# Presentation: 340043

## James S. EllisNational Atmospheric Release Advisory Center
Lawrence Livermore National LaboratoryDepartment of Energy (DOE)

***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**

## 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**

## NARAC Model Testing

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

## Staten Island, NY FireFeb. 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

## NARAC Simulation of Smoke Dispersion From Staten Island Fire

- Simulated smoke plume

- Fire location

## NARAC Simulation of Smoke Dispersion From Staten Island Fire

**NARAC Simulation of Smoke Dispersion ****From Staten Island Fire**

**NARAC Simulation of Smoke Dispersion ****From Staten Island Fire**

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

**Satellite image showing smoke plume**

**Photograph of smoke plume ****looking South from Manhattan**

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

## 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

**Notes:**
Analytic solutions
“Are equations being solved correctly?”
Test numerical methods and adaptive time step algorithms versus known solutions 
Component testing
Limitation: Simplifying assumptions required to obtain analytic solutions
Field experiments
“Are correct equations being solved?”
Evaluate validity of model equations and parameterizations for realistic conditions
Multiple space scales: microscale (e.g., Prairie Grass), mesoscale (e.g, Savannah River MATS), long-range (e.g., ETEX). 
Limitations: 
Limited number of processes included (e.g., only transport and diffusion of trace gas)
Limited range of source, meteorological and geographical conditions studied
Operational testing
Test usability, efficiency and robustness of models 

## 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)

**Notes:**
Analytic solutions
“Are equations being solved correctly?”
Test numerical methods and adaptive time step algorithms versus known solutions 
Component testing
Limitation: Simplifying assumptions required to obtain analytic solutions
Field experiments
“Are correct equations being solved?”
Evaluate validity of model equations and parameterizations for realistic conditions
Multiple space scales: microscale (e.g., Prairie Grass), mesoscale (e.g, Savannah River MATS), long-range (e.g., ETEX). 
Limitations: 
Limited number of processes included (e.g., only transport and diffusion of trace gas)
Limited range of source, meteorological and geographical conditions studied
Operational testing
Test usability, efficiency and robustness of models