The presentation will examine how meteorological conditions affect air pollution and how to take this into account when modelling. What are the important meteorological factors? How do we obtain representative meteorological data?Is an observation site 30 km away always suitable? Should modellers pay more attention to the selection of meteorological datasets? How might climate change affect future modelling? What are NWP data? Should modelled meteorological data be used more? Are surface characteristics important? Does the choice of meteorological data sets have an impact on modelled results? This presentation intends to answer some of these important questions!

1. The weather, a very British topic of
conversation, but what does it mean for
air quality modelling?!
IAQM Routes to Clean Air
12th October 2021

2. Background
A look into meteorological conditions and how they relate to air quality
modelling.
• What is representative meteorological data?
• Do modellers know what they are using when modelling with meteorological
datasets?
• What should be considered for the future?
• What is NWP data? Should modelled meteorological data be used more.
• How important are the model inputs?
• Can low-cost wind sensors provide a benefit for urban modelling?
• Do meteorologic conditions have an impact on model results?
• And so many more questions…
Slide
278
Focus on local scale models

3. Where do we get data from?
Observational data
• Synoptic
• METAR
Modelled dataset – Numerical Weather Predictions (NWP)
• Met office - Unified Model – UK
• European Center for Medium-range Weather Forecast (ECMWF) – European
• National Centers for Environmental Prediction (NCEP) Global Forecast System
(GFS) – US
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279

4. Observational data
Synoptic & METeorological
Aerodrome Report (METAR)
• Synoptic: “Observations made primarily for
the purpose of providing information on the
present state of the atmosphere”
• What is the measurement and what are we
getting?
• Met Office sites vs METAR
Slide
280
Lots of studies have been carried out on sensitivities
Ref: https://www.airpollutionservices.co.uk/meteorological-data/

5. Observational data
Synoptic vs METAR
Slide
281
Ref: https://www.airpollutionservices.co.uk/meteorological-data/

6. Why does it matter?
Wind direction & speed
• Determines the direction of a plume and recirculation zones
Boundary layer
• Mixing and stability is important
Source type
• Low level sources vs elevated sources or high
velocity/buoyancy releases
Model input
• Uncertainty/errors: poor data in = poor results out
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282

7. Representative met data
“The dispersion modelling has been carried out using five years
(2014-2018) … Data from a meteorological station at Doncaster
Airport (approximately 23 km southwest of the Site) has been
used for the assessment, which is the most representative data
currently available for the area.” June 2021.
Not uncommon:
• Nearest observational site
• Some consideration of the site setting
• Missing data e.g. cloud cover, patched in from other
sites even further
• No justification for why it is representative
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283
Ref: Picture - https://www.bruceslutsky.com/2016/08/a-wrong-weather-forecast-messed-up-my.html

8. M1 Tinsley – Highways England (NH)
“Overall, the meteorological dataset for Doncaster
Sheffield (Robin Hood) airport is considered the
most robust, and therefore likely to be reasonably
representative of the whole of the study area”
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284
Ref: Air Quality Geographic Pilot Study – M1 Tinsley – Final Report.pdf May 2018

9. M1 Tinsley cont.
Road sources – direction and speed key
Slide
285
Variation with year
Ref: Air Quality Geographic Pilot Study – M1 Tinsley – Final Report.pdf May 2018

10. M60 - Highways England (NH)
Slide
286
Ref: Air Quality Geographical Pilot Study – Manchester M60 – Final Report.pdf May 2018

11. Point source modelling
• Common to test model
sensitivity to surface
parameters and met
year
• Clear variation
depending on
parameters used and
variation in annual
datasets
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287

12. Modelled data
Numerical Weather Predictions
• Forecasting and hindcasting
• ECMWF ERA5
• Weather Research and Forecasting
(WRF) Model
• First guess – reanalysis data
• Observational data added for
observational nudging
• 3x3 km or 1x1 km
• 3D grid cells
Slide
288
Ref: https://serc.carleton.edu/eet/envisioningclimatechange/part_2.html

13. Bristol
Quick roads model
• Bristol is hilly
• Restricted dispersion
and recirculation in
street zones
• Almost all studies use
Bristol Airport as a
source of met data –
11km SW
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289

14. Bristol cont.
xxxx
• xxx
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290
Observational NWP – 3x3 km
Ref: https://www.airpollutionservices.co.uk/meteorological-data/
Ref: https://www.airpollutionservices.co.uk/meteorological-nwp-data/
Bristol Airport

15. Bristol cont.
Slide
291
Modelled result using:
NWP near Bristol Airport 17.0
Bristol Airport Observation 17.6
NWP at study area 20.7

16. Climate Change and future modelling
Should we be modelling more extremes? More extreme weather
conditions are becoming the norm – how do we model the
future?
What would happen if you verified a model using 3 different
years – using relevant traffic data, monitoring data and
meteorological data for each year. How would the results
change?
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292

17. AQ modelling
Modelling in UK (gaussian plume models)
• Default in UK to using observational data
• Single point used to infer 3D conditions
• Example – ADMS uses a met processor which numerically calculates
boundary layer heights, wind speeds and other parameters:
“Specifying h [boundary layer height] is advisable if you think you can
provide a better estimate than the met input module”
“Most of the assumptions are inexact and suffer from some limitations
and uncertainties.” “the effects of topography and non-uniformities in
the surface properties are neglected.”
Can we do better as modellers?
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293

18. Thoughts
• All opportunities to improvement modelling should be taken or
residual uncertainty discussed
• If using observational datasets, should we be modelling a range of
spatially variable datasets rather than/as well as multiple years
(temporal variability)?
• Large domains – multiple source specific met roses?
• Should NWP data be used more in AQ modelling?
• As a minimum should NWP data be used to validate how
representative a observational dataset is?
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294
Representative wind speed and direction are very important for both point source
modelling and also road modelling

19. Any questions?
contact@airpollutionservices.co.uk
Tel: 01179 112434