unclassified assimilating concentration data into dispersion models with a genetic algorithm sue...

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Assimilating Concentration Data into Dispersion Models

with a Genetic AlgorithmSue Ellen HauptKerrie J. Long

Anke Beyer-LoutGeorge S. Young

7th Conference on Artificial Intelligence Applications to Environmental Science Phoenix, AZ - January 12, 2009

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Assimilation to Refine Hazard Areas

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Assimilation Theory

Dynamical Prediction System:

Assimilation Process:

Objectives:1. Determine realization characteristics2. Assimilate data into forecast

Can separate into wind and concentration equations

t

x

Mx η

( )Gt

0 fx

Mx η x ,x

( , , )o fvG C C

t

o f

v v

vM (v)v η v , v

( ) ( , , )o fC

CC G C C

t

o f

C CM v η v , v

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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

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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

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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

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Comparison

Exact Solution

FEWN

GA-Var

Anke Beyer-Lout

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Comparison

Wind Direction Puff Centroid

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Sensitivity to Resolution

FEWN GA-Var

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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

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The Shallow Water Assimilation: TusseyPuff

2-D shallow water model

Gaussian Puff model

Wind field

Concentration field

TusseyPuff

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Identical Twin Experiment

• Random observation stations

• Add noise (5%)

• Fit Gaussian distribution to observed concentrations

• Observations: wind, puff location, amplitude and

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GA-Var Results

13

Me

an

Ab

solu

te E

rror

Sigma LocationSource Strength

Time (s) Time (s) Time (s)M

agn

itu

de

of

Lo

cati

on

Err

or

[m]

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GA-Var in TusseyPuff Conclusions

• GA-Var can recovered time dependent puff parameters:

X-locationY-location Sigmasource strength

• Assimilate concentration observations by using wind field observations despite one-way coupling

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Conclusions

GA-Var is a useful technique for assimilating concentration data into0 a time-varying wind field

Process involves:

1. Determine the realization characteristics2. Assimilate the data into that current prediction

Can recover wind information given concentration observations

Produces better concentration predictions Could have broader applicability

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Questions?