1. Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure: Experience of European FUMAPEX and COST715 Studies Alexander Baklanov Danish Meteorological Institute alb@dmi.dk with contributions from the project partners The First GURME Air Quality Forecasting Workshop for the Latin America project, Santiago de Chile, 13-16 October 2003 University of Chile Faculty of Physical and Mathematical Sciences

2. European COST Action 715: URBAN METEOROLOGY ‘Meteorology Applied to Urban Air Pollution Problems’ Action period: 1999-2003 (Chairman Bernard Fisher, UK) WG1: Wind fields in urban areas (Chair M. Rotach). WG2: Mixing height and surface energy budgets (Chair M Piringer). WG3: Meteorology during peak pollution episodes (Chair J. Kukkonen). WG4: Input data for urban air pollution models (Chair M. Schatzmann).  Basel UrBan Boundary Layer Experiment (BUBBLE), initiated by the Swiss COST organisation (Leader M. Rotach)  UBL/CLU, Marseille, France, an associated project of ESCOMPTE  UBL/CLU, Marseille, France, an associated project of ESCOMPTE (Leader P. Mestayer)

3. Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure FUMAPEX EVK4-2001-00281 http://fumapex.dmi.dk Shared-cost RTD, November 2002 – October 2005 The Fifth Framework Programme (FP5) Energy, Environment and Sustainable Development Sub-programme: Environment and Sustainable Development Key Action 4: City of Tomorrow and Cultural Heritage

4. Project participants Danish Meteorological Institute, DMIA.Baklanov (co-ordinator), A.Rasmussen German Weather Service, DWDB. Fay Hamburg University, MIHUM.Schatzmann Centro De Estudios Ambientales Del Mediterrano, CEAMM. Millan Ecole Centrale de Nantes, ECNP. Mestayer Finnish Meteorological Institute, FMIJ. Kukkonen ARIANET Consulting, ARIA­NETS. Finardi Environ. Protection Agency of Emilia Romagna, ARPAM. Deserti The Norwegian Meteorological Institute, DNMIN. Bjergene, E. Berge Norwegian Institute for Air Research, NILUL.H. Slordal University of Hertfordshire, UHR.S. Sokhi INSA CNRS-Universite-INSA de Rouen, CORIAA. Coppalle Finnish National Public Health Institute, KTLM. Jantunen Environmental Protection Agency of Piedmont, ARPAPF. Lollobrigida Environment Institute - Joint Research Center, JRC EIA. Skouloudis Swiss Federal Institute of Technology, ETHA. Clappier, M. Rotach University of Uppsala, MIUUS. Zilitinkevich Université catolique de Louvain, UCLG. Schayes Danish Emergency Management Agency, DEMAS.C. Hoe Helsinki Metropolitan Area Council, YTVP. Aarnio Norwegian Traffic Authorities, NTAP. Rosland Municipality of Oslo, MOO.M. Hunnes

5. WHY to study it now ? Meteorological fields constitute a main source of uncertainty in urban air quality (UAQ) forecasting models. Historically, UAQ forecasting and NWP models were developed separately and there is no tradition for co-operation between the modelling groups. This was plausible in the previous decades when the resolution of NWP models was too poor for city-scale air pollution forecasting, but the situation has now changed and it is obvious that a revision of the conventional conception of urban air quality forecasting is required.

6. FUMAPEX objectives (i)the improvement of meteorological forecasts for urban areas, (ii)the connection of NWP models to UAQ and population exposure models, (iii)the building of improved Urban Air Quality Information and Forecasting Systems (UAQIFS), and (iv)their application in cities in various European climates.

7. In order to achieve the innovative FUMAPEX goal of establishing and implementing an improved new UAQIFS in four European target cities to assist sustainable urban development, the following steps have to be achieved: 1.improve predictions of the meteorological fields needed by UAP models by refining resolution and developing specific parameterisations of the urban effects in NWP models, 2.develop suitable interface/meteorological pre-processors from NWP to UAP models, 3.validate the improvements in NWP models and meteorological pre- processors by evaluating their effects on the UAP models against urban measurement data, 4.apply the improved meteorological data to UAQ and population exposure models and compare and analyse the results, and 5.successfully link meteorologists/NWP modellers with urban air pollution scientists and ’end-users’ of UAQIFS.

8. FUMAPEX Work Packages Structure WP 1Analysis and evaluation of air pollution episodes in European cities (leaded by J. Kukkonen, FMI) WP 2Assessment of different existing approaches to forecast UAP episodes (leaded by R.S. Sokhi, UH) WP 3Testing the quality of different operational meteorological forecasting systems for urban areas (leaded by B. Fay, DWD) WP 4Improvement of parameterisation of urban atmospheric processes and urban physiographic data classification (leaded by A. Baklanov, DMI) WP 5Development of interface between urban-scale NWP and UAP models (leaded by S. Finardi, Arianet) WP 6Evaluation of the suggested system (UAQIFS) to uncertainties of input data for UAP episodes (leaded by N. Bjergene, DNMI) WP 7Development and evaluation of population exposure models in combination with UAQIFS’s (leaded by M. Jantunen, KTL) WP 8Implementation and demonstration of improved Urban Air Quality Information and Forecasting Systems (leaded by L.H. Slørdal, NILU) WP 9Providing and dissemination of relevant information (leaded by A. Skouloudis, JRC) WP 10Project management and quality assurance (leaded by A. Rasmussen, DMI).

