atmospheric dispersion and air qualityresearch.metoffice.gov.uk/research/nwp/publications/...title...

© Crown copyright 2005-6

Atmospheric Dispersion and Air Quality

16th November 2006


Purpose

Predicting:Spread from major atmospheric releases

nuclear, chemical, biological, volcanic ash, major fires etcAirborne disease spread

foot and mouth, bluetongue, legionairesAirborne dustRoutine air quality

Identifying source strengths/locations from observations

Policy support to government on dispersion/air quality

Environmental impact assessments


Outline of talk

Model strategy:Models usedWhy more than one modelAdvantages/disadvantagesEulerian v Lagrangian approaches

Progress and plans in specific areas:Emergency responseAir quality in the Unified ModelDevelopment of the NAME modelSource identification Dust


Model strategy

We have three models:NAME – Lagrangian (‘off-line’)UM – EulerianADMS – Gaussian plume ADMS – short range

chemical incidents

NAME – main current model

UM – being developed for dispersion modelling


Model strategy

NAME advantages:Better representation of turbulent dispersionNear source resolution not limited by gridAbility to include plume rise easilyAbility to run ‘backwards’ and compute source-receptor relationships

Cheaper for fast emergency response and long policy support runs

UM advantages:Better access to meteorology (time resolution; internal variables)More effective treatment of species which are ‘everywhere’Possibility of feedbacks on the meteorologyAccess to data assimilation infrastructureClear advantages for routine air quality forecasts


Model strategy

We propose to develop methods of combining the advantages of Lagrangian & Eulerian approaches

initially in NAME– Lagrangian ‘particles’ for near field dispersion– Eulerian advection scheme at long range

and then, if successful, in the UM– Lagrangian sub-models for

near source effectsconvective boundary layersplume rise


NAME modelling of the Buncefield oil depot incident

MSG 12:00UTC 11 Dec

Source details uncertainCompositionQuantity of materialPlume rise

Initial modellingUnit release of tracerUtilising pilot report and satellite imagery to best estimate plume heightSimple elevated source

NAME 12:00UTC 11 Dec


NAME modelling of the Buncefield oil depot incident

Post-event NAME modelling has incorporated

Plume observationsEmission estimates

Rates (~50 kg/s PM10?)PollutantsTime variation

Plume riseEstimated heat flux

Air quality measurements


Using the NAME plume rise scheme

Maximum height of the plume too lowInsufficient vertical spread of the plume

Why?Lofting of the plumeRelease of latent heatComplex sourceInaccurate meteorological representationErrors in plume rise model


What if the explosion had occurred in different meteorological conditions?

Let it burn?Sunday 11th December

Over whole event

Maximum hourly averaged boundary layer PM10

concentrations

Windy conditions

Convective conditions


AQUM: Progress to date

Tropospheric chemistry in UM 6.1 N216 & 12km mesoscale

Lateral boundary conditions for chemical tracers

Testing physical behaviour of tracers in model: mass conservation, maintain uniform mixing ratio

Initial evaluations of chemistry scheme

Urban site comparison with observations

Aug. 2003 pollution episode: CO

N216~60km

12km meso


AQUM: Future Developments

Next 6 months: Interface with GEMS system to enable

running our forecasts using GEMS analysis

feeding our predictions back to GEMS

Begin near real time GEMS forecast delivery summer 07

Link to central GEMS verification system

Develop realistic emissions variations

Review suitability of chemistry scheme for air quality applications

Compare with other off-line and on-line systems

NAME/UM,

EMEP


GEMS Contributions

Met Office lead work to produce verification methodology for GEMS Regional Air Quality sub-project. Recommendations:

Basic field statistics (modified mean bias, fractional gross error and correlation)Taylor diagrams for selected speciesContingency tables and Odds Ratio

skill score for evaluating forecast of exceedanceAssess against persistence

Nor

mal

ised

sta

ndar

d de

viat

ion

(radi

al)

Correlation

rms error

Taylor diagram


NAME: Recent progress

Substantial upgrade (NAME III) now almost complete

Improved short range performancePuff model (to complement particle model)Sub-models – e.g. building model, small scale terrain modelFlexible coordinate systems and grids Flexible output optionsUsed operationally

Improved version of puff model still to do


NAME: Recent progress

Comparison with Kincaid experimentPower station stack in the USAMostly convective met conditions

Normalised mean

square errorFractional

bias CorrelationFraction within a

factor of 2NAME II 1.07 0.180 0.306 0.667NAME III 0.56 –0.072 0.473 0.758

(Comparisons of ground-level centre-line concentrations as function of downwind distance – “quality 3” data only)


NAME: Planned improvements

Deposition – current treatment is poor having been changed little since early versions of NAME

Currently not very species or land use specificMaterial at all heights in boundary layer depositedConvective/dynamic rain differences at increased resolutionPotential to use detailed cloud/rain profilesLinks with Bristol University


NAME: Planned improvements

Urban effects Urban areas of central interest for toxic release and routine air qualityImportance of near and far sources for air quality, and hence of canopy flow/turbulenceNAME has no explicit treatment of urban effects other than those inherited from the UMStatistical representation of urban canopy – semi-empirical mean flow and turbulence profilesWill also make use of UM developments – surface heat flux and effective roughnessLinks with Reading University


Inversion modelling: estimating routine emissions

NAME air history maps used to estimate emissionsEstimates sensitive to

Cost function (choice of best map)MeteorologyTransport (turbulence etc.)Number of observing sites constraining solutionAssumptions (e.g. uniform emissions)

Different cost functions

2 vs. 3 observing sites

Black line = Reported inventoryOther lines = NAME-inversion

emission estimates with different cost functions or number of observing sites


Inversion modelling: locating accidental releases

Identifying source regions using NAME & RIMNET sensor network

Utilise ability to track air mass origins Generate hourly “backmaps” for all 95 RIMNET sitesAssign “hit” or “miss” to RIMNET observationsProcess NAME output to estimate source

location + time of release

Reasonable skill after 3 hoursResults dependent on met + no. of stations hit

Case Study(Blayais power station)

Fictitious event

Source identification


Dust Forecasting: Progress to date

Two experimental forecast systems developed for independent assessment

NAME-basedUM-based (CAMM)

Systems demonstrated in support of flight campaigns over western Africa (DODO/DABEX)

Model improvementsDust uplift scheme modifications

April 2007: Decision regarding system April 2008: Initial operational system

atmospheric dispersion and air qualityresearch.metoffice.gov.uk/research/nwp/publications/...title...

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