HR: 0830h
AN: NG11A-0169    [PDF]
TI: Data Assimilation for Plume Models
AU: * DeRosier, S
EM: sderosie@msi.umn.edu
AF: Department of Geology and Geophysics, University of Minnesota, 310 Pillsbury Drive SE, Minneapolis, MN 55455 United States
AU: * DeRosier, S
EM: sderosie@msi.umn.edu
AF: Minnesota Supercomputing Institute, University of Minnesota, 117 Pleasant Street SE, Minneapolis, MN 55455 United States
AU: B\'{e}langer, E
EM: belanger@astro.umontreal.ca
AF: D\'{e}partement de Physique, Universit\'{e} de Montr\'{e}al, C.P. 6128, Succ. Centre-Ville, Montr\'{e}al, QC H3C 3J7 Canada
AU: B\'{e}langer, E
EM: belanger@astro.umontreal.ca
AF: Centre de recherche en calcul appliqu\'{e}, 5160, boulevard D\'{e}carie, bureau 400, Montr\'{e}al, QC H3X 2H9 Canada
AU: Hier Majumder, C A
EM: cathy@msi.umn.edu
AF: Department of Geology and Geophysics, University of Minnesota, 310 Pillsbury Drive SE, Minneapolis, MN 55455 United States
AU: Hier Majumder, C A
EM: cathy@msi.umn.edu
AF: Minnesota Supercomputing Institute, University of Minnesota, 117 Pleasant Street SE, Minneapolis, MN 55455 United States
AU: Vincent, A P
EM: vincent@astro.umontreal.ca
AF: D\'{e}partement de Physique, Universit\'{e} de Montr\'{e}al, C.P. 6128, Succ. Centre-Ville, Montr\'{e}al, QC H3C 3J7 Canada
AU: Vincent, A P
EM: vincent@astro.umontreal.ca
AF: Centre de recherche en calcul appliqu\'{e}, 5160, boulevard D\'{e}carie, bureau 400, Montr\'{e}al, QC H3X 2H9 Canada
AU: Yuen, D A
EM: davey@krissy.geo.umn.edu
AF: Department of Geology and Geophysics, University of Minnesota, 310 Pillsbury Drive SE, Minneapolis, MN 55455 United States
AU: Yuen, D A
EM: davey@krissy.geo.umn.edu
AF: Minnesota Supercomputing Institute, University of Minnesota, 117 Pleasant Street SE, Minneapolis, MN 55455 United States
AB: Numerical forecasting of environmental flows is limited by the degree of accuracy to which the initial conditions are known. Even when initial conditions are reasonably well-known, noise and other effects can also significantly influence the outcome of a simulation. A case in point is weather prediction. Variational data assimilation is a well-proven method in meteorology and oceanography that can decrease the effect of inaccuracies in the initial conditions by integrating observations back into the simulation as it proceeds in time. Here 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 plumes in fluids with a Prandtl number characteristic of water and Rayleigh numbers between 10$^{6}$ and 10$^{7}$, and we find the quality of the prediction to have a definite dependence on the Rayleigh number. As the Rayleigh number is increased, so is the quality of the prediction, due to an increase of the inertial effects in the adjoint equations for momentum and energy. The horizon predictability time, or how far into the future the 4D-VAR method can predict, slightly decreases as Rayleigh number increases, however. We have monitored the 4D-VAR predictions for various Prandtl numbers between 0.1 and 100. We have also studied the 4D-VAR predictions of 2-D thermal convection in fluids with a Prandtl number characteristic of the mantle. These 2-D computations are not computationally intensive and allow us to examine many different physical effects, such as Prandtl and Rayleigh numbers and eventually effects of compressibility.
DE: 3200 MATHEMATICAL GEOPHYSICS (New field)
DE: 3210 Modeling
DE: 3220 Nonlinear dynamics
DE: 3230 Numerical solutions
SC: Nonlinear Geophysics [NG]
MN: 2003 Fall Meeting