would like to take advantage of these methods to tackle the optimization problem in high dimensional space. High dimensional control space is obtained when dealing with parameter fields estimation, or with control of the full 4D (space time) trajectory. It is important since it enables us to take into account model errors. In that case, multigrid methods can be used to solve the large scales of the problem at a lower cost, this being potentially coupled with a scale decomposition of the variables themselves.
3.3 Dealing with uncertainties
There are many sources of uncertainties in numerical models. They are due to imperfect external forcing, poorly known parameters, missing physics and discretization errors. Studying these uncertainties and their impact on the simulations is a challenge, mostly because of the high dimensionality and nonlinear nature of the systems. To deal with these uncertainties we work on three axes of research, which are linked: sensitivity analysis, parameter estimation and risk assessment. They are based on either stochastic or deterministic methods.
Sensitivity analysis Sensitivity analysis (SA), which links uncertainty in the model inputs to uncertainty in the model outputs, is a powerful tool for model design and validation. First, it can be a prestage for parameter estimation (see 3.3), allowing for the selection of the more significant parameters. Second, SA permits understanding and quantifying (possibly nonlinear) interactions induced by the different processes defining e.g., realistic ocean atmosphere models. Finally SA allows for validation of models, checking that the estimated sensitivities are consistent with what is expected by the theory. On ocean, atmosphere and coupled systems, only first order deterministic SA are performed, neglecting the initialization process (data assimilation). AIRSEA members and collaborators proposed to use second order information to provide consistent sensitivity measures, but so far it has only been applied to simple academic systems. Metamodels are now commonly used, due to the cost induced by each evaluation of complex numerical models: mostly Gaussian processes, whose probabilistic framework allows for the development of specific adaptive designs, and polynomial chaos not only in the context of intrusive Galerkin approaches but also in a blackbox approach. Until recently, global SA was based primarily on a set of engineering practices. New mathematical and methodological developments have led to the numerical computation of Sobol' indices, with confidence intervals assessing for both metamodel and estimation errors. Approaches have also been extended to the case of dependent entries, functional inputs and/or output and stochastic numerical codes. Other types of indices and generalizations of Sobol' indices have also been introduced.
Concerning the stochastic approach to SA we plan to work with parameters that show spatiotemporal dependencies and to continue toward more realistic applications where the input space is of huge dimension with highly correlated components. Sensitivity analysis for dependent inputs also introduces new challenges. In our applicative context, it would seem prudent to carefully learn the spatiotemporal dependences before running a global SA. In the deterministic framework we focus on second order approaches where the sought sensitivities are related to the optimality system rather than to the model; i.e., we consider the whole forecasting system (model plus initialization through data assimilation).
All these methods allow for computing sensitivities and more importantly a posteriori error statistics.
Parameter estimation Advanced parameter estimation methods are barely used in ocean, atmosphere and coupled systems, mostly due to a difficulty of deriving adequate response functions, a lack of knowledge of these methods in the oceanatmosphere community, and also to the huge associated computing costs. In the presence of strong uncertainties on the model but also on parameter values, simulation and inference are closely associated. Filtering for data assimilation and Approximate Bayesian Computation (ABC) are two examples of such association.
Stochastic approach can be compared with the deterministic approach, which allows to determine the sensitivity of the flow to parameters and optimize their values relying on data assimilation. This approach is already shown to be capable of selecting a reduced space of the most influent parameters in the local parameter space and to adapt their values in view of correcting errors committed by the numerical approximation. This approach assumes the use of automatic differentiation of the source code with respect to the model parameters, and optimization of the obtained raw code.
AIRSEA assembles all the required expertise to tackle these difficulties. As mentioned previously, the choice of parameterization schemes and their tuning has a significant impact on the result of model simulations. Our research will focus on parameter estimation for parameterized Partial Differential Equations (PDEs) and also for parameterized Stochastic Differential Equations (SDEs). Deterministic approaches are based on optimal control methods and are local in the parameter space (i.e., the result depends on the starting point of the estimation) but thanks to adjoint methods they can cope with a large number of unknowns that can also vary in space and time. Multiscale optimization techniques as described in 8.3 will be one of the tools used. This in turn can be used either to propose a better (and smaller) parameter set or as a criterion for discriminating parameterization schemes. Statistical methods are global in the parameter state but may suffer from the curse of dimensionality. However, the notion of parameter can also be extended to functional parameters. We may consider as parameter a functional entity such as a boundary condition on time, or a probability density function in a stationary regime. For these purposes, nonparametric estimation will also be considered as an alternative.
Risk assessment Risk assessment in the multivariate setting suffers from a lack of consensus on the choice of indicators. Moreover, once the indicators are designed, it still remains to develop estimation procedures, efficient even for high risk levels. Recent developments for the assessment of financial risk have to be considered with caution as methods may differ pertaining to general financial decisions or environmental risk assessment. Modeling and quantifying uncertainties related to extreme events is of central interest in environmental sciences. In relation to our scientific targets, risk assessment is very important in several areas: hydrological extreme events, cyclone intensity, storm surges...Environmental risks most of the time involve several aspects which are often correlated. Moreover, even in the ideal case where the focus is on a single risk source, we have to face the temporal and spatial nature of environmental extreme events. The study of extremes within a spatiotemporal framework remains an emerging field where the development of adapted statistical methods could lead to major progress in terms of geophysical understanding and risk assessment thus coupling data and model information for risk assessment.
Based on the above considerations we aim to answer the following scientific questions: how to measure risk in a multivariate/spatial framework? How to estimate risk in a non stationary context? How to reduce dimension (see 3.2) for a better estimation of spatial risk?
Extreme events are rare, which means there is little data available to make inferences of risk measures. Risk assessment based on observation therefore relies on multivariate extreme value theory. Interacting particle systems for the analysis of rare events is commonly used in the community of computer experiments. An open question is the pertinence of such tools for the evaluation of environmental risk.
Most numerical models are unable to accurately reproduce extreme events. There is therefore a real need to develop efficient assimilation methods for the coupling of numerical models and extreme data.
3.4 High performance computing
Methods for sensitivity analysis, parameter estimation and risk assessment are extremely costly due to the necessary number of model evaluations. This number of simulations require considerable computational resources, depends on the complexity of the application, the number of input variables and desired quality of approximations. To this aim, the AIRSEA team is an intensive user of HPC computing platforms, particularly grid computing platforms. The associated grid deployment has to take into account the scheduling of a huge number of computational requests and the links with datamanagement between these requests, all of these as automatically as possible. In addition, there is an increasing need to propose efficient numerical algorithms specifically designed for new (or future) computing architectures and this is part of our scientific objectives. According to the computational cost of our applications, the evolution of high performance computing platforms has to be taken into account for several reasons. While our applications are able to exploit space parallelism to its full extent (oceanic and atmospheric models are traditionally based on a spatial domain decomposition method), the spatial discretization step size limits the efficiency of traditional parallel methods. Thus the inherent parallelism is modest, particularly for the case of relative coarse resolution but with very long integration time (e.g., climate modeling). Paths toward new programming paradigms are thus needed. As a step in that direction, we plan to focus our research on parallel in time methods.
New numerical algorithms for high performance computing Parallel in time methods can be classified into three main groups. In the first group, we find methods using parallelism across the method, such as parallel integrators for ordinary differential equations. The second group considers parallelism across the problem. Falling into this category are methods such as waveform relaxation where the spacetime system is decomposed into a set of subsystems which can then be solved independently using some form of relaxation techniques or multigrid reduction in time. The third group of methods focuses on parallelism across the steps. One of the best known algorithms in this family is parareal. Other methods combining the strengths of those listed above (e.g., PFASST) are currently under investigation in the community.
Parallel in time methods are iterative methods that may require a large number of iteration before convergence. Our first focus will be on the convergence analysis of parallel in time (Parareal / Schwarz) methods for the equation systems of oceanic and atmospheric models. Our second objective will be on the construction of fast (approximate) integrators for these systems. This part is naturally linked to the model reduction methods of section (8.3.1). Fast approximate integrators are required both in the Schwarz algorithm (where a first guess of the boundary conditions is required) and in the Parareal algorithm (where the fast integrator is used to connect the different time windows). Our main application of these methods will be on climate (i.e., very long time) simulations. Our second application of parallel in time methods will be in the context of optimization methods. In fact, one of the major drawbacks of the optimal control techniques used in 3.3 is a lack of intrinsic parallelism in comparison with ensemble methods. Here, parallel in time methods also offer ways to better efficiency. The mathematical key point is centered on how to efficiently couple two iterative methods (i.e., parallel in time and optimization methods).
4 Application domains
4.1 The OceanAtmosphere System
The evolution of natural systems, in the short, mid, or long term, has extremely important consequences for both the global Earth system and humanity. Forecasting this evolution is thus a major challenge from the scientific, economic, and human viewpoints.
Humanity has to face the problem of global warming, brought on by the emission of greenhouse gases from human activities. This warming will probably cause huge changes at global and regional scales, in terms of climate, vegetation and biodiversity, with major consequences for local populations. Research has therefore been conducted over the past 15 to 20 years in an effort to model the Earth's climate and forecast its evolution in the 21st century in response to anthropic action.
