Multiscale Data Assimilation Research Articles

Potential of the International Monitoring System radionuclide network for inverse modelling

The International Monitoring System (IMS) radionuclide network enforces the Comprehensive Nuclear-Test-Ban Treaty which bans nuclear explosions. We have evaluated the potential of the IMS radionuclide network for inverse modelling of the source, whereas it is usually assessed by its detection capability. To do so, we have chosen the degrees of freedom for the signal (DFS), a well established criterion in remote sensing, in order to assess the performance of an inverse modelling system. Using a recent multiscale data assimilation technique, we have computed optimal adaptive grids of the source parameter space by maximising the DFS. This optimisation takes into account the monitoring network, the meteorology over one year (2009) and the relationship between the source parameters and the observations derived from the FLEXPART Lagrangian transport model. Areas of the domain where the grid-cells of the optimal adaptive grid are large emphasise zones where the retrieval is more uncertain, whereas areas where the grid-cells are smaller and denser stress regions where more source variables can be resolved.The observability of the globe through inverse modelling is studied in strong, realistic and small model error cases. The strong error and realistic error cases yield heterogeneous adaptive grids, indicating that information does not propagate far from the monitoring stations, whereas in the small error case, the grid is much more homogeneous. In all cases, several specific continental regions remain poorly observed such as Africa as well as the tropics, because of the trade winds. The northern hemisphere is better observed through inverse modelling (more than 60% of the total DFS) mostly because it contains more IMS stations. This unbalance leads to a better performance of inverse modelling in the northern hemisphere winter. The methodology is also applied to the subnetwork composed of the stations of the IMS network which measure noble gases.

Use of Covariance Matrices in a Consistent (Multiscale) Data Assimilation for Improvement of Basic Nuclear Parameters in Nuclear Reactor Applications: from Meters to Femtometers

A new approach is proposed, the consistent data assimilation, that allows to link the integral data experiment results to basic nuclear parameters employed by evaluators to generate ENDF/B point energy files in order to improve them. A practical example is provided where the sodium neutron propagation experiments, EURACOS and JANUS-8, are used to improve via modifications of {sup 23}Na nuclear parameters (like scattering radius, resonance parameters, Optical model parameters, Statistical Hauser-Feshbach model parameters, and Preequilibrium Exciton model parameters) the agreement of calculation versus experiments for a series of measured reaction rate detectors slopes. Future work involves comparison of results against a more traditional multigroup adjustments, and extension to other isotope of interest in the reactor community as {sup 56}Fe, actinides, and fission products.

Bayesian design of control space for optimal assimilation of observations. Part II: Asymptotic solutions

AbstractA consistent formalism for a Bayesian design of control space for an optimal assimilation of observations was proposed in Part I of this two‐part article. This optimal discretization of control space leads to an efficient data assimilation scheme implementation. However, the construction of the grid itself, prior to its use for data assimilation, requires an optimization that may be challenging for high‐dimensional systems. This paper derives analytical solutions for these optimal grids in the limit where the discretization of control space has a large number of grid cells. Analytical solutions for the density of grid cells are obtained for the so‐called tilings, qtrees and ftrees, that represent different types of adaptive grids, with more or fewer degrees of freedom. These analytical solutions are explicit and the algorithms that allow densities to be converted into discrete adaptive grids are costless.The approach is tested with a simplified physics in the Jacobian matrix in a tracer dispersion context in which radionuclides are monitored by the global observation network operated by the Comprehensive Nuclear Test Ban Treaty Organisation of the United Nations. The asymptotic solutions are then compared to the optimal grids obtained from the methodology perfected in Part I. In this example, and using qtree representations, the discrepancy between the approximate solution and the exact solution almost vanishes when the number of grid cells represents as few as 1% of the total number of grid cells in the finest grid. This opens the way to the application of this multiscale data assimilation framework to computationally challenging problems. Copyright © 2011 Royal Meteorological Society

Toward Optimal Choices of Control Space Representation for Geophysical Data Assimilation

