Operational real-time and forecast modelling of Atlantic albacore tuna

The model SEAPODYM (Spatial Ecosystem And Population Dynamics) has now reached a degree of maturity allowing to use it for testing management scenarios and to implement operational monitoring. It is proposed to implement an operational forecast system for the Atlantic albacore tuna. The system will use physical field (temperature, currents and primary production) from Copernicus CMEMS. The sensitivity to improved physical variables with data assimilation will be analysed and the interest of this operational production of tuna stock distributions evaluated in collaboration with colleagues involved in the management of tuna fisheries at ICCAT and FAO, and the AtlantOS fitness for this modelling analysed [D8.9

Optimal design of ecosystem module

Within Task 5.3 “Regional Observing system simulation experiments and process modelling”; CLS is in charge of improving an ecosystem module for the ocean mid-trophic levels (i.e. micronekton) that utilizes multiple in-situ and satellite data as input to derive predictions for different trophic levels including fish [D5.5] and has the potential to be implemented into the routine services supported by a future IAOOS. With physical and biogeochemical variables becoming available in real-time, the real-time monitoring of marine resources relying on the development of ecosystem models is envisaged. Optimal design of acoustic sampling to calibrate the model parameters is investigated and the ecosystem module prepared for integration into the operational system in support of Task 8.7

Toward the assimilation of images

Abstract. The equations that govern geophysical fluids (namely atmosphere, ocean and rivers) are well known but their use for prediction requires the knowledge of the initial condition. In many practical cases, this initial condition is poorly known and the use of an imprecise initial guess is not sufficient to perform accurate forecasts because of the high sensitivity of these systems to small perturbations. As every situation is unique, the only additional information that can help to retrieve the initial condition are observations and statistics. The set of methods that combine these sources of heterogeneous information to construct such an initial condition are referred to as data assimilation. More and more images and sequences of images, of increasing resolution, are produced for scientific or technical studies. This is particularly true in the case of geophysical fluids that are permanently observed by remote sensors. However, the structured information contained in images or image sequences is not assimilated as regular observations: images are still (under-)utilized to produce qualitative analysis by experts. This paper deals with the quantitative assimilation of information provided in an image form into a numerical model of a dynamical system. We describe several possibilities for such assimilation and identify associated difficulties. Results from our ongoing research are used to illustrate the methods. The assimilation of image is a very general framework that can be transposed in several scientific domains

Eigenvalue computations in the context of data-sparse approximations of integral operators

In this work, we consider the numerical solution of a large eigenvalue problem resulting from a finite rank discretization of an integral operator. We are interested in computing a few eigenpairs, with an iterative method, so a matrix representation that allows for fast matrix-vector products is required. Hierarchical matrices are appropriate for this setting, and also provide cheap LU decompositions required in the spectral transformation technique. We illustrate the use of freely available software tools to address the problem, in particular SLEPc for the eigensolvers and HLib for the construction of H-matrices. The numerical tests are performed using an astrophysics application. Results show the benefits of the data-sparse representation compared to standard storage schemes, in terms of computational cost as well as memory requirements.This work was partially supported by the Spanish Ministerio de Ciencia e Innovacion under projects TIN2009-07519, TIN2012-32846 and AIC10-D-000600 and by Fundacao para a Ciencia e a Tecnologia - FCT under project FCT/MICINN proc 441.00.Román Moltó, JE.; Vasconcelos, PB.; Nunes, AL. (2013). Eigenvalue computations in the context of data-sparse approximations of integral operators. Journal of Computational and Applied Mathematics. 237(1):171-181. doi:10.1016/j.cam.2012.07.021S171181237

Assimilation of Images in Numerical Models in Geophysics

ISBN 978-85-7650-152-7International audiencePredicting the evolution of geophysical fluids (ocean, atmosphere, continental water) is a ma jor scientific and societal challenge. Achieving this goal requires to consider all the available information: numerical models, observations, error statistics... In order to combine these heterogeneous source of information one uses the data assimilation techniques. During the last two decades, many visible and infrared band sensors have been launched on different satellites. This provide a large amount sequence of images of the earth system. This kind of information is underused in current data assimilation systems. In this paper we will describe how to use optimal control methods for data assimilation and in particular we will emphasise on techniques allowing to assimilate sequences of images

Coupling dynamic equations and satellite images for modelling ocean surface circulation

International audienceSatellite image sequences visualise the ocean surface and allow assessing its dynamics. Processing these data is then of major interest to get a better understanding of the observed processes. As demonstrated by state-of-the-art, image assimilation permits to retrieve surface motion, based on assumptions on the dynamics. In this paper, we demonstrate that a simple heuristics, such as the Lagrangian constancy of velocity, can be used and successfully replaces the complex physical properties described by the Navier-Stokes equations for assessing surface circulation from satellite images. A data assimilation method is proposed that adds an acceleration term a(t) to this Lagrangian constancy equation, which summarises all physical processes other than advection. A cost function is designed that quantifies discrepancy between satellite data and model values. This cost function is minimised by the BFGS solver with a dual method of data assimilation. The result is the initial motion field and the acceleration terms a(t) on the whole temporal interval. These values a(t) model the forces, other than advection, that contribute to surface circulation. Our approach was tested on synthetic data and with Sea Surface Temperature images acquired on Black Sea. Results are quantified and compared to those of state-of-the-art methods

KRILLPODYM: a mechanistic, spatially resolved model of Antarctic krill distribution and abundance

Robust prediction of population responses to changing environments requires the integration of factors controlling population dynamics with processes affecting distribution. This is true everywhere but especially in polar pelagic environments. Biological cycles for many polar species are synchronised to extreme seasonality, while their distributions may be influenced by both the prevailing oceanic circulation and sea-ice distribution. Antarctic krill (krill, Euphausia superba) is one such species exhibiting a complex life history that is finely tuned to the extreme seasonality of the Southern Ocean. Dependencies on the timing of optimal seasonal conditions have led to concerns over the effects of future climate on krill’s population status, particularly given the species’ important role within Southern Ocean ecosystems. Under a changing climate, established correlations between environment and species may breakdown. Developing the capacity for predicting krill responses to climate change therefore requires methods that can explicitly consider the interplay between life history, biological conditions, and transport. The Spatial Ecosystem And Population Dynamics Model (SEAPODYM) is one such framework that integrates population and general circulation modelling to simulate the spatial dynamics of key organisms. Here, we describe a modification to SEAPODYM, creating a novel model – KRILLPODYM – that generates spatially resolved estimates of krill biomass and demographics. This new model consists of three major components: (1) an age-structured population consisting of five key life stages, each with multiple age classes, which undergo age-dependent growth and mortality, (2) six key habitats that mediate the production of larvae and life stage survival, and (3) spatial dynamics driven by both the underlying circulation of ocean currents and advection of sea-ice. We present the first results of KRILLPODYM, using published deterministic functions of population processes and habitat suitability rules. Initialising from a non-informative uniform density across the Southern Ocean our model independently develops a circumpolar population distribution of krill that approximates observations. The model framework lends itself to applied experiments aimed at resolving key population parameters, life-stage specific habitat requirements, and dominant transport regimes, ultimately informing sustainable fishery management

Analyse et résolution numérique de l'équation de transfert (application au problème des atmosphères stellaires)

ST ETIENNE-BU Sciences (422182103) / SudocSudocFranceF