With regard to shortterm forecasts, the best and oldest example is of course weather forecasting. Meteorological services have been providing daily shortterm forecasts for several decades which are of crucial importance for numerous human activities.
Numerous other problems can also be mentioned, like seasonal weather forecasting (to enable powerful phenomena like an El Ni$\tilde{\text{n}}$o event or a drought period to be anticipated a few months in advance), operational oceanography (shortterm forecasts of the evolution of the ocean system to provide services for the fishing industry, ship routing, defense, or the fight against marine pollution) or the prediction of floods.
As mentioned previously, mathematical and numerical tools are omnipresent and play a fundamental role in these areas of research. In this context, the vocation of AIRSEA is not to carry out numerical prediction, but to address mathematical issues raised by the development of prediction systems for these application fields, in close collaboration with geophysicists.
5 Social and environmental responsibility
5.1 Impact of research results
Most of the research activities of the AIRSEA team are directed towards the improvement of numerical systems of the ocean and the atmosphere. This includes the development of appropriated numerical methods, model/parameter calibration using observational data and uncertainty quantification for decision making. The AIRSEA team members work in close collaboration with the researchers in the field of geophyscial fluid and are partners of several interdisciplinary projects. They also strongly contribute to the development of state of the art numerical systems, like NEMO and CROCO in the ocean community.
6 Highlights of the year
 The paper 29 was selected for an oral presentation at the NeurIPS2020 conference (top 1% of the submitted papers)
6.1 Awards
 The association "La Grange des maths" directed by Christine Kazantsev received the Jacqueline Ferrand Award, decerned by the French Mathematical Society.
7 New software and platforms
7.1 New software
7.1.1 AGRIF
 Name: Adaptive Grid Refinement In Fortran
 Keyword: Mesh refinement
 Scientific Description: AGRIF is a Fortran 90 package for the integration of full adaptive mesh refinement (AMR) features within a multidimensional finite difference model written in Fortran. Its main objective is to simplify the integration of AMR potentialities within an existing model with minimal changes. Capabilities of this package include the management of an arbitrary number of grids, horizontal and/or vertical refinements, dynamic regridding, parallelization of the grids interactions on distributed memory computers. AGRIF requires the model to be discretized on a structured grid, like it is typically done in ocean or atmosphere modelling.
 Functional Description: AGRIF is a Fortran 90 package for the integration of full adaptive mesh refinement (AMR) features within a multidimensional finite difference model written in Fortran. Its main objective is to simplify the integration of AMR potentialities within an existing model with minimal changes. Capabilities of this package include the management of an arbitrary number of grids, horizontal and/or vertical refinements, dynamic regridding, parallelization of the grids interactions on distributed memory computers. AGRIF requires the model to be discretized on a structured grid, like it is typically done in ocean or atmosphere modelling.
 News of the Year: In 2019, a new contract has been signed with CMEMS (Copernicus Marine Environment Moniroting Service) in order to extent the multiresolution capabilities of the AGRIF and its integration into the NEMO ocean system.

URL:
https://
gitlab. inria. fr/ ldebreu/ agrif  Publications: tel01546328, hal00387435
 Contact: Laurent Debreu
 Participants: Roland Patoum, Laurent Debreu
7.1.2 BALAISE
 Name: Bilbliothèque d’Assimilation Lagrangienne Adaptée aux Images Séquencées en Environnement
 Keywords: Multiscale analysis, Data assimilation, Optimal control
 Functional Description: BALAISE (Bilbliothèque d’Assimilation Lagrangienne Adaptée aux Images Séquencées en Environnement) is a test bed for image data assimilation. It includes a shallow water model, a multiscale decomposition library and an assimilation suite.
 Contact: Arthur Vidard
7.1.3 NEMOVAR
 Name: Variational data assimilation for NEMO
 Keywords: Oceanography, Data assimilation, Adjoint method, Optimal control
 Functional Description: NEMOVAR is a stateoftheart multiincremental variational data assimilation system with both 3D and 4D var capabilities, and which is designed to work with NEMO on the native ORCA grids. The background error covariance matrix is modelled using balance operators for the multivariate component and a diffusion operator for the univariate component. It can also be formulated as a linear combination of covariance models to take into account multiple correlation length scales associated with ocean variability on different scales. NEMOVAR has recently been enhanced with the addition of ensemble data assimilation and multigrid assimilation capabilities. It is used operationnaly in both ECMWF and the Met Office (UK)
 Contact: Arthur Vidard
 Partners: CERFACS, ECMWF, Met Office
7.1.4 Sensitivity
 Functional Description: This package is useful for conducting sensitivity analysis of complex computer codes.

URL:
https://
cran. rproject. org/ web/ packages/ sensitivity/ index. html  Contact: Laurent Gilquin
8 New results
8.1 Modeling for Oceanic and Atmospheric flows
8.1.1 Numerical Schemes for Ocean Modelling
Participants: Eric Blayo, Matthieu Brachet, Laurent Debreu, Nicolas Ducousso, Emilie Duval, Florian Lemarié, Gurvan Madec, Antoine Nasser.
With the increase of resolution, the hydrostatic assumption becomes less valid and the AIRSEA group also works on the development of nonhydrostatic ocean models. The treatment of nonhydrostatic incompressible flows leads to a 3D elliptic system for pressure that can be ill conditioned in particular with non geopotential vertical coordinates. That is why we favour the use of the nonhydrostatic compressible equations that removes the need for a 3D resolution at the price of reincluding acoustic waves 17.
A comparison between 2D and 3D simulations of non hydrostatic surface waves has been performed in 44.
In addition, Emilie Duval started her PhD in September 2018 on the coupling between the hydrostatic incompressible and nonhydrostatic compressible equations. A detailed analysis of acousticgravity waves in a freesurface compressible and stratified ocean is presented in 35.
Accurate and stable implementation of bathymetry boundary conditions in ocean models remains a challenging problem. The dynamics of ocean flow often depend sensitively on satisfying bathymetry boundary conditions and correctly representing their complex geometry. Generalized (e.g. ) terrainfollowing coordinates are often used in ocean models, but they require smoothing the bathymetry to reduce pressure gradient errors. Geopotential coordinates are a common alternative that avoid pressure gradient and numerical diapycnal diffusion errors, but they generate spurious flow due to their “staircase” geometry. In 9, we introduce a new Brinkman volume penalization to approximate the noslip boundary condition and complex geometry of bathymetry in ocean models. This approach corrects the staircase effect of coordinates, does not introduce any new stability constraints on the geometry of the bathymetry and is easy to implement in an existing ocean model. The porosity parameter allows modelling subgrid scale details of the geometry. We illustrate the penalization and confirm its accuracy by applying it to three standard test flows: upwelling over a sloping bottom, resting state over a seamount and internal tides over highly peaked bathymetry features. Figure (1) shows strong improvements obtained when the penalization method is used in comparison with traditional terrain following $\sigma $ simulations. At 6 km resolution, the penalization methods (Figure (1) d)), that takes into account details of bathymetry, allows to recover internal tide wave beams closed to the 3 km simulation. (Figure (1) a)). Following this work, Antoine Nasser started his PhD in October 2019 on penalization methods. One of the objectives is to study the representation of overflows in global ocean model. The NEMO ocean model will be used for the applications.
In the context of the H2020 IMMERSE project, the AIRSEA team is in charge of the development of a new time stepping strategy for the NEMO ocean model. Nicolas Ducousso works particularly on the design of Runge Kutta schemes for ocean models, taking into account the splitting between barotropic and baroclinic models. The team also studies the use of exponential time integrators (40) for ocean models.
8.1.2 Coupling Methods for Oceanic and Atmospheric Models and Representation of the AirSea Interface
Participants: Eric Blayo, Florian Lemarié, Sophie Thery, Simon Clément, Manolis Perot.
The Airsea team is involved in the modeling and algorithmic aspects of oceanatmosphere (OA) coupling. For the last few years we have been actively working on the analysis of such coupling both in terms of its continuous and numerical formulation (see 61 for an overview). Our activities can be divided into four general topics
Continuous and discrete analysis of Schwarz algorithms for OA coupling: we have been developing coupling approaches for several years, based on socalled Schwarz algorithms. Schwarzlike domain decomposition methods are very popular in mathematics, computational sciences and engineering notably for the implementation of coupling strategies. However, for complex applications (like in OA coupling) it is challenging to have an a priori knowledge of the convergence properties of such methods. Indeed coupled problems arising in Earth system modeling often exhibit sharp turbulent boundary layers whose parameterizations lead to peculiar transmission conditions and diffusion coefficients. In the framework of S. Thery PhD (defended in February 2021) the wellposedness of the nonlinear coupling problem including parameterizations has been addressed and a detailed continuous analysis of the convergence properties of the Schwarz methods has been pursued to entangle the impact of the different parameters at play in such coupling problem 45. During the first year of C. Simon PhD, a general framework has been proposed to study the convergence properties at a (semi)discrete level to allow a systematic comparison with the results obtained from the continuous problem.
