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Cooper and Caulcott Ltd

Environmental Modelling Consultants

How Uncertain are Numerical Models?

Neill Cooper
email: webnsc@cplusc.co.uk

Overheads of a talk given to the NSCA Dispersion Model User Group on 11th May, 2000

Background Issues

    • Models are approximate
    • Data is approximate
    • Validation data is sparse
    • The atmosphere is turbulent
    • Plume behaviour is unpredictable
    • Modelling of single episodes is important

Sources of uncertainty include

    • Input Data Errors
    • Insufficient data
    • Meteorology
    • Emission data (Talk 2)
    • Atmospheric Turbulence
    • Model Simplifications
    • Future weather
    • Spatial and Temporal Averaging

Categories of uncertainty

    • Parameter Uncertainty
    • Conceptual Model Uncertainty
    • Scenario (Future) Uncertainty
    • Variability (Turbulence)

Parameter Uncertainty

    • Tests with a simple ‘R91’ type model
    • Undertake a Monte Carlo study

Uncertainty Study: input values

    • Source Q : 1.0 ± 20%
    • 10m Wind speed u : 5 ± 2 ms-1
    • Boundary layer height A : 750± 250m
    • Roughness length z0 : 0.1 to 0.4m
    • Stability category : B C or D
    • Effective source height z : 25 to 50 m
    • Receptor height H : 10m ± 5m
    • Number of runs : 100

Uncertainty Study: results

    • Concentration 100m downwind varies by a factor of 17.
    • Variation 1 and 10 km downwind about a factor of 5

     

    • Important parameters:
      • 100 m: source height, receptor height
      • 1 km: wind speed
      • 10 km: wind speed; boundary layer height

Conceptual Model Uncertainty

    • Most models use a Gaussian cross-section for plumes.
    • Model intercomparisons show large differences.

Scenario (Future) Uncertainty

    • How often, and for how long, will the site have specific meteorology (e.g. calm conditions) next year? The year after?

Variability - Turbulence

    • The same release in the same meteorology will give different maximum surface concentrations.

Quantifying uncertainty

    • Parameter Uncertainty - gives a factor of 2 on the concentration from a single plume
    • Conceptual Model Uncertainty - ?
    • Scenario (Future) Uncertainty - ?
    • Variability (Turbulence) - factor of 2

How accurate are models?

    • QQ plots show excellent agreement
    • Scatter plots show poorer agreement
    • Validation of ADMS and AERMOD give average differences of about a factor of 2 for single, short term releases.

Conclusions

For a single release, the uncertainty in numerical model results is about a factor of 2 or 3.

© 2000 Neill S Cooper
Last changed on June 12, 2000

Classification of the uncertainty in numerical models can be found here.
Estimates of the uncertainty in atmospheric dispersion models can be found here.
Atmospheric Dispersion Modelling page

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