In geophysical data assimilation, observations shed light on a control parameter space through a model, a statistical prior, and an optimal combination of these sources of information. This control space can be a set of discrete parameters, or, more often in geophysics, part of the state space, which is distributed in space and time. When the control space is continuous, it must be discretized for numerical modeling. This discretization, in this paper called a representation of this distributed parameter space, is always fixed a priori. In this paper, the representation of the control space is considered a degree of freedom on its own. The goal of the paper is to demonstrate that one could optimize it to perform data assimilation in optimal conditions. The optimal representation is then chosen over a large dictionary of adaptive grid representations involving several space and time scales. First, to motivate the importance of the representation choice, this paper discusses the impact of a change of representation on the posterior analysis of data assimilation and its connection to the reduction of uncertainty. It is stressed that in some circumstances (atmospheric chemistry, in particular) the choice of a proper representation of the control space is essential to set the data assimilation statistical framework properly. A possible mathematical framework is then proposed for multiscale data assimilation. To keep the developments simple, a measure of the reduction of uncertainty is chosen as a very simple optimality criterion. Using this criterion, a cost function is built to select the optimal representation. It is a function of the control space representation itself. A regularization of this cost function, based on a statistical mechanical analogy, guarantees the existence of a solution. This allows numerical optimization to be performed on the representation of control space. The formalism is then applied to the inverse modeling of an accidental release of an atmospheric contaminant at European scale, using real data.

An integrated hydrologic modeling and data assimilation framework

The Land Information System (LIS) is a multiscale hydrologic modeling and data assimilation framework that integrates the use of satellite and ground-based observational data products with advanced land-surface modeling tools to aid several application areas, including water resources management, numerical weather prediction, agricultural management, air quality, and military mobility assessment.

An Integrated Hydrologic Modeling and Data Assimilation Framework

The Land Information System (LIS) is a multiscale hydrologic modeling and data assimilation framework that integrates the use of satellite and ground-based observational data products with advanced land-surface modeling tools to aid several application areas, including water resources management, numerical weather prediction, agricultural management, air quality, and military mobility assessment.

A Multiscale Data Assimilation with the Ensemble Kalman Filter

In this paper, a multiscale data assimilation approach is constructed to evaluate boundary conditions for particle fluxes in numerical simulations of particle transport problems. An adaptation of the ensemble Kalman filtering (EnKF) method is used as the engine for estimation and filtering across scales. To implement this multiscale approach, a multiscale bridging model, which can predict the particle fluxes across arbitrary spatial intervals in any scale, is developed by considering particles undergoing transverse random walk emitted along a continuous boundary corresponding to the finest scale. In the model, particle fluxes across one scale are taken as the parameter set which is used to determine fluxes across another scale. The significance of this multiscale model is demonstrated through an example. Ensembles of Gaussian random processes of particle fluxes along the boundary are generated as the microscale model state according to a specified a priori information on the error covariance or spectral density functions. Measurements of particle quantities are taken at macroscale locations above the boundary and assimilated with model predictions to update the micro- and macroscale particle fluxes using the inverse analysis scheme of the multiscale EnKF approach. The updated random macroscale fluxes can be used as consistent boundary conditions for numerical simulations, such as large eddy simulation.

Mesoscale Modeling and Four-Dimensional Data Assimilation in Areas of Highly Complex Terrain

Abstract A multiscale four-dimensional data assimilation (FDDA) technique, based on Newtonian relaxation, is incorporated into a mesoscale model and evaluated using meteorological and tracer data collected during the Atmospheric Studies in Complex Terrain (ASCOT) field experiment in the winter of 1991. The mesoscale model is used to predict the synoptically driven flows and small-scale circulations influenced by terrain along the Front Range in Colorado in the vicinity of Rocky Flats Plant for four nocturnal periods during the ASCOT field experiment. Data assimilation is used to create dynamically consistent analysis fields based on the mesoscale forecasts and the special asynoptic data taken during this experiment. Observations from towers, minisodars, airsondes, tethersondes, rawinsondes, and profilers near the Rocky Flats Plant, as well as observations from surface stations throughout Colorado, are incorporated into the high-resolution analysis fields. The wind and turbulence quantities produced by the...

A Multiscale Four-Dimensional Data Assimilation System Applied in the San Joaquin Valley during SARMAP. Part I: Modeling Design and Basic Performance Characteristics

Abstract This paper presents results of numerical simulations made with a high-resolution multiscale four-dimensional data assimilation system applied over California during two episodes associated with high ozone concentrations in the San Joaquin Valley. The model used here is the nonhydrostatic Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The focus of the paper is the objective validation of the regional (mesoalpha scale) meteorological results. The mulliscale data assimilation approach produces highly reliable simulations of the wind, temperature, mixed-layer depth, and moisture, each of which is vital to air quality modeling and a host of other mesoscale applications. The significance of this research is threefold. First, it is the first evaluation of this multiscale assimilation system in strongly heated summertime conditions and with comparatively fine grid resolution (4-km inner mesh). Second, the assimilation system has been extended so that tempera...