Relationships in errors between meteorological forecasts and air quality forecasts

Email: K.M.Milczewska@pgr.reading.ac.uk

Exposure to pollutants in the air we breathe may trigger respiratory problems. Pollutants such as ozone (O_{3}) and particulate matter (PM_{2.5}) – particles of about 1/20th of the width of a hair strand – can get into our lungs and cause inflammation, alter their function, or otherwise cause trouble for the cardiovascular system – especially in people with existing underlying respiratory conditions. Although high pollution episodes in the UK are infrequent, the public becomes aware of the associated problems during events such as red skies, in part caused by long-range transport of Saharan dust. Furthermore, the World Health Organisation (WHO) estimates that 85% of UK towns regularly exceed the safe annual PM_{2.5} limit. It is therefore important to forecast surface pollution concentrations accurately in order to enable the public to mitigate some of those adverse health risks.

Figure 1: Smog in London (December 1952). This 5-day event caused many deaths attributable to elevated concentrations of pollutants. The Clean Air Act of 1956 followed. Credit: TopFoto / The Image Works.

In general, air pollution can be difficult to forecast near the surface because of the multitude of factors which affect it. Incorrectly modelling chemical processes within the atmosphere, surface emissions or indeed the meteorology can lead to errors in predicting ground-level pollution concentrations. It is well accepted within the literature that weather forecasting is of decisive importance for air quality. Thus, my PhD project tries to link forecast errors in meteorological processes within the atmospheric boundary layer (BL) with forecast errors in pollutants such as O_{3} and NO_{2} (nitrogen dioxide) using the operational air quality forecasting model in the UK, the Air Quality in the Unified Model (AQUM). This model produces an hourly air quality forecast issued to the public by DEFRA in the form of a Daily Air Quality Index (DAQI) and is verified against surface-based observations from the Automatic Urban and Rural Network (AURN).

Figure 2: Automatic Urban and Rural Network (AURN) ground-based measuring sites for O_{3} and NO_{2}.

A three-month evaluation of hourly forecasts from AQUM shows a delay in the average increase of the morning O_{3} + NO_{2} (‘total oxidant’) concentrations when compared to AURN observations. We also know that BL depth is important for the mixing of pollutants – it acts as a sort of lid on top of the lower part of the troposphere. Since the noted lag in total oxidant increase in our model occurs exactly at the time of the morning BL development, we can form a testable hypothesis: that an inaccurate representation of BL processes – specifically, morning BL growth – leads to a delay in entrainment of O_{3}-rich air masses from the layer of air above it: the residual layer. It has been suggested in the literature that when the daytime convective mixed layer collapses upon sunset, the remaining pollutants are effectively trapped in the leftover (‘residual’) layer, and thus can act as a night-time reservoir of O_{3} above the stable or neutral night-time boundary layer (NBL).

Figure 3: Total oxidant (O_{3} + NO_{2}) average forecast (AQUM, red) and observations (AURN, black) diurnal cycle, averaged over JJA 2017 at 48 urban background sites. Shading is inter-quartile range.
Figure 4: Rate of change of the mean diurnal profile of the forecast (AQUM, red) and observations (AURN, black) of the total oxidant.

To test the hypothesis, semi-idealised experiments are conducted. We simulate a one-month long release of chemically inert tracers within the Numerical Atmospheric Dispersion Environment (NAME) using different sets of numerical weather prediction (NWP) outputs. This enables a process-based evaluation of how different meteorology affects tracers within the BL. Tracers are released within the lateral boundaries of the domain centred on the UK. The idea is to separate the effects of meteorology from chemistry on the tracer concentrations. In particular, we want to understand the role of entrainment of O_{3}-rich air masses from the residual layer down into the developing BL during the morning hours.

We located around 50 AURN sites in urban locations and compared hourly BL depths from June 2017 in the two sets of NWP output used for the tracer simulations: the UKV and UM Global (UMG) configurations of the Met Office Unified Model. It was found that although the average diurnal profiles of BL depth were quite similar, there was a lag in the morning increase of BL depth within the UMG configuration. This may be because the representation of surface sensible heat flux (SSHF) differs in the two NWP models: the UMG uses a single tile scheme to represent urban areas, whereas the UKV uses a more realistic, two-tile scheme (‘MORUSES’) which distinguishes between roof surfaces and street canyons. SSHF is a measure of energy exchange at the ground, where positive fluxes represent a loss of heat from the surface to the atmosphere. Therefore, a more realistic representation of SSHF results in the UKV being better at capturing and storing urban heat. This leads to a faster development of the BL depth in the UKV compared to the UMG, which in turn could mean that there is more turbulent motion and mixing within the atmosphere.

Assuming that the vertical gradient in pollutant concentrations is positive between the morning BL and the free troposphere, mixing air from above should enhance pollutant concentrations nearer to the surface. Our tracer results show that during days when synoptic conditions are dominated by high pressure, the diurnal cycle in forecast and observed surface pollutant concentrations can be adequately replicated by our simplified set-up. Differences between the diurnal cycle between tracer simulations with the two different meteorological set-ups show that the UKV is not only entraining more tracer from above the boundary layer than the simulation using UMG, but also the concentrations increase on average 1 – 2 hours earlier in the morning. These results suggest that indeed the model meteorology – in particular, representation of BL processes – is important to entrainment of polluted air masses into the BL, which in turn has a significant influence on the surface pollutant concentrations.

Within the past two decades, it has been recognised by the weather and air quality modelling communities that neither type of model can truly exist without the other. This post has discussed just one aspect of how meteorology influences the air quality forecast – there are, of course, many other parameters (e.g. wind speed, precipitation, relative humidity) which affect the forecast pollutant concentrations. We therefore also evaluated night-time errors in the wind speed and found that these errors are positively correlated with the total oxidant forecast errors. This means that when the wind speed forecast is overestimated, it is likely to affect the night-time and morning forecast of both O_{3} and NO_{2} in a significant way.

References

Ambient Air Pollution: A global assessment of exposure and burden of disease. WHO, 2016.

Bohnenstengel S., Evans S., Clark P., Belcher S.: Simulations of the London urban heat island, Quarterly Journal of the Royal Meteorological Society, 2011 vol: 137 (659) pp: 1625-1640

Cocks A., 1993: The Chemistry and Deposition of Nitrogen Species in the Troposphere, The Royal Society of Chemistry, Cambridge 1993

Savage N., Agnew P., Davis L., Ordonez C., Thorpe R., Johnson C., O’Connor F., Dalvi M.: Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation, Geoscientific Model Development, 2013 vol: 6 pp: 353-372

Sun J., Mahrt L., Banta R., Pichugina Y.: Turbulence Regimes and Turbulence Intermittency in the Stable Boundary Layer during CASES-99, Journal of the Atmospheric Sciences, 2012 vol: 69 (1) pp: 338-351

Zhang, 2008: Online-coupled meteorology and chemistry models: History, current status, and outlook. Atmos. Chem. Phys, 2008 vol: 8 (11) pp: 2895-2932

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