9. Current regulatory (dash line) and suggested (solid and dash lines) ways for systems of forecasting of urban meteorology for UAQIFS Release / Emission Data Meteorological observations Global / Regional NWP models Limited area NWP Meso-meteorological models (e.g. non-hydrostatic) Local scale models Meteo preprocessors, Interfaces Urban Air Pollution and Emergency Preparedness models Resolution:Models, e.g.:  15 kmECMWF/HIRLAM,GME  1-5 kmLM,HIRLAM, > 0.1 kmMM5, RAMS, LM ~ 1-10 mCFD, box models

10.  During the last decade substantial progress in NWP modelling and in the description of urban atmospheric processes was achieved.  Modern nested NWP models are approaching the resolution of the meso- and city- scale utilising land-use databases down to 1 km resolution or finer.  In combination with the recent scientific developments in the field of urban atmospheric physics and the enhanced availability of high-resolution urban surface characteristics, the capability of the NWP models to provide high quality urban meteorological data will therefore increase.  Existing operational UAP models often employ simple local measurements and meteorological pre-processors with a poor description of the temporal and spatial evolution of meteorological variables on the urban scale.  Modern UAP models demand a lot more of additional meteorological input data, such as humidity distribution, cloud characteristics, intensity and type of precipitation, radiation characteristics etc.  Clearly, present UAP models could greatly benefit from utilising meteorological data from NWP models to give a physically consistent basis for urban air quality forecasts. Possibilities of NWP Models for UAP forecasting

11. Shortcomings of existing NWP:  Despite the increased resolution of existing operational NWP models, urban and non-urban areas mostly contain similar sub- surface, surface, and boundary layer formulation.  These do not account for specifically urban dynamics and energetics and their impact on the numerical simulation of the atmospheric boundary layer and its various characteristics (e.g. internal boundary layers, urban heat island, precipitation patterns).  Additionally, NWP models are not primarily developed for air pollution modelling and their results need to be designed as input to urban and mesoscale air pollution models.

12. Urban BL features Local-scale inhomogeneties, sharp changes of roughness and heat fluxes, Wind velocity reduce effect due to buildings, Redistribution of eddies due to buildings, large => small, Trapping of radiation in street canyons, Effect of urban soil structure, diffusivities heat and water vapour, Anthropogenic heat fluxes, urban heat island, Internal urban boundary layers (IBL), urban Mixing Height, Effects of pollutants (aerosols) on urban meteorology and climate, Urban effects on clouds and precipitation.

13. Existing Approaches for Treatment of Urban BL Features Urban roughness effects (e.g., Bornstein, 1975, 2001; Hunt et al., 2003) Urban surface energy balance (Oke et al., 1999, Piringer et al., 2002) Town Energy Balance (TEB) scheme (Masson, 2000) Urban surface exchange sub-layer model (Martilli et al., 2001) SM2-U urban area soil submodel (Mestayer et al., 2002) Prognostic models for UBL height (Zilitinkevich and Baklanov, 2002; Gryning and Bartchvarova, 2002).

14. Urban Scale NWP modeling needs Higher grid resolution / downscaling Physiographic data / Land use / RS data Calculation of urban roughness Calculation of urban heat fluxes Urban canopy model Mixing height in urban areas Urban measurement assimilation in NWP models

15. Urban Air Pollution models Population Exposure/Dose models Urban heat flux parametrisation Soil model for urban areas Urban roughness classification & parameterisation Usage of satellite information on surface Meso- / City - scale NWP models Mixing height and eddy diffusivity estimation Down-scaled models or ABL parameterisations Estimation of additional advanced meteorological parameters for UAP Grid adaptation and interpolation, assimilation of NWP data WP5: Interface to UAP models WP4: NWP models for urban areas Scheme of the suggested improvements of meteorological forecasts (NWP) in urban areas and interfaces to UAP models

16. Main problems to be solved: Nested high resolution, urban scale resolved models; on-line coupling atmospheric mesoscale models with heterogeneous chemistry and aerosol models. Improvement of the urban boundary layer parameterisation, e.g. turbulent sensible and latent heat fluxes, revised roughness and land use parameters and models. Assimilation of surface characteristics based on satellite data and additional urban meteorological measurements for urban scale NWP models. A model interface capable to connect meso-scale meteorological model results to updated UAQ and atmospheric chemistry models. An improved integrated urban meteorology, air pollution and population exposure modelling system suitable to be applied to any European urban area on a basis of available operational weather forecast. Evaluation and sensitivity studies of these improvements on the meteorological input fields for UAQ models and the resulting air quality simulations.