Within the COCOA project, a Schwarzlike iterative method has been applied in a stateoftheart EarthSystem model to evaluate the consequences of inaccuracies in the usual adhoc oceanatmosphere coupling algorithms used in realistic models 20. Numerical results obtained with an iterative process show large differences at sunrise and sunset compared to usual adhoc algorithms thus showing that synchrony errors inherent to adhoc coupling methods can be significant.

Representation of the airsea interface in coupled models: During the PhDthesis of Charles Pelletier the scope was on including the formulation of physical parameterizations in the theoretical analysis of the coupling, in particular the parameterization schemes to compute airsea fluxes. Following this work, a novel and rigorous framework for a consistent twosided modeling of the surface boundary layer has been proposed 22. This framework allows for a more general representation of the vertical physics at the airsea interface while improving the mathematical regularity of the numerical solutions. Moreover, it is flexible enough to include additional physical parameters for example to account for the effect of surface waves in the turbulent flux computation. This work is the first step toward more adequate discretization methods for the parameterization of surface and planetary boundary layers in climate models 3162 (PhDthesis of C. Simon). The problem of interest takes the form of an nonstationary nonlinear parabolic equation. The objective is to derive a discretization for which we could prove nonlinear stability criteria and show robustness to large variations in parabolic Courant number while being consistent with our knowledge of the underlying physical principles (e.g. the MoninObukhov theory in the surface layer).This work will be carried out in the framework of a project funded by SHOM (contract 19CP07).
Lately, in the framework of the M. Perrot internship, we have been working in collaboration with Etienne Mémin (Fluminance project team) to investigate the possibility to derive a stochastic representation of the atmospheric surface layer dynamics and thermodynamics based on a modeling under location uncertainty. Efforts in this direction will be pursued.
 A simplified atmospheric boundary layer model for oceanic purposes: Part of our activities within the IMMERSE project is dedicated to the development of a simplified model of the marine atmospheric boundary layer (called ABL1d) of intermediate complexity between a bulk parameterization and a full threedimensional atmospheric model and to its integration to the NEMO general circulation model 19. A constraint in the conception of such a simplified model is to allow an apt representation of the downward momentum mixing mechanism and partial reenergization of the ocean by the atmosphere while keeping the computational efficiency and flexibility inherent to ocean only modeling. First realistic applications of the coupled NEMOABL1d modeling system have been carried out 6.
 Analysis and improvement of airseawave interactions in realistic simulations: part of our activity has been in collaboration with atmosphericists and physical oceanographers to study the impact on some modeling assumptions in realistic oceanatmosphere coupled simulations 13, 70, 65. At a more fundamental level, in collaboration with A. Wirth, we have studied turbulent fluctuations in a coupled Ekman layer problem with randomized drag coefficient 27. Moreover, within the ALBATROS project 63, we have contributed to the development of a 2way coupling between an ocean global circulation model (NEMO) with a surface wave model (WW3). Such coupling is not straightforward to implement since it requires modifications of the governing equations, boundary conditions and subgrid scale closures in the oceanic model 8.
These topics are addressed through strong collaborations between the applied mathematicians and the climate and operational community (MeteoFrance, Ifremer, SHOM, MercatorOcean, LMD, and LOCEAN). Our work on oceanatmosphere coupling has steadily matured over the last few years and has reached a point where it triggered interest from the physicists. Through the funding of the the projects ANR COCOA (started in January 2017, PI: E. Blayo) and SHOM 19CP07 (started in January 2020, PI: F. Lemarié), Airsea team members play a major role in the structuration of a multidisciplinary scientific community working on oceanatmosphere coupling spanning a broad range from mathematical theory to practical implementations in climate and operational models. An expected outcome of those projects should be the design of a benchmark suite of idealized coupled test cases representative of known issues in coupled models. Such idealized test cases should motivate further collaborations at an international level. In this context, a singlecolumn version of the CNRM climate models has been designed and several coupling algorithms have been implemented (work done by S. Valcke, CERFACS). This model will be used to illustrate the relevance of our theoretical work in a semirealistic context.
8.1.3 Nonhydrostatic Modeling
Participant: Eric Blayo, Laurent Debreu, Emilie Duval.
In the context of the French initiative CROCO (Coastal and Regional Ocean COmmunity model, https://
8.1.4 Machine learning for reconstruction of model parameters.
Participants: Laurent Debreu, Eugene Kazantsev, Arthur Vidard, Olivier Zahm.
Artificial intelligence and machine learning may be considered as a potential way to address unresolved model scales and to approximate poorly known processes such as dissipation that occurs essentially at small scales. In order to understand the possibility to combine numerical model and neural network learned with the aid of external data, we develop a network generation and learning algorithm and use it to approximate nonlinear model operators. Beginning with a simple nonlinear equations like transportdiffusion and Burgers ones, we use artificially generated external data to learn the network by Adam algorithm 59. Results show the possibility to approximate nonlinear, and even discontinuous dissipation operator with a quite good accuracy, however, several millions iterations are necessary to learn. Another potential way to reconstruct subgrid scales consists in application the Image SuperResolution methods that refer to the process of recovering highresolution images from lowresolution image in computer vision and image processing. Recent years have shown remarkable progress of image superresolution using machine learning techniques 73. We try to use this methodology in order to identify fine structure of the chaotic turbulent solution of a simple barotropic ocean model. After the learning the flow patterns obtained by the high resolution model, the neuron net can identify fine structure in the lowresolution model solution with better precision than bicubic interpolation.8.2 Assimilation of spatially dense observations
Participant: Elise Arnaud, FrançoisXavier Le Dimet, Arthur Vidard, Emilie Rouzies.
8.2.1 Direct assimilation of image sequences
At the present time the observation of Earth from space is done by more than thirty satellites. These platforms provide two kinds of observational information:
 Eulerian information as radiance measurements: the radiative properties of the earth and its fluid envelops. These data can be plugged into numerical models by solving some inverse problems.
 Lagrangian information: the movement of fronts and vortices give information on the dynamics of the fluid. Presently this information is scarcely used in meteorology by following small cumulus clouds and using them as Lagrangian tracers, but the selection of these clouds must be done by hand and the altitude of the selected clouds must be known. This is done by using the temperature of the top of the cloud.
Our current developments are targeted at the use of learning methods methods to describe the evolution of the images. This approach is being applied to the tracking of oceanic oil spills in the framework of a Long Li's Phd in cosupervision with Jianwei Ma.
8.2.2 Observation error representation
Accounting for realistic observations errors is a known bottleneck in data assimilation, because dealing with error correlations is complex. Following a previous study on this subject, we propose to use multiscale modelling, more precisely wavelet transform, to address this question. In 7 we investigate the problem further by addressing two issues arising in reallife data assimilation: how to deal with partially missing data (e.g., concealed by an obstacle between the sensor and the observed system); how to solve convergence issues associated to complex observation error covariance matrices? Two adjustments relying on wavelets modelling are proposed to deal with those, and offer significant improvements. The first one consists in adjusting the variance coefficients in the frequency domain to account for masked information. The second one consists in a gradual assimilation of frequencies. Both of these fully rely on the multiscale properties associated with wavelet covariance modelling.
A collaborative project started with C. Lauvernet (IRSTEA) in order to make use of this kind of assimilation strategies on the control of pesticide transfer and it led to the co supervision of E. Rouzies PhD, started in Dec 2019.
8.3 Model reduction / multiscale algorithms
8.3.1 Parameter space dimension reduction and Model order reduction
Participants: Mohamed Reda El Amri, Arthur Macherey, Youssef Marzouk, Clémentine Prieur, Alessio Spantini, Ricardo Baptista, Daniele Bigoni, Olivier Zahm.
Numerical models describing the evolution of the system (ocean + atmosphere) contain a large number of parameters which are generally poorly known. The reliability of the numerical simulations strongly depends on the identification and calibration of these parameters from observed data. In this context, it seems important to understand the kinds of lowdimensional structure that may be present in geophysical models and to exploit this lowdimensional structure with appropriate algorithms. We focus in the team, on parameter space dimension reduction techniques, lowrank structures and transport maps techniques for probability measure approximation.
In 29, we propose a framework for the greedy approximation of highdimensional Bayesian inference problems, through the composition of multiple lowdimensional transport maps or flows. Our framework operates recursively on a sequence of “residual” distributions, given by pulling back the posterior through the previously computed transport maps. The action of each map is confined to a lowdimensional subspace that we identify by minimizing an error bound. At each step, our approach thus identifies (i) a relevant subspace of the residual distribution, and (ii) a lowdimensional transformation between a restriction of the residual onto this subspace and a standard Gaussian. We prove weak convergence of the approach to the posterior distribution, and we demonstrate the algorithm on a range of challenging inference problems in differential equations and spatial statistics.
Identifying a lowdimensional informed parameter subspace offers a viable path to alleviating the dimensionality challenge in the sampledbased solution to largescale Bayesian inverse problems. The article 41 introduces a novel gradientbased dimension reduction method in which the informed subspace does not depend on the data. This permits onlineoffline computational strategy where the expensive lowdimensional structure of the problem is detected in an offline phase, meaning before observing the data. This strategy is particularly relevant for multiple inversion problems as the same informed subspace can be reused. The proposed approach allows to control the approximation error (in expectation over the data) of the posterior distribution. We also present sampling strategies which exploit the informed subspace to draw efficiently samples from the exact posterior distribution. The method is successfully illustrated on two numerical examples: a PDEbased inverse problem and a tomography problem with Poisson data.
