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James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric Transport and Dispersion Modeling Support for Homeland Security Panel 3: Developing a Common Framework for Model Evaluation George Mason University Fairfax, Virginia June 19, 2003

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Page 1: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

James S. EllisNational Atmospheric Release Advisory Center

Lawrence Livermore National LaboratoryDepartment of Energy (DOE)

OFCM Special Session

Atmospheric Transport and Dispersion Modeling Support for Homeland Security

Panel 3: Developing a Common Framework for Model Evaluation

George Mason University Fairfax, Virginia

June 19, 2003

Page 2: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

JE.CrystalCityEval.12/5/01.lp.Pg.2

Model Evaluation Methodologies

Apply analytic solutions to check numerical methods against known solutions

Use tracer field experiments to evaluate validity of models for real-world cases

• Limitations: — Limited number of processes included (e.g., only transport and

diffusion of trace gas)— Limited range of source, meteorological and geographical conditions

studied

Test operational system for ease of use, efficiency and robustness

Open literature publication and public availability of the model to allow for scrutiny by the scientific and user communities

Evaluate in operational mode or environment

Evaluate for unknown source term with tracer material measurements

Page 3: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

NARAC Model Testing

JN.LasVegas.10/16/2000.lp. Pg.3

• Operational testing evaluates the usability, efficiency, consistency and robustness of models for operational conditionsExamples: Chernobyl, Kuwait oil fires, tire fires, industrial accidents, Algeciras Spain Cesium release, Tokaimura criticality accident

• Analytic solutions test models versus known, exact results

•Field experiments test models in real-world cases Examples: Project Prairie Grass, Savannah River Mesoscale Atmospheric Tracer Studies, Diablo Canyon Tracer Study, URBAN

JE.CrystalCityEval.12/5/01.lp.Pg.3

Page 4: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

JE.CrystalCityEval.12/5/01.lp.Pg.4

Staten Island, NY FireFeb. 21, 2003

Through the NYC Office of Emergency Management, LINC/NARAC provided the following operational assistance:

• Real-time 3-D plume prediction of affected areas and population

• Guidance for deployment of emergency responders and field monitoring teams

• Health risk levels for 1-hr and 24-hr exposure (in consultation with NOAA Hazardous Material Response and Assessment Division, Seattle, team)

• Forecasts of plume spread over next 24 hours

• NARAC Plumes exported to NYC OEM GIS

Page 5: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

NARAC Simulation of Smoke Dispersion From Staten Island Fire

Simulated smoke plume

Simulated smoke plume

Fire locationFire location

Page 6: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

NARAC Simulation of Smoke Dispersion From Staten Island Fire

Page 7: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

NARAC Simulation of Smoke Dispersion From Staten Island Fire

Page 8: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

NARAC Simulation of Smoke Dispersion From Staten Island Fire

Page 9: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

JE.CrystalCityEval.12/5/01.lp.Pg.9

Staten Island, New York, Smoke Plume: Feb. 21, 2003

NOAA

Satellite image showing smoke plume

Photograph of smoke plume looking South from Manhattan

Page 10: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

JE.CrystalCityEval.12/5/01.lp.Pg.10

Staten Island, Fire Smoke Plume: Satellite Image (1130-1300)

Page 11: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

JE.CrystalCityEval.12/5/01.lp.Pg.11

Evaluate in Operational Mode or Environment

User wants to know performance in real-world events

Establish a number of baseline sites (e.g., city, coastal, mountain, plains)

Sites would have advanced observational instrumentation to “well” characterize the atmosphere on an ongoing basis

Occasional tracer releases at various times and locations within site area

Evaluate plume modeling system with full, subsets and predicted data sets

Evaluate for multiple time and spatial scales and resolutions, scenarios, and atmospheric conditions

Evaluate observational data needs

Page 12: James S. Ellis National Atmospheric Release Advisory Center Lawrence Livermore National Laboratory Department of Energy (DOE) OFCM Special Session Atmospheric

JE.CrystalCityEval.12/5/01.lp.Pg.12

Evaluate for unknown source term with tracer material measurements

In real-world WMD event source term is usually not known

Use meteorological and tracer measurement data sets from sites to

evaluate plume modeling system’s ability to reconstruct event

Evaluate for accuracy and speed

Evaluate effects of uncertainties in data

Evaluate sensitivity to various users (reproducibility)