# Presentation: 691540

## Federal R&D Needs and Priorities for Atmospheric Transport and Diffusion Modeling

- Ninth GMU Conference on Atmospheric Transport & Dispersion Modeling
- 19 July 2005
- Walter D. Bach, Jr.
- Cochair, OFCM  Joint Action Group on Atmospheric Transport and Diffusion Research and Development Plan
- Program Manager, Environmental Sciences Division
- U.S. Army Research Office

***Uncertainty in ATD Modeling***

## The Report

**The Report**

**www.ofcm.gov/r23/r23-2004/fcm-r23.htm**

**  ****Purpose**
**  ****User’s Needs from User’s     ****	****Perspective**
**  ****Model Capabilities to Meet ****	****User’s Needs**
**  ****Modeling and Measurement ****	****Research Needs**
**  ****R&D Strategy to Meet User’s ****	****Needs**
**  ****Recommendations**

## Perspectives

**Decision Maker**
- Safety of people is first
- Need actionable information
- Best, timely, consistent representation of hazard areas over time
- No time to evaluate
- Add a margin of error

**Science / Provider**
- No prediction (model) is perfect
- Not enough data
- Multiple users /decision makers
- Evolution on multiple time and space scales
- Acute and chronic exposure

## An R&D Strategy to Meet User Needs

**COORDINATED AGENCY SUPPORT & FUNDING**

**GOALS**

**MEASUREMENT CAPABILITIES**

**MODEL EVAL  STANDARDS**

**ATD TEST BEDS**

**BRIDGE THE SCALE GAP**

**CAPTURE AND USE EXISTING DATA SETS**

**LOCAL/ REGIONAL MEASUREMENT SITING**

**Routinely Quantify Uncertainty**

**Interpret Uncertainty**

**OBJECTIVES**

## Tennekes’ Challenges

**No observation is complete without an appropriately sampled estimate of the variance of the properties observed.**
**No forecast is complete without a preceding estimate of forecast skill.**
**No model calculation is complete without a calculation of its variance**

## ATD Modeling Uncertainty

- The total model uncertainty is measured by the variance in the ***predicted*** and the ***observed***** **quantity over a large number of events that have similar properties (an ensemble).

## Total Model Variance

- Let **C**o = **C**oa +**C**o  and  **C**p = **C**pa +**C**p

- Square of model bias

- Total model
-  variance

- Error variance of observations

- Stochastic uncertainty

## Contributions to Model Uncertainty

- 	**Internal**: 	Numerical approximations,
- 		 	modeling errors, and
- 			treatment of dynamical processes
- 	**External**:  	Data errors in execution and 				evaluation, model parameterizations, 			and initial & boundary conditions
- 	**Stochastic**:	Natural variability of the atmosphere 			(turbulence)

## A Familiar Chart

## Why Emphasize Uncertainty?

-  Critical to making intelligent life / death  decisions
-  Ubiquitous
-  Poorly characterized in modeling efforts
-  Measure of the robustness of the modeling / observing system.
-  Reducing uncertainty requires knowledge of current uncertainty
-  Believability of our products

## What to Do?

***Capture and Use Existing Data Sets***
-   Basis of present ATD knowledge
-   Accessible
***Establish Test Beds***
-   All-weather conditions (24/7 operation)
-   Daily test and evaluations
-   Feedback among operational modeling and users
***Develop Measurement Technologies***
-   Meteorological variables – T, h, winds, turbulence
-   Tracer materials – cheap, harmless, remote, in situ
***Conduct controlled experiments***
-   Physical modeling – flow channels, wind tunnels, convection tanks
-   Computational modeling – DNS, CFD, LES

## Implementation Actions

- Develop robust techniques to assess probabilities of occurrence of exceeding significant thresholds
- Adapt and verify measurement capabilities at or below scales of model predictions
- Test, verify, and verify model improvements
- Establish a shared data system to test  and evaluate uncertainty quantification techniques
- Quantify and reduce uncertainty in predictions

## Implementation Actions

- Develop applicable analysis techniques to display uncertainty in non-linear regimes
- Use uncertainty analyses to guide improvement in ATD models
- Develop and implement processes for uncertainty displays in data – sparse environments

## Questions?Comments?