In 36 we propose to robustly characterize joint and conditional probability distributions via transport maps. Transport maps or "flows" deterministically couple two distributions via an expressive monotone transformation. Yet, learning the parameters of such transformations in high dimensions is challenging given few samples from the unknown target distribution, and structural choices for these transformations can have a significant impact on performance. Here we formulate a systematic framework for representing and learning monotone maps, via invertible transformations of smooth functions, and demonstrate that the associated minimization problem has a unique global optimum. Given a hierarchical basis for the appropriate function space, we propose a sampleefficient adaptive algorithm that estimates a sparse approximation for the map. We demonstrate how this framework can learn densities with stable generalization performance across a wide range of sample sizes on realworld datasets.
In 38 we introduce a method for the nonlinear dimension reduction of a highdimensional function $u:{\mathbb{R}}^{d}\to \mathbb{R}$, $d\gg 1$. Our objective is to identify a nonlinear feature map $g:{\mathbb{R}}^{d}\to {\mathbb{R}}^{m}$, with a prescribed intermediate dimension $m\ll d$, so that $u$ can be well approximated by $f\circ g$ for some profile function $f:{\mathbb{R}}^{m}\to \mathbb{R}$. We propose to build the feature map by aligning the Jacobian $\nabla g$ with the gradient $\nabla u$, and we theoretically analyze the properties of the resulting $g$. Once $g$ is built, we construct $f$ by solving a gradientenhanced least squares problem. Our practical algorithm makes use of a sample ${\{{x}^{\left(i\right)},u\left({x}^{\left(i\right)}\right),\nabla u\left({x}^{\left(i\right)}\right)\}}_{i=1}^{N}$ and builds both $g$ and $f$ on adaptive downwardclosed polynomial spaces, using cross validation to avoid overfitting. We numerically evaluate the performance of our algorithm across different benchmarks, and explore the impact of the intermediate dimension $m$. We show that building a nonlinear feature map $g$ can permit more accurate approximation of $u$ than a linear $g$, for the same input data set.
The paper 43 is concerned with minimizing a sum of rational functions over a compact set of highdimension. This work is motivated by the paper 38 in which one needs to optimze the sum of Rayleigh quotients. The proposed approach relies on the second Lasserre's hierarchy (also known as the upper bounds hierarchy) formulated on the pushforward measure in order to work in a space of smaller dimension. We show that in the general case the minimum can be approximated as closely as desired from above with a hierarchy of semidefinite programs problems or, in the particular case of a single fraction, with a hierarchy of generalized eigenvalue problems. We numerically illustrate the potential of using the pushforward measure rather than the standard upper bounds hierarchy. In our opinion, this potential should be a strong incentive to investigate a related challenging problem interesting in its own; namely integrating an arbitrary power of a given polynomial on a simple set (e.g., unit box or unit sphere) with respect to Lebesgue or Haar measure.
In the framework of Arthur Macherey’s PhD, we have proposed algorithms for solving highdimensional Partial Differential Equations (PDEs) that combine a probabilistic interpretation of PDEs, through FeynmanKac representation, with sparse interpolation 49. MonteCarlo methods and timeintegration schemes are used to estimate pointwise evaluations of the solution of a PDE. We use a sequential control variates algorithm, where control variates are constructed based on successive approximations of the solution of the PDE. We are now interested in solving parametrized PDE with stochastic algorithms in the framework of potentially high dimensional parameter space. A preliminary step was the development of a PAC algorithm in relative precision for bandit problem with costly sampling 39.
Reduced models are also developed In the framework of robust inversion. In 55, we have combined a new greedy algorithm for functional quantization with a Stepwise Uncertainty Reduction strategy to solve a robust inversion problem under functional uncertainties. In a more recent work, we further reduced the number of simulations required to solve the same robust inversion problem, based on Gaussian process metamodeling on the joint input space of deterministic control parameters and functional uncertain variable 34. These results are applied to automotive depollution. This research axis was conducted in the framework of the Chair OQUAIDO.
8.4 Sensitivity analysis
Participants: Elise Arnaud, Eric Blayo, Laurent Gilquin, Maria Belén Heredia, Adrien Hirvoas, FrançoisXavier Le Dimet, Henri Mermoz Kouye, Clémentine Prieur, Laurence Viry.
Scientific context
Forecasting geophysical systems require complex models, which sometimes need to be coupled, and which make use of data assimilation. The objective of this project is, for a given output of such a system, to identify the most influential parameters, and to evaluate the effect of uncertainty in input parameters on model output. Existing stochastic tools are not well suited for high dimension problems (in particular timedependent problems), while deterministic tools are fully applicable but only provide limited information. So the challenge is to gather expertise on one hand on numerical approximation and control of Partial Differential Equations, and on the other hand on stochastic methods for sensitivity analysis, in order to develop and design innovative stochastic solutions to study high dimension models and to propose new hybrid approaches combining the stochastic and deterministic methods. We took part to the writing of a position paper on the futur of sensitivity analysis 23.
8.4.1 Global sensitivity analysis
Participants: Elise Arnaud, Eric Blayo, Laurent Gilquin, Maria Belén Heredia, Adrien Hirvoas, Alexandre Janon, Henri Mermoz Kouye, Clémentine Prieur, Laurence Viry, Arthur Vidard, Emilie Rouzies.
8.4.2 Global sensitivity analysis with dependent inputs
An important challenge for stochastic sensitivity analysis is to develop methodologies which work for dependent inputs. Recently, the Shapley value, from econometrics, was proposed as an alternative to quantify the importance of random input variables to a function. Owen 67 derived Shapley value importance for independent inputs and showed that it is bracketed between two different Sobol' indices. Song et al. 71 recently advocated the use of Shapley value for the case of dependent inputs. In a recent work 66, in collaboration with Art Owen (Standford's University), we show that Shapley value removes the conceptual problems of functional ANOVA for dependent inputs. We do this with some simple examples where Shapley value leads to intuitively reasonable nearly closed form values. We also investigated further the properties of Shapley effects in 58.
8.4.3 Iterative estimation of Sobol’ indices
Participants: Elise Arnaud, Laurent Gilquin, Clémentine Prieur.
In the field of sensitivity analysis, Sobol’ indices are widely used to assess the importance of the inputs of a model to its output. Among the methods that estimate these indices, the replication procedure is noteworthy for its efficient cost. A practical problem is how many model evaluations must be performed to guarantee a sufficient precision on the Sobol’ estimates. We proposed to tackle this issue by rendering the replication procedure iterative 14. The idea is to enable the addition of new model evaluations to progressively increase the accuracy of the estimates. These evaluations are done at points located in underexplored regions of the experimental designs, but preserving their characteristics. The key feature of this approach is the construction of nested spacefilling designs. For the estimation of firstorder indices, a nested Latin hypercube design is used. For the estimation of closed secondorder indices, two constructions of a nested orthogonal array design are proposed. Regularity and uniformity properties of the nested designs are studied.
8.4.4 Green sensitivity for multivariate and functional outputs
Participants: María Belén Heredia, Clémentine Prieur.
Another research direction for global SA algorithm starts with the report that most of the algorithms to compute sensitivity measures require special sampling schemes or additional model evaluations so that available data from previous model runs (e.g., from an uncertainty analysis based on Latin Hypercube Sampling) cannot be reused. One challenging task for estimating global sensitivity measures consists in recycling an available finite set of input/output data. Green sensitivity, by recycling, avoids wasting. These given data have been discussed, e.g., in 68, 69. Most of the given data procedures depend on parameters (number of bins, truncation argument…) not easy to calibrate with a biasvariance compromise perspective. Adaptive selection of these parameters remains a challenging issue for most of these givendata algorithms. In the context of María Belén Heredia’s PhD thesis, we have proposed 2 a nonparametric given data estimator for aggregated Sobol’ indices, introduced in 60 and further developed in 56 for multivariate or functional outputs. We also introduced aggregated Shapley effects and we have extended a nearest neighbor estimation procedure to estimate these indices 37.
8.4.5 Global sensitivity analysis for parametrized stochastic differential equations
Participants: Henri Mermoz Kouye, Clémentine Prieur.
Many models are stochastic in nature, and some of them may be driven by parametrized stochastic differential equations. It is important for applications to propose a strategy to perform global sensitivity analysis (GSA) for such models, in presence of uncertainties on the parameters. In collaboration with Pierre Etoré (DATA department in Grenoble), Clémentine Prieur proposed an approach based on FeynmanKac formulas 12. The research on GSA for stochastic simulators is still ongoing, first in the context of the MATHAmSud project FANTASTIC (Statistical inFerence and sensitivity ANalysis for models described by sTochASTIC differential equations) with Chile and Uruguay, secondly through the PhD thesis of Henri Mermoz Kouye, cosupervised by Clémentine Prieur, in collaboration with INRA Jouy.
