Examining the Potential Impact of SWOT Observations In an Ocean Analysis-Forecasting System

NASA\u27s Surface Water and Ocean Topography (SWOT) satellite, scheduled for launch in 2020, will provide observations of sea surface height anomaly (SSHA) at a significantly higher spatial resolution than current satellite altimeters. This new observation type is expected to improve the ocean model mesoscale circulation. The potential improvement that SWOT will provide is investigated in this work by way of twin-data assimilation experiments using the Navy Coastal Ocean Model four-dimensional variational data assimilation (NCOM-4DVAR) system in its weak constraint formulation. Simulated SWOT observations are sampled from an ocean model run (referred to as the nature run) using an observation-simulator program provided by the SWOT science team. The SWOT simulator provides realistic spatial coverage, resolution, and noise characteristics based on the expected performance of the actual satellite. Twin-data assimilation experiments are run for a two-month period during which simulated observations are assimilated into a separate model (known as the background model) in a series of 96-h windows. The final condition of each analysis window is used to initialize a new 96-h forecast, and each forecast is compared to the nature run to determine the impact of the assimilated data. It is demonstrated here that the simulated SWOT observations help to constrain the model mesoscale to be more consistent with the nature run than the assimilation of traditional altimeter observations alone. The findings of this study suggest that data from SWOT may have a substantial impact on improving the ocean model forecast of mesoscale features and surface ocean velocity

Vector field regularization by generalized diffusion

Regularization is a common procedure when dealing with inverse problems. Because of the ill-posedness of many inverse problems, one needs to add some constraints as regularization to the problem in order to get a satisfactory solution. A difficulty when using multiple constraints is to properly choose a weighting parameter for each constraint. We propose here a vector field regularization method that combines in a single constraint the two well-known regularization methods namely Tikhonov regularization and smoothing regularization. The particularity of this new method is that one have only one balance parameter to determine. We also suggest a robust implementation of the proposed method based on the equivalent generalized diffusion equation in some particular cases. This implementation is illustrated on a set of vector fields of fluid motio

Generalised diffusion based regularization for inverse problem in image processing

International audienceDue to the ill-posedness of inverse problems, it is important to make use of most of the \textit{a priori} informations while solving such a problem. These informations are generally used as constraints to get the appropriate solution. In usual cases, constrains are turned into penalization of some characteristics of the solution. A common constraint is the regularity of the solution leading to regularization techniques for inverse problems. Regularization by penalization is affected by two principal problems: - as the cost function is composite, the convergence rate of minimization algorithms decreases - when adequate regularization functions are defined, one has to define weighting parameters between regularization functions and the objective function to minimize. It is very difficult to get optimal weighting parameters since they are strongly dependant on the observed data and the truth solution of the problem. There is a third problem that affects regularization based on the penalization of spatial variation. Although the penalization of spatial variation is known to give best results (gradient penalization and second order regularization), there is no physical underlying foundation. Penalization of spatial variations lead to smooth solution that is an equilibrium between good and bad characteristics. Here, we introduce a new approach for regularization of ill-posed inverse problems. Penalization of spatial variations is weighted by an observation based trust function. The result is a generalized diffusion operator that turns regularization into pseudo covariance operators. All the regularization informations are then embedded into a preconditioning operator. On one hand, this method do not need any extra terms in the cost function, and of course is affected neither by the ill-convergence due to composite cost function, nor by the choice of weighting parameters. On the other hand, The trust function introduced here allows to take into account the observation based a priori knowledges on the problem. We suggest a simple definition of the trust function for inverse problems in image processing. Preliminary results show a promising method for regularization of inverse problems

A new approach for regularization of inverse problems in images processing

International audienceOptical flow motion estimation from two images is limited by the aperture problem. A method to deal with this problem is to use regularization techniques. Usually, one adds a regularization term with appriopriate weighting parameter to the optical flow cost funtion. Here, we suggest a new approach to regularization for optical flow motion estimation. In this approach, all the regularization informations are used in the definition of an appropriate norm for the cost function via a trust function to be defined, one does not ever need weighting parameter. A simple derivation of such a trust function from images is proposed and a comparison with usual approaches is presented. These results show the superiority of such approach over usual ones

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

Sensitivity Analysis: A Variational Approach

International audienceA sensitivity analysis is defined by some response function and the variable with respect to which the sensitivity is evaluated. In many cases the observations have errors and it is important to estimate the impact of this error, If the state of the system is retrieved through a variational data assimilation then the observation is found only in the Optimality System (O.S.). Therefore the sensitivity analysis has to be carried out on the optimality system, in that sense sensitivity analysis is a second order property and the O.S. can be considered as a generalized model because it contains all the available information. In this presentation we will see how a sensitivity analysis can be carried out. The method is applied to water pollution. The model is derived from shallow water equations and an equation of concentration of the pollutant, it is discretized using a finite volume method and the sensitivity with respect to the source term of the pollutant is studied

