Chem/Bio Hazard Assessment and Refinement Through Sensor Data Fusion

1. Chem/Bio Hazard Assessment and Refinement Through Sensor Data Fusion Dr. Paul E. Bieringer FFT-07 VIP Day September 18, 2007

2. Sensor Data Fusion (SDF) Program • Utilizing CBR and meteorological sensor readings and transport and dispersion models to: • Characterize unknown CBR source characteristics • Refine CBR downwind hazard assessment Source Characterization CBR/Met Sensors SDF Refined Downwind Hazard

3. Operational CBR Defense Problem • Scenario • A sensor or sensor network detects CBR materials • Detection is currently used as the source to forecast the downwind impact • The initial forecast may not accurately reflect the actual threat Actual Release Location Actual CBR Plume CBR Sensor Location Sensor Detection Based Plume

4. CBR Obs. Co(t) Adjoint of Eulerian Plume Model L3-Titan SCIPUFF Adjoint Source Estimate (SN) Source Estimate (S1) Eulerian Plume Model (Cp(t)) SCIPUFF Forward Model (Cp(t)) NO J = | Co(t) - Cp(t) |2 J = | Co(t) - Cp(t) |2 J <= T YES Refined Downwind Hazard L3-Titan SCIPUFF SDF Variational Algorithm Design

5. Eulerian Plume and Adjoint Model Source Characteristics Refinement SCIPUFF Source Characterization CBR Obs. Co(t) CBR Obs. Co(t) Adjoint of Eulerian Plume Model L3-Titan SCIPUFF Adjoint Source Estimate (SN) Source Estimate (S1) Eulerian Plume Model (Cp(t)) SCIPUFF Forward Model (Cp(t)) NO NO J = | Co(t) - Cp(t) |2 J = | Co(t) - Cp(t) |2 J <= T J <= T YES YES Refined Downwind Hazard L3-Titan SCIPUFF SDF Variational Algorithm Design Phase II Phase I Hazard Refinement

6. SCIPUFF Source Characterization(Reverse SCIPUFF Source Location Estimate) Location Estimate Max Value Method Location Estimate Comparison Location Estimate Location Estimate Centroid Method Computes the centroid location of the area >= 70% of the maximum value

7. SCIPUFF Source Characterization (Reverse SCIPUFF Mass and Release Time Estimates) • Release time estimated from the time-series of the max release location function • Determine release mass at release time estimate Release Mass Estimate Release Time Estimate

8. Truth Plume SDF Plume Sensor Observations SCIPUFF Source Characterization (Refined Hazard Assessment) Release Time Estimate SCIPUFF Source Estimate Release Mass Estimate Refined Hazard Assessment Release Location Estimate

9. Virtual Threat ResponseEmulation Test Bed (VTHREAT) • Provides the capability to emulate a CBRN attack scenario • Realistic meteorological and transport and dispersion conditions • Meteorological and CBRN sensor data • Observational uncertainties included • Simulated meteorological and CBRN ground truth information • VTHREAT functions • Generate synthetic meteorological environment • Numerical weather prediction (NWP) models (e.g. MM5, WRF, etc.) • Compile an archive of cases • Generate a synthetic stimulant release • Transport and dispersion models (e.g. SCIPUFF, EULAG, etc.) • Extract synthetic meteorological and chem/bio sensor measurements • Utilizing observation and sensor error characteristics • Run SDF algorithms with synthetic observations • Compute skill metrics Goal: Capability to objectively evaluate sensor data fusion (SDF) system performance

10. SDF Test and Evaluation(VTHREAT) CLIENT PC NCAR Synthetic Atmospheric Generator GUI CLIENT Synthetic Meteorology Sensor Emulators MM5/WRF Synthetic CBR Sensor Emulators Windmill Anemometer Synthetic T&D Generator EULAG Aerosol LIDAR Radiosonde LPDM Generic CBR Sensor Synthetic Atmospheric Repository Doppler LIDAR MM5/WRF Simulations Sonic Anemometer EULAG Simulations