8.4.6 Sensitivity analysis in pesticide transfer models
Participants: Arthur Vidard, Emilie Rouzies.
Pesticide transfer models are valuable tools to predict and prevent pollution of water bodies. However, using such models in operational contexts requires a strong knowledge of their structure including influential parameters. This project aims at performing global sensitivity analysis (GSA) of the PESHMELBA model (pesticide and hydrology: modelling at the catchment scale). This work is made hard due to the modular, complex structure of the model that couples different physical processes. It results in a large input space dimension and a high computational cost that limits the number of available runs. Using classical GSA tools such as Sobol' indices is thus not feasible. In order to circumvect those limitations, alternative techniques such as HSIC dependence measure or Random Forest metamodel are explored in 33 and 48. Additionally, the use of such methods in the specific context of spatially distributed output is explored in a paper to be submitted soon.
8.5 Model calibration and statistical inference
Participants: Maria Belén Heredia, Adrien Hirvoas, Clémentine Prieur, Victor Trappler, Arthur Vidard, Elise Arnaud, Laurent Debreu.
8.5.1 Bayesian calibration
Physicallybased avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in longterm forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and diverse data is now increasingly available from testsites, but for measurements made along ow propagation, potential autocorrelation should be explicitly accounted for. In the context of María Belén Heredia’s PhD, in collaboration with IRSTEA Grenoble, we have proposed in 57 a comprehensive Bayesian calibration and statistical model selection framework with application to an avalanche sliding block model with the standard Voellmy friction law and high rate photogrammetric images. An avalanche released at the Lautaret testsite and a synthetic data set based on the avalanche were used to test the approach. Results have demonstrated i) the efficiency of the proposed calibration scheme, and ii) that including autocorrelation in the statistical modelling definitely improves the accuracy of both parameter estimation and velocity predictions. In the context of the energy transition, wind power generation is developing rapidly in France and worldwide. Research and innovation on wind resource characterisation, turbin control, coupled mechanical modelling of wind systems or technological development of offshore wind turbines floaters are current research topics. In particular, the monitoring and the maintenance of wind turbine is becoming a major issue. Current solutions do not take full advantage of the large amount of data provided by sensors placed on modern wind turbines in production. These data could be advantageously used in order to refine the predictions of production, the life of the structure, the control strategies and the planning of maintenance. In this context, it is interesting to optimally combine production data and numerical models in order to obtain highly reliable models of wind turbines. This process is of interest to many industrial and academic groups and is known in many fields of the industry, including the wind industry, as "digital twin”. The objective of Adrien Hirvoas's PhD work is to develop of data assimilation methodology to build the "digital twin" of an onshore wind turbine. Based on measurements, the data assimilation should allow to reduce the uncertainties of the physical parameters of the numerical model developed during the design phase to obtain a highly reliable model. Various ensemble data assimilation approches are currently under consideration to address the problem. In the context of this work, it is necessary to develop algorithms of identification quantifying and ranking all the uncertainty sources. This work in done in collaboration with IFPEN. A first paper has been accepted for publication 42.
8.5.2 Simulation & Estimation of EPIdemics with Algorithms
Participants: Clémentine Prieur.
Due to the sanitary context, Clémentine Prieur decided to join a working group, SEEPIA Simulation & Estimation of EPIdemics with Algorithms, animated by Didier Georges (Gipsalab). A first work has been published 15. An extension of the classical pandemic SIRD model was considered for the regional spread of COVID19 in France under lockdown strategies. This compartment model divides the infected and the recovered individuals into undetected and detected compartments respectively. By fitting the extended model to the real detected data during the lockdown, an optimization algorithm was used to derive the optimal parameters, the initial condition and the epidemics start date of regions in France. Considering all the age classes together, a network model of the pandemic transport between regions in France was presented on the basis of the regional extended model and was simulated to reveal the transport effect of COVID19 pandemic after lockdown. Using the the measured values of displacement of people mobilizing between each city, the pandemic network of all cities in France was simulated by using the same model and method as the pandemic network of regions. Finally, a discussion on an integrodifferential equation was given and a new model for the network pandemic model of each age was provided.
8.5.3 NonParametric statistical inference for Kinetic Diffusions
Participants: Clémentine Prieur, Jose Raphael Leon Ramos.
This research is the subject of a collaboration with Chile and Uruguay. More precisely, we started working with Venezuela. Due to the crisis in Venezuela, our main collaborator on that topic moved to Uruguay.
We are focusing our attention on models derived from the linear FokkerPlanck equation. From a probabilistic viewpoint, these models have received particular attention in recent years, since they are a basic example for hypercoercivity. In fact, even though completely degenerated, these models are hypoelliptic and still verify some properties of coercivity, in a broad sense of the word. Such models often appear in the fields of mechanics, finance and even biology. For such models we believe it appropriate to build statistical nonparametric estimation tools. Initial results have been obtained for the estimation of invariant density, in conditions guaranteeing its existence and unicity 51 and when only partial observational data are available. A paper on the non parametric estimation of the drift has been accepted recently 52 (see Samson et al., 2012, for results for parametric models). As far as the estimation of the diffusion term is concerned, a paper has been accepted 52, in collaboration with J.R. Leon (Montevideo, Uruguay) and P. Cattiaux (Toulouse). Recursive estimators have been also proposed by the same authors in 53, also recently accepted. In a recent collaboration with Adeline Samson from the statistics department in the Lab, we considered adaptive estimation, that is we proposed a datadriven procedure for the choice of the bandwidth parameters.
In 50, we focused on damping Hamiltonian systems under the socalled fluctuationdissipation condition. Idea in that paper were reused with applications to neuroscience in 64.
Note that Professor Jose R. Leon (Caracas, Venezuela, Montevideo, Uruguay) was funded by an international Inria Chair, allowing to collaborate further on parameter estimation.
We recently proposed a paper on the use of the Euler scheme for inference purposes, considering reflected diffusions. This paper could be extended to the hypoelliptic framework.
We also have a collaboration with Karine Bertin (Valparaiso, Chile), Nicolas Klutchnikoff (Université Rennes) and Jose R. León (Montevideo, Uruguay) funded by a MATHAMSUD project (20162017) and by the LIA/CNRS (2018). We are interested in new adaptive estimators for invariant densities on bounded domains 3, and would like to extend that results to hypoelliptic diffusions.
8.5.4 Parameter control in presence of uncertainties: robust estimation of bottom friction
Participants: Victor Trappler, Arthur Vidard, Elise Arnaud, Laurent Debreu.
Many physical phenomena are modelled numerically in order to better understand and/or to predict their behaviour. However, some complex and small scale phenomena can not be fully represented in the models. The introduction of adhoc correcting terms, can represent these unresolved processes, but they need to be properly estimated.
A good example of this type of problem is the estimation of bottom friction parameters of the ocean floor. This is important because it affects the general circulation. This is particularly the case in coastal areas, especially for its influence on wave breaking. Because of its strong spatial disparity, it is impossible to estimate the bottom friction by direct observation, so it requires to do so indirectly by observing its effects on surface movement. This task is further complicated by the presence of uncertainty in certain other characteristics linking the bottom and the surface (eg boundary conditions). The techniques currently used to adjust these settings are very basic and do not take into account these uncertainties, thereby increasing the error in this estimate.
Classical methods of parameter estimation usually imply the minimisation of an objective function, that measures the error between some observations and the results obtained by a numerical model. In the presence of uncertainties, the minimisation is not straightforward, as the output of the model depends on those uncontrolled inputs and on the control parameter as well. That is why we will aim at minimising the objective function, to get an estimation of the control parameter that is robust to the uncertainties.
The definition of robustness differs depending of the context in which it is used. In this work, two different notions of robustness are considered: robustness by minimising the mean and variance, and robustness based on the distribution of the minimisers of the function. This information on the location of the minimisers is not a novel idea, as it had been applied as a criterion in sequential Bayesian optimisation. However, the constraint of optimality is here relaxed to define a new estimate. To evaluate this estimation, a toy model of a coastal area has been implemented. The control parameter is the bottom friction, upon which classical methods of estimation are applied in a simulationestimation experiment. The model is then modified to include uncertainties on the boundary conditions in order to apply robust control methods. This has been published in 26
8.6 Modeling and inference for extremes
Participants: Philomène Le Gall, Clémentine Prieur, Patricia Tencaliec.
In 72, we are considering the modeling of precipitation amount with semiparametric models, modeling both the bulk of the distribution and the tails, but avoiding the arbitrary choice of a threshold. We work in collaboration with AnneCatherine Favre (LGGELab in Grenoble) and Philippe Naveau (LSCE, Paris).
In the context of Philomène Le Gall’s PhD thesis, we are applying the aforementioned modeling of extreme precipitation with the aim of regionalizing extreme precipitation.
9 Bilateral contracts and grants with industry
9.1 Bilateral contracts with industry
 A 5year contract (started in January 2020) 19CP07 with the Oceanographic and Hydrographic Service of the French Navy (SHOM) on the topic « Analyse numérique pour la réconciliation en espace et en temps des discrétisations des échanges airmer et leur paramétrisation. Application à des cas simplifiés et réalistes couplés.» (PI: F. Lemarié).
