Source Reconstruction

1. Source Reconstruction CRTI-02-0093RD Project Review Meeting Canadian Meteorological Centre August 22-23, 2006

2. Component 6: Inverse Source Determination and Bayesian Inference Bayesian inference for inverse source determination urbanBLS urbanAEU Adaptive sampling strategy PSTP Component 6

3. Motivation for Source Reconstruction • Localization of leakage of toxic gases and other pollutants (regulatory application) • Terrorist incidents – localization of unknown source following event detection by network of CBR sensors (“electronic noses”) as quickly as possible • Comprehensive Nuclear Test Ban Treaty (CTBT) – “sniffing” out clandestine nuclear tests (133Xe network of electronic noses) • Source characteristics • emission rate • spatial location • on/off times • number of sources Putative release event(s) Observations from sensors (electronic noses) Source reconstruction

4. Bayesian Inference: Foundations Stochastic uncertainty Source-receptor relationship Model error Co Cp Forward Map: G ― Observation error Input error Noise: Posterior • input uncertainty • (meteorology) • model errors • stochastic uncertainty • observation error Bayesian Inference Likelihood (Probabilistic data fitting) Estimate Prior Noise: Bayes’ rule Posterior distribution for source parameters: prior × likelihood inference

5. Application of Bayesian Inference for Inverse Source Determination Assumed source distribution: where Source parameter vector Infer source parameter vector using Bayes theorem: • need to specify likelihood and prior to define posterior PDF for source parameters

6. Source-Receptor Relationship Eulerian Known receptor Unspecified source Problem: Determine the concentration at fixed receptor location, for an arbitrary unspecified source distribution Duality relation urbanAEU Receptor oriented approach

7. Design of Backward Lagrangian Stochastic Model • Constraints imposed by the duality relation between the forward and backward transition PDFs on the coefficients of LS models: (Source-oriented approach) (Receptor-oriented approach) Principal Result: The duality between the forward and backward transition PDFs imply that the backward drift and diffusion coefficients are related to the forward drift and diffusion coefficients as follows (converse is also true): urbanBLS-1

8. Examples of Source Reconstruction • Joint Urban 2003 (JU2003) • Real cityscape (highly disturbed flow) • European Tracer Experiment (ETEX) • Non-stationary, inhomogeneous flow over complex terrain • Long-range dispersion on continental scales

9. Joint Urban 2003

10. Dual Concentration Field C* urbanAEU Detector # 515 (1-km sampling arc) log C* [C] = pptv [Q] = kg s-1

11. Example 1: (4 detectors) Actual source location: (xs, zs) = (3.2506,1.5537) 74 Distributed drag force representation N – estimated source location at one standard deviation: Detector Source

12. Example 1: (4 detectors) Actual xs: Actual Q : 3.2506 2.00 g s-1 – estimated source parameters at one standard deviation: Actual zs : 1.5537

13. European Tracer Experiment

14. Inverse Source Determination for ETEX • Concentration data extracted only from 10 sampling sites out of a total of 168 sampling sites • Only 35 concentration time samples out of the total available 5,040 concentration samples were utilized for inversion (0.69% utilization of available data) Sampling sites: F02: Alencon [2] F19: Paris Orly [4] F21: Rennes [2] D10: Essen [4] D13: Offenbach [5] D19: Hof [3] D34: Nurburg [7] D44: Trier-Petrisberg [4] Map adapted from Platt et al. (2004) D45: Wasserkuppe [3] CR04: Temelin [1]

15. Meteorological Data for ETEX • Global Environmental Multiscale (GEM) model was executed in regional configuration with core resolution of 0.14° over Europe • GEM produced a series of 3 and 6 h forecasts over the period of time corresponding to ETEX releases • Initial data for GEM came from CMC Global data assimilation system • Series of forecasts was used to “drive” backward LS particle model (MLPD-0) for calculation of C* (viz., GEM model outputs were used as “smart interpolator” of meteorological fields in space and time) • C*fields were computed on a polar stereographic 229  229 grid over Europe (including UK) with a 15 km mesh length

16. Example 1: Results • estimate of source location at one standard deviation Actual source location:

17. Example 1: Results • estimate of source on/off times at one standard deviation Actual source on/off times:

18. Example 1: Results Source on Actual source on/off times: HPD interval (or, credible interval) (97.5% probability content) Source off (lower,upper) Normalized Posterior PDFs of source on/off times

19. Example 1: Results Normalized Posterior PDF of source strength Q Actual source strength: HPD interval (or, credible interval) (97.5% probability content) (lower,upper) • estimate of source strength at one standard deviation

20. Conclusions • Bayesian inference applied successfully to source reconstruction in complex environments involving highly disturbed wind fields (meteorological complexity) • Methodology allows optimal estimates of source parameters along with their reliabilities, fully accounting for model and data uncertainty • Future effort will extend methodology to more complex source configurations • Multiple sources • Area/volume sources • Moving sources

21. FUsing Sensor Information from Observing Networks (FUSION) Field Trial 2007 (FFT 07) Objective: Generate a comprehensive tracer, meteorological and sensor dataset suitable for testing of current and future Sensor Data Fusion algorithms. • Provide an abundance of tracer sensors and met instruments rather than an “optimal” placement. Sparser data sets can be had by ignoring unwanted measurements. • Provide a variety of source types, strengths and locations. Include simultaneous emissions from different locations.

22. Approach Combine a Unique and Synergistic Set of Instrumentation in a Single Test Series that includes • Multiple and extended sources • High resolution (spatial and temporal) concentration sampling • Limited vertical sampling • Potential 4-D sampling • DPG DIAL and FTIR • Aerospace FTIR • High resolution (spatial and temporal) meteorological measurements • Including vertical measurements (towers and profilers) • Regular sampler grid • Dense sensor spacing for data denial studies

23. 1 km 1 km 2 km Details of the Proposed FUSION Test-bed 32 meter towers with sonics and other sensors at five levels (2, 4, 8, 16 and 32m). Propylene release areas 100 dPID propylene samplers in 1 km square grid for high temporal resolution (1 Hz) of propylene concentration data. 25 3D sonics for high temporal (10 Hz) resolution wind data close to high resolution samplers. 20 PWIDs to display real-time winds on the test bed Other Possible Instrumentation (locations TBD): DPG SAMS sites, DPG 924-MHz radar wind profiler and mini-sodars, DPG FM/CW boundary layer radar, Net SW/LW radiometers, DPG tethersonde, Aerospace FTIR, MIRAN detectors. 23 UK UVICS on the down-wind perimeter with high sensitivity (10 ppb) and temporal resolution (1-10 Hz) in the region of lowest expected concentration.