11. SDF Algorithm Demo • VTHREAT used to produce a release scenario • 1.4 kg instantaneous release of propylene • Unstable boundary layer • 3 m/s winds perpendicular to the sensor lines • Demo of the SCIPUFF source characterization and hazard refinement LES Model Based Truth Plume

12. SDF Algorithm Performance • Algorithm performance in an operational scenario given most up to date observations

13. SDF Algorithm Performance • Algorithm performance in an operational scenario with continually updating observations

14. Eulerian Puff and Puff Adjoint • SCIPUFF source characterization and hazard assessment • Requires a priori knowledge of release scenario • Provides source characterization of: • Release time • Release mass • Release location • Can be computationally intensive in certain conditions • Large numbers of sensors • High frequency observations • Eulerian puff and puff adjoint model • Does not require a priori knowledge of release scenario • Can provide a source and environmental variable characterization • Release characteristics (time,mass, location) • Relevant environmental characteristics (U and V winds) • Runs efficiently

15. CBR Obs. Co(t) Adjoint of Eulerian Plume Model Source Estimate (SN) Eulerian Plume Model (Cp(t)) NO J = | Co(t) - Cp(t) |2 J <= T YES Refined Downwind Hazard L3-Titan SCIPUFF Eulerian Puff and Puff Adjoint(First Guess Source Estimates for Adjoint Puff Model) Release Time Estimate Eulerian Puff and Puff Adjoint Model Release Mass Estimate First Guess Release Location Estimate

16. J=|Cobs-Cfg|2 J1 Adjoint Forward J2 J3 X2 Eulerian Puff Model Adjoint(Development, Test, and Evaluation) • Sources characterization refinements made using the adjoint of the simple puff model • Completed initial development of adjoint • Completed preliminary adjoint code tests • Validation of accuracy of the tangent linear model • Validation of accuracy of the adjoint model • Gradient check • Idealized tests of adjoint minimization through gradient descent Xt X3 X1

17. Source Estimation Tests(Idealized Observational Data) Wind Speed Estimation Release Mass Estimation Actual release mass: 10 kg First guess release mass: 10 kg Actual wind speeds: U and V = 1m/s, Release mass estimate: 9.3 kg (20 iterations) Wind speed estimate: 1.2 m/s (20 iterations) Actual release mass: 10 kg First guess release mass: 100 kg Release mass estimate: 10 kg (4 iterations) All other parameters known

18. Conclusions • SCIPUFF based SDF prototype algorithm completed • Currently testing and evaluating the algorithm • VTHREAT • FFT07 data • Utilizing variational data assimilation techniques to further refine the source characterization • Ability to assimilate both sampler and meteorological data • Currently does not require a priori knowledge of release scenario • Rapid refinement of hazard assessment • Prototype variational SDF algorithm will be in place by Nov 2007

19. Operational CBR Defense Problem • Scenario • A sensor or sensor network detects CBR materials CBR Sensor Location

20. CBR Sensor Location Sensor Detection Based Plume Operational CBR Defense Problem • Scenario • A sensor or sensor network detects CBR materials • Detection is currently used as the source to forecast the downwind impact

21. Actual Release Location CBR Sensor Location Sensor Detection Based Plume Operational CBR Defense Problem • Scenario • A sensor or sensor network detects CBR materials • Detection is currently used as the source to forecast the downwind impact

22. SDF Algorithm Demo • VTHREAT used to produce a release scenario • 1.4 kg instantaneous release of propylene • Unstable boundary layer • 3 m/s winds perpendicular to the sensor lines • Demo of the SCIPUFF source characterization and hazard refinement

23. Eulerian Puff Model Performance • Utilizes transport and dispersion information from SCIPUFF • Plume dispersion characteristics • Plume motion SCIPUFF Plume Eulerian Puff Model Plume kg/m3 kg/m3