Mathematical immunology: processes, models and data assimilation

Data assimilation for plume models

We use a four-dimensional variational data assimilation (4D-VAR) algorithm to observe the growth of 2-D plumes from a point heat source. In order to test the predictability of the 4D-VAR technique for 2-D plumes, we perturb the initial conditions and compare the resulting predictions to the predictions given by a direct numerical simulation (DNS) without any 4D-VAR correction. We have studied p...

MODELS Data Assimilation ( Physical / Interdisciplinary )

Data assimilation is a novel, versatile methodology for estimating oceanic variables. The estimation of a quantity of interest via data assimilation involves the combination of observational data with the underlying dynamical principles governing the system under observation. The melding of data and dynamics is a powerful methodology which makes possible efRcient, accurate, and realistic estima...

Mathematical Models and Control of Catastrophic Processes

Data Assimilation in Multiscale Chemical Transport Models

In this paper we discuss variational data assimilation using the STEM atmospheric Chemical Transport Model. STEM is a multiscale model and can perform air quality simulations and predictions over spatial and temporal scales of different orders of magnitude. To improve the accuracy of model predictions we construct a dynamic data driven application system (DDDAS) by integrating data assimilation...

Helioseismic Data Assimilation in Solar Dynamo Models

An essential ingredient in kinematic dynamo models of the solar cycle is the internal velocity field within the simulation domain – the solar convection zone. In the last decade or so, the field of helioseismology has revolutionized our understanding of this velocity field. In particular, the internal differential rotation of the Sun is now fairly well constrained by helioseismic observations a...