DassFlow v1.0: a variational data assimilation software for river flows

Dassflow is a computational software for river hydraulics (floods), especially designed for variational data assimilation. The forward model is based on the bidimensional shallow-water equations, solved by a finite volume method (HLLC approximate Riemann solver). It is written in Fortran 95. The adjoint code is generated by the automatic differentiation tool Tapenade. Thus, Dassflow software includes the forward solver, its adjoint code, the full optimization framework (based on the M1QN3 minimization routine) and benchmarks. The generation of new data assimilation twin experiments is easy. The software is interfaced with few pre and post-processors (mesh generators, GIS tools and visualization tools), which allows to treat real data

The Coupled Multi-scale Downscaling Climate System : a decision-making tool for developing countries

International audienceThe purpose of this paper is to introduce and evaluate a high-resolution (5km) atmospheric downscaling system. The NCEP-NCAR Reanalysis and the NCEP-DOE Reanalysis II are used to drive a 30 years multi-scale simulation. We report in this paper the different steps for the implementation of the downscaling strategy. Our system has been applied to the east coast of South America with a specific emphasis put on Uruguay. We validate the results by carrying out a direct comparison with the available historical in situ observations. We show that our simulation develops a good representation of the seasonal and interannual variability for temperature and winds in the surface layer. The methodology presented here provides a basis for generalizing this approach to future studies especially for regions with a very limited observational network. Moreover we are on the path to include more precise informations about the sea-state by adding a wave model in our downscaling system

Variational data assimilation for 2D fluvial hydraulics simulations

International audienceA numerical method for model parameters identification is presented for a river model based on a finite volume discretization of the bidimensional shallow water equations. We use variational data assimilation to combine optimally physical information from the model and observation data of the physical system in order to identify the value of model inputs that correspond to a numerical simulation which is consistent with reality. Two numerical examples demonstrate the efficiency of the method for the identification of the inlet discharge and the bed elevation. An application to real data on the Pearl River for the identification of boundary conditions is presented

T2DInverse: Towards calibration and sensitivity analysis into Telemac2D using automatic differentiation

The industrial system Telemac2D, developed by LNHE-EDF and commercialized by Sogreah Co., is an software dedicated to the hydrodynamical and environmental modelling of maritime and river flows. It is based on finite element method and is written in Fortran 90. Our final objective is to include a full variational data assimilation process in the system. This requires the development of an adjoint model. We plan to obtain it using the Automatic Differentiation (AD) tool Tapenade v2.0. In this study, we prepare and validate a reduced version of Telemac2D that is both meant for river flows and suitable to Tapenade v2.0. Then, we establish a strategy of AD according to limitations of the current release of Tapenade (v2.0). Also, the optimization chain is achieved thanks to the unconstrained minimizer N1QN3 based on a quasi-Newton type method. The final objective is to exploit the fluvial measurements and observations in an optimal manner in Telemac2D, in order to identify the partially known or lacking parameters (bathymetry, bed friction, inflow discharge and initial state)

DassFlow v1.0: a variational data assimilation software for river flows

Dassflow is a computational software for river hydraulics (floods), especially designed for variational data assimilation. The forward model is based on the bidimensional shallow-water equations, solved by a finite volume method (HLLC approximate Riemann solver). It is written in Fortran 95. The adjoint code is generated by the automatic differentiation tool Tapenade. Thus, Dassflow software includes the forward solver, its adjoint code, the full optimization framework (based on the M1QN3 minimization routine) and benchmarks. The generation of new data assimilation twin experiments is easy. The software is interfaced with few pre and post-processors (mesh generators, GIS tools and visualization tools), which allows to treat real data

Dassflow :a Direct and Adjoint model for 2D Shallow Water flows

The Dassflow software is designed to solve the bidimensional Shallow Water equations used for the numerical modeling of river hydraulics flows. It was written to carry out variational data assimilation experiments. Based on a finite volume discretization of the Shallow Water equations, the code is written in Fortran 90. The adjoint code necessary to compute the partial derivatives of a cost function of the state variables w.r.t. the control variables of the model was developed using the automatic differentiation tool Tapenade. We present the considered equations, the theoretical bases of variational data assimilation, the numerical scheme used for solving the equations as well as the implementation of the adjoint code. Finally, we present twin experiments of data assimilation performed with Dassflow