 A 2year contract with MercatorOcean on the thematic "The AGRIF software in the NEMO European ocean model": see 7.1.1
 Contract with IFPEN (Institut Français du pétrole et des énergies nouvelles), for the supervision of a PhD (Adrien Hirvoas). Research subject: Development of a data assimilation method for the calibration and continuous update of wind turbines digital twins
 Contract with IFPEN (Institut Français du pétrole et des énergies nouvelles), for the supervision of a PhD (Clément Duhamel). Research subject: Inversion robuste d’un code de calcul prenant en entrées des données de nature fonctionnelle. Application à la conception d’éoliennes
 The Chair OQUAIDO – for "Optimisation et QUAntification d'Incertitudes pour les Données Onéreuses" in French – is the chair in applied mathematics held at Mines SaintÉtienne (France). It aims at gathering academical and technological partners to work on problems involving costlytoevaluate numerical simulators for uncertainty quantification, optimization and inverse problems. This Chair, created in January 2016, is the continuation of the projects DICE and ReDICE which respectively covered the periods 20062009 and 20112015. Reda El Amri's PhD thesis 54 has been funded by OQUAIDO. The Chair is reconducted for one year in 2020 and then it turns to the consortium CIROQUO (Consortium Industriel de Recherche en Optimisation et QUantification d'incertitudes pour les données Onéreuses).
10 Partnerships and cooperations
10.1 International initiatives
10.1.1 Inria associate team not involved in an IIL
UNQUESTIONABLE
 Title: UNQUESTIONABLE
 Duration: 2018  2020
 Coordinator: Clémentine Prieur

Partners:
 Aerospace Computational Design Laboratory, Massachusetts Institute of Technology (United States)
 Inria contact: Clémentine Prieur

Summary:
The ability to understand and predict the behavior of geophysical flows is of greatest importance, due to its strong societal impact. Numerical models are essential to describe the evolution of the system (ocean + atmosphere), and involve a large number of parameters, whose knowledge is sometimes really poor. The reliability of the numerical predictions thus requires a step of parameter identification. The InriaAIRSEA team has a strong expertise in variational approaches for inverse problems. An alternative is the use of particle filters, whose main advantage is their ability to tackle nongaussian frameworks. However, particle filters suffer from the curse of dimensionality. The main objective of the collaboration we propose between the InriaAIRSEA team and the MIT UQ group is the understanding of potential lowdimensional structure underlying geophysical applications, then the exploitation of such structures to extend particle filter to highdimensional applications. See also https://
team. inria. fr/ unquestionable/
10.1.2 Inria international partners
Informal international partners
 C. Prieur collaborates with Jose R. Leon (Universdad de la república de Uruguay, Montevideo).
 C. Prieur collaborates with K. Bertin (CIMFAV, Valparaíso).
 F.X. Le Dimet is a Honorary Professor of the Institut of Mechanics, Ac.Sci. Vietnam.
 F.X. Le Dimet is a Honorary Professor of the Institut of Numerical Mathematics, Russian Ac.Sci.
10.2 European initiatives
10.2.1 FP7 & H2020 Projects
IMMERSE
 Title: Improving Models for Marine EnviRonment SErvices
 Duration: December 2018  Novembre 2022.
 Coordinator: CNRS

Partners:
 ALMA MATER STUDIORUM  UNIVERSITA DI BOLOGNA (Italy)
 BARCELONA SUPERCOMPUTING CENTER  CENTRO NACIONAL DE SUPERCOMPUTACION (Spain)
 CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS (France)
 CERFACS CENTRE EUROPEEN DE RECHERCHE ET DE FORMATION AVANCEE EN CALCUL SCIENTIFIQUE SOCIETE CIVILE (France)
 FONDAZIONE CENTRO EUROMEDITERRANEOSUI CAMBIAMENTI CLIMATICI (Italy)
 HELMHOLTZ ZENTRUM FUR OZEANFORSCHUNG KIEL (Germany)
 INSTITUT DE RECHERCHE POUR LE DEVELOPPEMENT (France)
 PLYMOUTH MARINE LABORATORY LIMITED (UK)
 Puertos del Estado (Spain)
 UNITED KINGDOM RESEARCH AND INNOVATION (UK)
 UNIVERSITEIT UTRECHT (Netherlands)
 Inria contact: F. Lemarié
 Summary: The overarching goal of IMMERSE project is to ensure that the Copernicus Marine Environment Monitoring Service (CMEMS) will have continuing access to worldclass marine modelling tools for its next generation systems while leveraging advances in space and information technologies, therefore allowing it to address the everincreasing and evolving demands for marine monitoring and prediction in the 2020s and beyond.
10.2.2 Collaborations in European programs, except FP7 and H2020
 Program: C3S
 Project acronym: ERGO
 Project title: Enabling an Ensemble of Data Assimilation for the Ocean
 Duration: Février 2019  juillet 2021
 Coordinator: Arthur Vidard
 Other partners: Cerfacs (France), Met Office (U.K.), CMRE (int, Italie)
 Abstract: The scope of this contract is to improve ocean data assimilation capabilities at ECMWF, used in both initialization of seasonal forecasts and generation of coupled Earth System reanalyses. In particular it shall focus on i) improving ensemble capabilities in NEMO and NEMOVAR and the use of their information to represent background error statistics; ii) extend NEMOVAR capabilities to allow for multiple resolution in multiincremental 3DVar; iii) make better use of ocean surface observations. It shall also involve performing scout experiments and providing relevant diagnostics to evaluate the benefit coming from the proposed developments.
10.2.3 Collaborations with major European organizations
 Partner: European Center for Medium Range Weather Forecast. Reading (UK)
 World leading Numerical Weather Center, that include an ocean analysis section in order to provide ocean initial condition for the coupled ocean atmosphere forecast. They play a significant role in the NEMOVAR project in which we are also partner.
 Partner: Met Office (U.K) National British Numerical Weather and Oceanographic service. Exceter (UK).
 We do have a strong collaboration with their ocean initialization team through both our NEMO, NEMOASSIM and NEMOVAR activities. They also are our partner in the NEMOVAR consortium.
 Partner : SAMO board

SAMO board is in charge of the organization of the SAMO (sensitivity analysis of model outputs) conferences, every three years. It is strongly supported by the Joint Research Center of the European Commission.
In 2019, Clémentine Prieur, which is part of this board, as also cochair of a satellite event on the future of sensitivity analysis. A position paper 23 has been published, as a synthesis of the discussions hold in Barcelona (autumn 2019).
10.3 National initiatives
10.3.1 ANR
 A 4year contract : ANR COCOA (COmprehensive Coupling approach for the Ocean and the Atmosphere). PI: E. Blayo. (Jan. 2017  Jun. 2021). Other partners: Laboratoire des Sciences du Climat et de l'Environnement (UMR8212, GifsurYvette), Laboratoire de Météorologie Dynamique (UMR8539, Paris), Laboratoire d'Océanographie Physique et Spatiale (UMR6523, Brest), Centre National de Recherche Météorologique (UMR3589, Toulouse), Cerfacs (Toulouse). This project aims at revisiting the overall representation of airsea interactions in coupled oceanatmosphere models, and particularly in climate models, by coherently considering physical, mathematical, numerical and algorithmic aspects.
 A 4year contract : ANR ADOM (Asynchronous Domain decomposition methods)
https://
anr. fr/ ProjetANR18CE460008  A 5year contract : ANR MELODY (Bridging geophysics and MachinE Learning for the modeling, simulation and reconstruction of Ocean DYnamic) https://
anr. fr/ ProjetANR19CE460011  A 5year contract with the French Navy (SHOM) on the improvment of the CROCO ocean model http://
www. .crocoocean. org  C. Prieur and E. Arnaud are involved as experts in project HighTune http://
www. funded by ANR.agencenationalerecherche. fr/ ProjetANR16CE010010
10.3.2 Inria Challenge
 Sea Uncertainty Representation and Forecast (SURF),
 Coord : Airsea (A. Vidard),
 Partenaires Inria : Ange, Cardamom, Fluminance, Lemon, Mingus, Defi
 Partenaires extérieurs: BRGM, Ifremer, SHOM
10.3.3 Other Initiatives
 A. Vidard leads a group of projects gathering multiple partners in France and UK on the topic "Variational Data Assimilation for the NEMO/OPA9 Ocean Model", see 7.1.3.