Sensitivity Analysis: A Variational Approach

International audienceA sensitivity analysis is defined by some response function and the variable with respect to which the sensitivity is evaluated. In many cases the observations have errors and it is important to estimate the impact of this error, If the state of the system is retrieved through a variational data assimilation then the observation is found only in the Optimality System (O.S.). Therefore the sensitivity analysis has to be carried out on the optimality system, in that sense sensitivity analysis is a second order property and the O.S. can be considered as a generalized model because it contains all the available information. In this presentation we will see how a sensitivity analysis can be carried out. The method is applied to water pollution. The model is derived from shallow water equations and an equation of concentration of the pollutant, it is discretized using a finite volume method and the sensitivity with respect to the source term of the pollutant is studied

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Assimilation d'images pour les fluides géophysiques

Understanding and forecasting the evolution of geophysical fluids is a major scientific and societal challenge. A good forecast must take into account all available information on the studied system. These informations include models, observations and a priori knowledge. Data assimilation techniques combine all these informations in a consistent way to produce model inputs. During the last decades, many satellites were launched to increase the knowledge of earth. They produce, among others, image sequences showing the dynamical evolution of geophysical processes such as depressions and fronts. These images sequences are currently under-utilized in data assimilation. This thesis presents a consistent approach for taking into account image sequences in variational data assimilation. After a presentation of images, their current use and its limitation, we introduce the concepts of interpretation level, image space and image operator used for direct image sequences assimilation. We also propose a new approach of regularization based on generalized diffusion for ill-posed inverse problems. Preliminary results on image processing and image sequences assimilation show a promising approach that solve most of the problems encountered with classical approaches of regularization.La compréhension et la prévision de l'évolution des fluides géophysiques sont d'une importance capitale et constituent un domaine de recherche scientifique aux enjeux conséquents. Une bonne prévision est basée sur la prise en compte de toutes les informations disponibles sur le système considéré. Ces informations incluent les modèles, les observations et les connaissances a priori. L'assimilation de données permet de les combiner de façon optimale pour déterminer les entrées du modèle. Les dernières décennies ont vu croître en densité et en qualité la couverture satellitaire produisant, entre autres, des séquences d'images montrant l'évolution dynamique de certains phénomènes géophysiques tels que les dépressions et les fronts. Ces séquences d'images sont jusqu'à présent sous-utilisées en assimilation de données. Cette thèse propose une extension de l'assimilation variationnelle de données aux observations de type séquence d'images. Après avoir présenté les images, leur utilisation actuelle et ses limites, nous introduisons les notions de niveau d'interprétation, d'espaces et d'opérateur image. Ces notions sont utilisées pour formuler l'assimilation directe de séquences d'images. Nous proposons également une nouvelle approche de régularisation par diffusion généralisée pour les problèmes inverses. Les résultats préliminaires en traitement d'images et en assimilation directe de séquence d'images montrent une méthode prometteuse qui résout la plupart des problèmes rencontrés avec les approches classiques de régularisation

Assimilation d'images pour les fluides géophysiques

Understanding and forecasting the evolution of geophysical fluids is a major scientific and societal challenge. A good forecast must take into account all available information on the studied system. These informations include models, observations and a priori knowledge. Data assimilation techniques combine all these informations in a consistent way to produce model inputs. During the last decades, many satellites were launched to increase the knowledge of earth. They produce, among others, image sequences showing the dynamical evolution of geophysical processes such as depressions and fronts. These images sequences are currently under-utilized in data assimilation. This thesis presents a consistent approach for taking into account image sequences in variational data assimilation. After a presentation of images, their current use and its limitation, we introduce the concepts of interpretation level, image space and image operator used for direct image sequences assimilation. We also propose a new approach of regularization based on generalized diffusion for ill-posed inverse problems. Preliminary results on image processing and image sequences assimilation show a promising approach that solve most of the problems encountered with classical approaches of regularization.La compréhension et la prévision de l'évolution des fluides géophysiques sont d'une importance capitale et constituent un domaine de recherche scientifique aux enjeux conséquents. Une bonne prévision est basée sur la prise en compte de toutes les informations disponibles sur le système considéré. Ces informations incluent les modèles, les observations et les connaissances a priori. L'assimilation de données permet de les combiner de façon optimale pour déterminer les entrées du modèle. Les dernières décennies ont vu croître en densité et en qualité la couverture satellitaire produisant, entre autres, des séquences d'images montrant l'évolution dynamique de certains phénomènes géophysiques tels que les dépressions et les fronts. Ces séquences d'images sont jusqu'à présent sous-utilisées en assimilation de données. Cette thèse propose une extension de l'assimilation variationnelle de données aux observations de type séquence d'images. Après avoir présenté les images, leur utilisation actuelle et ses limites, nous introduisons les notions de niveau d'interprétation, d'espaces et d'opérateur image. Ces notions sont utilisées pour formuler l'assimilation directe de séquences d'images. Nous proposons également une nouvelle approche de régularisation par diffusion généralisée pour les problèmes inverses. Les résultats préliminaires en traitement d'images et en assimilation directe de séquence d'images montrent une méthode prometteuse qui résout la plupart des problèmes rencontrés avec les approches classiques de régularisation