Variational data assimilation for 2D fluvial hydraulics simulations

International audienceA numerical method for model parameters identification is presented for a river model based on a finite volume discretization of the bidimensional shallow water equations. We use variational data assimilation to combine optimally physical information from the model and observation data of the physical system in order to identify the value of model inputs that correspond to a numerical simulation which is consistent with reality. Two numerical examples demonstrate the efficiency of the method for the identification of the inlet discharge and the bed elevation. An application to real data on the Pearl River for the identification of boundary conditions is presented

Identification of equivalent topography in an open channel flow using Lagrangian data assimilation

International audienceWe present a Lagrangian data assimilation experiment in an open channel flow above a broad-crested weir. The observations consist of trajectories of particles transported by the flow and extracted from a video film, in addition to classical water level measurements. However, the presence of vertical recirculations on both sides of the weir actually conducts to the identification of an equivalent topography corresponding to the lower limit of a surface jet. In addition, results on the identification of the Manning coefficient may allow to detect the presence of bottom recirculations

Prediction of Response to Temozolomide in Low-Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics

International audienceBoth molecular profiling of tumors and longitudinal tumor size data modeling are relevant strategies to predict cancer patients' response to treatment. Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy. The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide. Using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, the model was able to predict the duration and magnitude of response, especially in those patients in whom repeated assessment of tumor response was obtained during the first 3 months of treatment. Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas. WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? þ First-line temozolomide is frequently used to treat low-grade gliomas (LGG), which are slow-growing brain tumors. The duration of response depends on genetic characteristics such as 1p/19q chromosomal codeletion, p53 mutation, and IDH mutations. However, up to now there are no means of predicting, at the individual level, the duration of the response to TMZ and its potential benefit for a given patient. • WHAT QUESTION DID THIS STUDY ADDRESS? þ The present study assessed whether combining longitudinal tumor size quantitative modeling with a tumor's genetic characterization could be an effective means of predicting the response to temozolomide at the individual level in LGG patients. • WHAT THIS STUDY ADDS TO OUR KNOWLEDGE þ For the first time, we developed a model of tumor growth inhibition integrating a tumor's genetic characteristics which successfully describes the time course of tumor size and captures potential tumor progression under chemotherapy in LGG patients treated with first-line temozolomide. The present study shows that using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, it is possible to predict the duration and magnitude of response to temozolomide. • HOW THIS MIGHT CHANGE CLINICAL PHARMACOLOGY AND THERAPEUTICS þ Our model constitutes a rational tool to identify patients most likely to benefit from temozolomide and to optimize in these patients the duration of temozolomide therapy in order to ensure the longest duration of response to treatment. Response evaluation criteria such as RECIST—or RANO for brain tumors—are commonly used to assess response to anticancer treatments in clinical trials. 1,2 They assign a patient's response to one of four categories, ranging from " complete response " to " disease progression. " Yet, criticisms have been raised regarding the use of such categorical criteria in the drug development process, 3,4 and regulatory agencies have promoted the additional analysis of longitudinal tumor size measurements through the use of quantitative modeling. 5 Several mathematical models of tumor growth and response to treatment have been developed for this purpose. 6,7 These analyses have led to th

Assimilation de données lagrangiennes pour la simulation numérique en hydraulique fluviale

This work concerns the variational assimilation of lagrangian data in river hydraulics, for the identification of parameters in a numerical model relying on the shallow water equations. We propose to take into account Lagrangian data such as trajectories of particles transported on the surface of the flow, in addition to classical eulerian observations which are sometimes not sufficient. The advantages of this approach to improve the identification of parameters are demonstrated through a series of numerical experiments using synthetic data as well as real data from an open channel flow where trajectories are extracted from a video.Ce travail porte sur l'assimilation variationnelle de données lagrangiennes en hydraulique fluviale, pour l'identification de paramètres dans un modèle numérique de rivière basé sur les équations de Saint-Venant, mise oeuvre dans le logiciel Dassflow. Nous proposons de prendre en compte des observations de nature Lagrangienne, comme des trajectoires de particules transportées à la surface de l'écoulement, en plus des observations classiquement disponibles, parfois insuffisantes. L'intérêt de cette approche pour améliorer l'identification de certains paramètres est mis en évidence à travers une série d'expériences numériques utilisant soit des données synthétiques, soit des données réelles issues d'un écoulement en canal, où des trajectoires sont extraites d'une séquence vidéo