 LEFE/GMMC CASIS, Coupled Assimilation Strategies for the Initialisation of an ocean atmospheric boundary layer System, A. Vidard en collaboration avec Mercator océan
 C. Prieur is coadvising the PhD thesis of Henri Mermoz Kouye, in the framework of the InriaINRA collaboration.
 C. Prieur chaired GdR MASCOT NUM 20102017, in which are also involved M. Nodet, E. Blayo, C. Helbert, E. Arnaud, L. Viry, S. Nanty, L. Gilquin. She is still strong involved in the group (cochair). In particular, she will cochair next GdR annual meeting in Aussois (May 2020, reported to April 2021).
https://
www. .gdrmascotnum. fr/
10.4 Regional initiatives
 C. Prieur is coleader of workpackage 3 of the crossdisciplinaryproject Trajectories from Idex Grenoble.
11 Dissemination
11.1 Promoting Scientific Activities
11.1.1 Scientific Events: Selection
Member of the Conference Program Committees
 F. Lemarié and L. Debreu were part of the scientific committee
of the second international COMMODORE Conference which took place in Hamburg
during 2831 January 2020 https://
www. conferences. unihamburg. de/ event/ 76/
11.1.2 Journal
Member of the Editorial Boards
 F. Lemarié is associate editor of the Journal of Advances in Modeling Earth Systems (JAMES)
Reviewer  Reviewing Activities
 E. Blayo: reviewer for Ocean Modelling, Journal of Scientific Computing
 F. Lemarié reviewed papers for Tellus A, Geoscientific Model Development, Ocean Modeling, and Journal of Advances in Modeling Earth Systems
11.1.3 Invited Talks
 F. Lemarié: Ocean Modeling for Predictions Workshop (online workshop which took place on June 2930) 32
11.1.4 Leadership within the Scientific Community
 L. Debreu is the chair of the CNRSINSU research program LEFEMANU on mathematical and numerical methods for ocean and atmosphere https://
insu. since April 2018.cnrs. fr/ fr/ methodesmathematiquesetnumeriquesmanu  L. Debreu is the coordinator of the national group COMODO (Numerical Models in Oceanography).
 L. Debreu is a member of the steering committee of the CROCO ocean model https://
www. crocoocean. org
11.1.5 Scientific Expertise
 F. Lemarié is a member of the CROCO
(https://
www. ) scientific committee in charge of the « numerical methods » topic.crocoocean. org/  F. Lemarié is a member of the NEMO
(https://
www. ) Developers Committee as external expert.nemoocean. eu/
11.1.6 Research Administration
 E. Blayo is a deputy director of the Jean Kuntzmann Lab.
 C. Prieur is a member of the Scientific Council of the Mathematical Society of France (SMF).
 C. Prieur is a member of the Research Council of UGA.
 E. Arnaud is in charge of the AMAC (Algorithmes, Modeles, Analyses, Calcul) department of the Jean Kuntzmann Lab.
 E. Arnaud : member of PhD recruitment committee CORDIS
 E. Arnaud : in charge of ATER recruitment committee in computer science at Univ Grenoble Alpes
 E. Arnaud : in charge of the parity commission at Jean Kuntzmann Lab.
11.2 Teaching  Supervision  Juries
11.2.1 Teaching
 Licence (ou équivalent) : E. Blayo, Mathematical analysis, 80h, L1, University Grenoble Alpes, France
 Licence (ou équivalent) : C. Kazantsev, Mathématiques, outils pour les sciences et l'Ingenierie, 100h, L1, UGA, France
 Licence (ou équivalent) : C. Kazantsev, Mathématiques pour les sciences de l'ingénieur, 66h, L2, UGA, France
 Master (ou équivalent) : E. Blayo, Partial Differential Equations, 45h, M1, University Grenoble Alpes, France
11.2.2 Supervision
 Intern : Camille Saint Martin, Couches absorbantes parfaitement adaptées pour un modèle d’écoulement océanique nonhydrostatique compressible, M2R, applied mathematics, Univ. Montpellier, 5 months, E. Blayo.
 PhD in progress: Sophie Théry, Numerical study of coupling algorithms and boundary layer parameterizations in climate models. October 2017, E. Blayo and F. Lemarié.
 PhD in progress: Emilie Duval, Coupling hydrostatic and nonhydrostatic ocean circulation models. October 2018, L. Debreu and E. Blayo.
 PhD in progress: Victor Trappler, Parameter control in presence of uncertainties, October 2017, E. Arnaud, L. Debreu and A. Vidard.
 PhD in progress: Rishabh Batth, Asynchronousparallel in time schemes for data assimilation, December 2019, L. Debreu and A. Vidard.
 PhD in progress: Simon Clément, Numerical analysis for reconciling in space and time the airsea exchanges and their parameterization. October 2019, E. Blayo and F. Lemarié.
 PhD in progress: Adrien Hirvoas, Development of a data assimilation method for the calibration and continuous update of wind turbines digital twins, May 2018, E. Arnaud, C. Prieur, F. Caleyron
 PhD in progress : Antoine Nasser, Volume penalization methods for the representation of flows along topography in 3D ocean models, October 2019, L. Debreu and G. Madec.
 Post Doc in progress : Anass El Aouni, Multiresolution techniques for ocean data assimilation, October 2019, A. Vidard
11.2.3 Juries
 Arthur Vidard
 Jury Ingénieur de recherche UGA 2020.
 E. Blayo:
 Jul. 17, 2020: PhD thesis of BeatrizIxetl Garçia Gomez, University Grenoble Alpes (president)
 Sep. 29, 2020: PhD thesis of Laura Gómez Navarro, University Grenoble Alpes (president)
 Nov. 10, 2020: HDR thesis of Laure Raynaud, University of Toulouse (reporter)
 Nov. 18, 2020: PhD thesis of Sibo Cheng, University Paris Saclay (reporter)
11.3 Popularization
11.3.1 Internal or external Inria responsibilities
 F. Lemarié is a member of the local "comité des emplois scientifiques" (CES) (since Sept 2019)
 F. Lemarié is the local scientific correspondent for national calls for projects (since July 2020)
 E.Kazantsev is a member of the Local Commission for Permanent Formation of Inria Grenoble  RhôneAlpes.
11.3.2 Articles and contents
 E. Blayo, L. Debreu et C. Kazantsev, 2020 : Les modèles de climat. Maths Express : les maths, oui ça sert !, juin 2020. Publication du Comité International des Jeux Mathématiques.
 Blayo E. et C. Kazantsev, 2020 : Le projet de la Grange des Maths. La Gazette des Mathématiciens, 166, 6571, octobre 2020.
11.3.3 Communication to the press
 C. Prieur was placed on a list of experts who could answer questions on various aspects of the news related to the ongoing Covid19 epidemic by the CNRS press office. This list is made available to journalists in order to direct them to the appropriate interlocutors according to the angle of their questions.
11.3.4 Creation of media or tools for science outreach
 E. Arnaud, in charge of the pedagogic platform math@uga : implementation of a collaborative moodle platform http://
math. to share pedagogical resources within teachers and towards students.uga. fr  C.Kazantsev participated in the edition of the Teachers notebooks which explain and advise how to use the "La Grange Suitcases" (sets of mathematical games, problems and animations) destined for primary and secondary schools teachers as well as for the general public.
 C.Kazantsev participated in the creation of mathematical activities that can be autonomously used by schoolchildren of primary and secondary schools and by the general public.
11.3.5 Education
 Ch. Kazantsev and E. Blayo are strongly involved in the creation and dissemination of pedagogic suitcases with mathematical activities designed for primary and secondary schools used by 10,000 – 12,000 pupils in 20182019. This is done in collaboration with the Rectorat de Grenoble. This action received the prize Jacqueline Ferrand 2020 from the French Mathematical Society for educational innovation.
 C. Kazantsev is a member of an IREM group for creation of scientific activities for professional development of secondary schools teachers.
 C. Kazantsev is a member of an International InterIREM commission, which work on the multilanguages problem for children in the mathematical learning. Three meetings take place in Paris during the year, the first was on September 28.
 E. Arnaud has given a Seminar and training day, "(se) tromper avec les chiffres", formation esprit critique, with La maison pour la sciences
 E. Arnaud, C. Kazantzev have visited the Varces prison in france of an action of the "La grange des maths" to show the interest of mathematics to young prisonners.
11.3.6 Interventions
 E. Blayo gave several outreach talks, in particular for middle school and high school students, and for more general audiences.
