An air quality information system for cities with complex terrain based on high resolution NWP

1. An air quality information system for cities with complex terrain based on high resolution NWP Viel Ødegaard, r&d department

2. An air quality information system • Daily forecasts for NO2 and dust (pm10/pm2.5) concentrations are produced for the five Norwegian cities Oslo, Drammen, Trondheim, Bergen and Stavanger during winter • A modelling system is developed by Norwegian Meteorological Institute and Norwegian Institute for Air Research in cooperation • The Norwegian Traffic Authorities fund the system and local health authorities in the cities are the end users • Meteorological and air quality forecasts for the next day and air quality statistics are available for the end users on internet.

3. Components • NWP model • interface module • air quality model • subjective interpretation • actions

4. Heavy air pollution episodes in northern areas occur under the following meteorological conditions Local circulation • high vertical stability • low wind speed • negative radiation balance • low surface temperature • dry surface Large scale conditions • persistent high pressure situation • (followed by a change in weather regime)

5. Winter time inversion • persistent low temperatures over several days lead to extensive wood burning and emission of particles (pm2.5) in addition to the traffic emmisions (NO2) • in high pressure situations in winter time the radiation term dominates the thermodynamics. The amount of incoming solar radiation is very small compared to the outgoing long wave radiation as the solar angle is low and the days are short • persistent radiative cooling of the surface builds up a surface inversion • a change in the large scale circulation, with warmer air carried to the coast and over the mountains can strengthen the inversions. The cold air is captured between the topography and the warm air aloft

8. Spring dust • road surface is torn off during the winter, but most of the matter is kept on the road surface in the snow. When the snow melts and the road surface becomes dry the particles stired up and dispersed by the traffic and the wind • common in the spring, after several days with good melting conditions and no precipitaiton • local concentrations of dust vary a lot. The wind forecasts (both speed and direction) and the description of snow cover (and snow melting) from the meteorological model is crucial

9. NWP model • resolution required for Norwegian cities implies that a non-hydrostatic model is used • MM5 v3 (Grell et al. 1994), two-way nesting, 3 and 1 km resolution • sigma vertical coordinate (17 layers – 9 below 1500m) • physics: non local plantetary boundary layer scheme (Hong and Pan, 1996) simple ice phase condensation scheme (Grell et al. 1994) – no convection radiation scheme interacting with clouds (Grell et al. 1994) 5 layer soil model (Dudiah, 1996) • run on local IBM pc-cluster 40 processors, 48 hour forecast for the Oslo area takes 28 min.

10. domains and resolution

11. Input • analysis interpolated from HIRLAM 0.1 deg resolution • boundaries from HIRLAM 0.1 deg on 15 pressure levels • snow cover, sea surface temperature, soil temperature and soil moisture in 7, 50 and 95 cm from HIRLAM • MM5 topography and land use data base, 0.9 km resolution, 16 surface classes • 24 – 48h forecast

12. Evaluation of forecasts • yearly verification of last winter’s forecasts • the observation network is extended with instruments supplied by the traffic authorities and the local governments • comparing air quality forecasts with the driving parameters from NWP (temperature, wind and vertical temperature gradient) in order to adress the sources of the errors. • observations in more than one level only in Oslo, where one station is measuring in two levels ( 7 and 25 m).

15. Main results from validation of the meteorology • MM5 forecasts have higher stde than forecasts from HIRLAM • Local wind directions are better represented in MM5 • MM5 developes too strong surface inversions • Wind speed is often overestimated in calm situations and sometimes underestimated when there is strong winds outside the coast (increase model domain) • 2m temperature has sometimes large errors. Key words are snow cover and boundary layer parameterisation in the stable case

17. Snow cover observations

18. Future • develope initial fields with snow cover based on satellite and surface observations • In FUMAPEX: testing higher vertical resolution and boundary layer parameterisations (inversion problem)