UNCLASSIFIED Assimilating Concentration Data into Dispersion Models with a Genetic Algorithm Sue Ellen Haupt Kerrie J. Long Anke Beyer-Lout George S. Young 7 th Conference on Artificial Intelligence Applications to Environmental Science Phoenix, AZ - January 12, 2009

UNCLASSIFIED Assimilation to Refine Hazard Areas

UNCLASSIFIED Assimilation Theory Dynamical Prediction System: Assimilation Process: Objectives: 1.Determine realization characteristics 2.Assimilate data into forecast Can separate into wind and concentration equations

UNCLASSIFIED GA-Var Assimilation Procedure Concentration Assimilation 1.Use “guessed” wind and source data to predict concentration. 2.Compute difference (innovation) between concentration prediction and observation. 3.Use GA-Var to update wind and source variables. Repeat until converged dynamically assimilate one time before going on to next time

UNCLASSIFIED Meandering Plume We wish to assimilate a puff in a meandering wind field to reconstruct time dependent wind by assimilating observations of dispersed contaminant concentrations

UNCLASSIFIED Problem Set-Up Experimental Design Identical Twin Sinusoidally varying wind field Puff dispersion Source characteristics are known Seek to compute wind direction given concentration observations Techniques 1.Genetic Algorithm Variational (GA-Var) Field based Eulerian 2.Feature Extraction with Nudging (FEWN) Entity Based Lagrangian

UNCLASSIFIED Comparison Exact Solution FEWN GA-Var Anke Beyer-Lout

UNCLASSIFIED Comparison Wind Direction Puff Centroid

UNCLASSIFIED Sensitivity to Resolution FEWN GA-Var

UNCLASSIFIED 10 Meandering Puff Conclusions Both Entity and Field approaches are useful for assimilating wind from concentration observations GA is useful for minimizing difficult cost function GA can be used within the variational formalism: GA-Var

UNCLASSIFIED The Shallow Water Assimilation: TusseyPuff 2-D shallow water model Gaussian Puff model Wind field Concentration field TusseyPuff

UNCLASSIFIED Identical Twin Experiment Random observation stations Add noise (5%) Fit Gaussian distribution to observed concentrations Observations: wind, puff location, amplitude and

UNCLASSIFIED GA-Var Results 13 Mean Absolute Error Sigma Location Source Strength Time (s) Magnitude of Location Error [m]

UNCLASSIFIED GA-Var in TusseyPuff Conclusions GA-Var can recovered time dependent puff parameters:  X-location  Y-location  Sigma  source strength Assimilate concentration observations by using wind field observations despite one- way coupling 14

UNCLASSIFIED Conclusions  GA-Var is a useful technique for assimilating concentration data into0 a time-varying wind field  Process involves: 1.Determine the realization characteristics 2.Assimilate the data into that current prediction  Can recover wind information given concentration observations  Produces better concentration predictions  Could have broader applicability

UNCLASSIFIED Questions?