 Ch. Kazantsev and E. Blayo are strongly involved in the creation of "La Grange des maths", a science popularization center that will be located in Varces (south of Grenoble), which will offer a huge variety of mathematical handson exhibits. See http://
www. lagrangedesmaths. fr/
12 Scientific production
12.1 Publications of the year
International journals
 1 articleAn Efficient Detection of Moroccan Coastal Upwelling Based on Fusion of Chlorophylla and Sea Surface Temperature Images With a New Validation IndexIEEE Geoscience and Remote Sensing LettersJune 2020, 15
 2 article Nonparametric estimation of aggregated Sobol' indices: application to a depth averaged snow avalanche model International Journal of Reliability, Quality and Safety Engineering January 2020
 3 articleAdaptive density estimation on bounded domains under mixing conditionsElectronic journal of statistics1412020, 21982237
 4 articleFast and Stable Schemes for Phase Fields ModelsComputers and Mathematics with Applications806September 2020, 16831713
 5 article Spherical Shallow Water Waves Waves Simulation by a Cubed Sphere Finite Difference Solver Quaterly Journal of the Royal Meteorological Society November 2020
 6 article Impact of the current feedback on kinetic energy over the NorthEast Atlantic from a coupled ocean/atmospheric boundary layer model Ocean Science Discussions August 2020
 7 articleMultiscale Representation of Observation Error Statistics in Data AssimilationSensors205March 2020, 120
 8 articleDevelopment of a 2way coupled oceanwave model: assessment on a global NEMO(v3.6)WW3(v6.02) coupled configurationGeoscientific Model Development13July 2020, 3067–3090
 9 articleBrinkman volume penalization for bathymetry in threedimensional ocean modelsOcean Modelling145January 2020, 113
 10 articleDatadriven stochastic inversion via functional quantizationStatistics and Computing303May 2020, 525541
 11 articleA hybrid identification and tracking of Lagrangian mesoscale eddiesPhysics of Fluids333March 2021, 036604
 12 articleGlobal sensitivity analysis for models described by stochastic differential equationsMethodology and Computing in Applied Probability22June 2020, 803831
 13 articleEnhanced vertical mixing in coastal upwelling systems driven by diurnalinertial resonance: numerical experimentsJournal of Geophysical Research. Oceans1259September 2020, e2020JC016208
 14 article Iterative estimation of Sobol’ indices based on replicated designs Computational and Applied Mathematics 40 18 January 2021
 15 articleTransport effect of COVID19 pandemic in FranceAnnual Reviews in Control502020, 394408
 16 articleThe Role of Tides in Ocean‐Ice Shelf Interactions in the Southwestern Weddell SeaJournal of Geophysical Research. Oceans1256June 2020, e2019JC015847
 17 articleNumerical modeling of hydraulic control, solitary waves and primary instabilities in the Strait of GibraltarOcean Modelling155July 2020, 101642
 18 articleMultifidelity Dimension Reduction via Active SubspacesSIAM Journal on Scientific Computing422April 2020, A929A956
 19 articleA simplified atmospheric boundary layer model for an improved representation of airsea interactions in eddying oceanic models: implementation and first evaluation in NEMO (4.0)Geoscientific Model Development14December 2020, 543  572
 20 article A Schwarz iterative method to evaluate ocean atmosphere coupling schemes. Implementation and diagnostics in IPSLCM6SWVLR Geoscientific Model Development Discussions December 2020
 21 articleThe Thermohaline Modes of the Global OceanJournal of Physical Oceanography49November 2020, 2535  2552
 22 article Twosided turbulent surface layer parameterizations for computing airsea fluxes Quarterly Journal of the Royal Meteorological Society February 2021
 23 articlePosition Paper: The Future of Sensitivity Analysis: An Essential Discipline for Systems Modelling and Policy MakingEnvironmental Modelling and Software137March 2021, 104954
 24 articleSensitivity of response functions in variational data assimilation for joint parameter and initial state estimationJournal of Computational and Applied Mathematics373August 2020, 112368:114
 25 article Randomized residualbased error estimators for the Proper Generalized Decomposition approximation of parametrized problems International Journal for Numerical Methods in Engineering 2020
 26 articleRobust calibration of numerical models based on relative regretJournal of Computational PhysicsNovember 2020, 109952
 27 article Jarzynski equality and Crooks relation for local models of airsea interaction Earth System Dynamics Discussions December 2020
 28 articleGradientbased dimension reduction of multivariate vectorvalued functionsSIAM Journal on Scientific Computing421February 2020, A929A956
International peerreviewed conferences
 29 inproceedings Greedy inference with structureexploiting lazy maps NeurIPS Virtual, Canada December 2020
Conferences without proceedings
 30 inproceedings Numerical representation of internal gravity waves propagation 2nd COMMODORE Workshop Hamburg, Germany January 2020
 31 inproceedings On the spatial and temporal discretization of vertical diffusion in the turbulent planetary boundary layer AGU Ocean Sciences Meeting San Diego, United States February 2020
 32 inproceedings Recent evolution and challenges for oceanic dynamical cores across all scales Ocean Modeling for Predictions Workshop Toulouse, France June 2020
 33 inproceedings Sensitivity analysis to evaluate a new spatialized processoriented model of water and pesticide transfers at the catchment scale 10th iEMSs Conference 2020 International Environmental Modelling and Software Society Brussels, Belgium September 2020
Reports & preprints
 34 misc Set inversion under functional uncertainties with joint metamodels 2020
 35 misc Theory and analysis of acousticgravity waves in a freesurface compressible and stratified ocean October 2020
 36 misc An adaptive transport framework for joint and conditional density estimation December 2020
 37 misc Aggregated Shapley effects: nearestneighbor estimation procedure and confidence intervals. Application to snow avalanche modeling July 2020
 38 misc Nonlinear dimension reduction for surrogate modeling using gradient information February 2021
 39 misc A PAC algorithm in relative precision for bandit problem with costly sampling February 2021
 40 misc Comparaison of Exponential integrators and traditional time integration schemes for the Shallow Water equations April 2020
 41 misc DataFree LikelihoodInformed Dimension Reduction of Bayesian Inverse Problems March 2021
 42 misc Quantification and reduction of uncertainties in a wind turbine numerical model based on global sensitivity analysis and recursive Bayesian inference approach June 2020
 43 misc Minimizing rational functions: a hierarchy of approximations via pushforward measures December 2020
 44 misc Tridimensional nonhydrostatic transient rip currents in a waveresolving model October 2020
 45 misc Analysis of Schwarz Waveform Relaxation for the Coupled Ekman Boundary Layer Problem with Continuously Variable Coefficients April 2020
 46 misc Certified dimension reduction in nonlinear Bayesian inverse problems March 2021
Other scientific publications
 47 misc LES Modelling of the Impact of the Topography on Largescale Exchange Flow in the Strait of Gibraltar San Diego, United States 2020
 48 misc How can we quantify and reduce the uncertainty of a watershedscale pesticide transfer model? A comparison of several approaches. Online, United States December 2020
12.2 Cited publications
 49 inproceedingsStochastic methods for solving highdimensional partial differential equationsInternational Conference on Monte Carlo and QuasiMonte Carlo Methods in Scientific Computing  MCQMC 2018324Springer Proceedings in Mathematics & StatisticsRennes, FranceSpringerJuly 2018, 125141
 50 articleAn overlook on statistical inference issues for stochastic damping Hamiltonian systems under the fluctuationdissipation conditionStatistics5112017, 1129
 51 articleEstimation for Stochastic Damping Hamiltonian Systems under Partial Observation. I. Invariant densityStochastic Processes and their Applications1243March 2014, 12361260
 52 articleEstimation for Stochastic Damping Hamiltonian Systems under Partial Observation. II Drift termALEA (Latin American Journal of Probability and Statistics)1112014, 359384
 53 articleRecursive Estimation for Stochastic Damping Hamiltonian SystemsJournal of Nonparametric Statistics2732015, 401424
 54 phdthesis Analyse d'incertitudes et de robustesse pour les modèles à entrées et sorties fonctionnelles. Université Grenoble Alpes April 2019
 55 articleDatadriven Stochastic Inversion via Functional QuantizationStatistics and Computing303May 2020, 525541
 56 articleSensitivity analysis for multidimensional and functional outputsElectronic Journal of Statistics812014, 575603
 57 inproceedings Calibration of the Voellmy avalanche friction parameters using a Bayesian approach from an high rate positioning avalanche EGU 2018  European Geosciences Union General Assembly Vienna, Austria April 2018
 58 articleShapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applicationsInternational Journal for Uncertainty Quantification952019, 493514
 59 misc Adam: A Method for Stochastic Optimization 2014
 60 articleMultivariate sensitivity analysis to measure global contribution of input factors in dynamic modelsReliability Engineering & System Safety9642011, 450459
 61 inproceedings An overview of the oceanatmosphere coupling 2019  PhysicsDynamics Coupling in Earth System Models Banff, Canada October 2019
 62 inproceedings On the discretization of vertical diffusion in the turbulent surface and planetary boundary layers PDC 2018  3rd workshop on Physics Dynamics Coupling Reading, United Kingdom July 2018
 63 inproceedings Recent developments in NEMO within the Albatross project DRAKKAR 2019  Drakkar annual workshop Grenoble, France January 2019
 64 article Hypoelliptic stochastic FitzHughNagumo neuronal model: mixing, upcrossing and estimation of the spike rate Annals of Applied Probability 2017
 65 articleImpacts of the Mesoscale OceanAtmosphere Coupling on the PeruChile Ocean Dynamics: The CurrentInduced Wind Stress ModulationJournal of Geophysical Research. Oceans1232February 2018, 812833
 66 articleOn Shapley value for measuring importance of dependent inputsSIAM/ASA Journal on Uncertainty Quantification511September 2017, 9861002
 67 articleSobol' indices and Shapley valueJournal on Uncertainty Quantification22014, 245251
 68 articleAn effective algorithm for computing global sensitivity indices (EASI)Reliability Engineering & System Safety9542010, 354360
 69 articleGlobal sensitivity measures from given dataEuropean Journal of Operational Research22632013, 536550
 70 articleOn the implementation and consequences of the oceanic currents feedback in oceanatmosphere coupled modelsOcean Modelling141September 2019, 101423
 71 techreport Shapley Effects for Global Sensitivity Analysis: Theory and Computation Northwestern University 2015
 72 articleFlexible semiparametric Generalized Pareto modeling of the entire range of rainfall amountEnvironmetrics312June 2019, e2582:128
 73 misc Deep Learning for Image Superresolution: A Survey 2020