17. Improvement of parameterisation of urban atmospheric processes and urban physiographic data classification Three levels of complexity of the NWP 'urbanization': 1. Simple corrections of the surface roughness for urban areas (e.g., following to Grimmond and Oke, 1999) and heat fluxes (adding the additional urban heat flux., e.g., via heat/energy production/using in the city and albedo change) within the existing non- urban physical parameterisations of the surface layer in the model with higher resolution and improved land-use classification. It is realised in the DMI-HIRLAM model. 2. Improvement and realization of a new flux aggregation technique, suggested by Risø NL in cooperation with DMI (Hasager et al., 2002: SAT-MAP-Climate Report) for urban areas. This module was realized in the DMI-HIRLAM model for non-urban areas for the moment. However, the approach can be extended for the urban canopies as well (we need experimental data to verify the parameterisations for urban areas). 3. Implementation of special physical parameterisations/submodel for the urban sub-layer into the NWP model. In the FUMAPEX project we plan to realize at least two different urban submodels/modules in HIRLAM and in MM5: (i) urban surface exchange parameterisation, developed by the Swiss partner (the model description in: Martilli et al., 2002), (ii) SM2-U urban area soil submodel, developed by the French partners (Mestayer etc., 2002).

18. Land-use classification and roughness simulation in DMI-HIRLAM model Land-use classification over Denmark with 1 km resolution. Roughness length for: (left) I-version in 1.4 km resolution, (centre) D-version in 5 km resolution, (right) E-version in 15 km resolution.

19. High resolution DMI-HIRLAM NWP forecast for Copenhagen area Examples of forecasted wind fields at 10-meter height and of 2-meter air temperature for the Copenhagen metropolitan area by the experimental version of DMI-HIRLAM with the horizontal resolution of 1.4 km.

20. New flux aggregation technique suggested by Risø NL in cooperation with DMI (Hasager et al., 2002) Version ‘H’Version ‘z0t’

21. The urban effects represented in the Martilli (2001) parameterization: Roof Wall Street MomentumTurbulenceHeat Drag Wake diffusion Radiation

22. Temperature measured and simulated by FVM with and without the urban parameterization over Mexico City © Clappier et al., 2003

23. SM2-U model structure (P. Mestayer and I. Calmet) SMU2-U Energy Budget

24. SM2-U WATER BUDGET © Mestayer et al., 2003

25. FUMAPEX NWP models for 'urbanization‘: 1.DMI-HIRLAM (Partner 1: DMI); 2.Lokalmodell (LM) (Partner 2: DWD); 3.MM5 (Partner 9: DNMI (met.no)); 4.RAMS (Partner 4: CEAM); 5.Finite Volume Model, FVM (Partner 16: EPFL) 6.Topographic Vorticity-Mode Mesoscale (TVM) Model (Guy Schayes, UCL, subcont. of DMI).

26. Urban BL features for MH estimation (i) internal urban boundary layer (IBL), (ii) elevated nocturnal inversion layer, (iii) strong horizontal inhomogeneity and temporal non-stationarity, (iv) so-called ‘urban roughness island’, zero-level of urban canopy, and z0u  z0T, (v) anthropogenic heat fluxes from street to city scale, (vi) downwind ‘urban plume’ and scale of urban effects in space and time, (vii) calm weather situation simulation, (viii) non-local character of urban MH formation, (ix) urban soil, effect of the water vapour fluxes.

27. Applicability of ‘rural’ methods of the MH estimation for urban areas: For estimation of the daytime MH, applicability of common methods is more acceptable than for the nocturnal MH. For the convective UBL the simple slab models (e.g. Gryning and Batchvarova, 2001) were found to perform quite well. The formation of the nocturnal UBL occurs in a counteraction with the negative ‘non-urban’ surface heat fluxes and positive anthropogenic/urban heat fluxes, so the applicability of the common methods for the SBL estimation is less promising. The determination of the SBL height needs further developments and verifications versus urban data. As a variant of the methods for SBL MH estimation the new Zilitinkevich et al. (2002) parameterisation can be suggested in combination with a prognostic equation for the horizontal advection and diffusion terms (Zilitinkevich and Baklanov, 2002). Meso-meteorological and NWP models with modern high-order non-local turbulence closures give promising results (especially for the CBL), however currently the urban effects in such models are not included or included with great simplifications.

28. WP 7: Exposure modelling (KTL) Meteorological Models Urban Air Pollution Models Population Exposure Models Emis- sions Population Exposure Models Populations/ Groups Indoor concentrations Outdoor concentrations Time activity Micro- environments E x p o s u r e

29. Definition of exposure as the interface between man and the environment

32. FUMAPEX target cities for improved UAQIFS implementation #1 – Oslo, Norway #2 – Turin, Italy #3 – Helsinki, Finland #4 – Valencia/Castellon, Spain #5 – Bologna, Italy #6 – Copenhagen, Denmark Different ways of the UAQIFS implementation: (i)urban air quality forecasting mode, (ii) urban management and planning mode, (iii) public health assessment and exposure prediction mode, (iv) urban emergency preparedness system.

33. Cover Turin City Urban Area: Modelling System for Forecasting Meteorology and Air Quality S. Finardi, G. Calori F. Lollobrigida, R. De Maria, M. Clemente

34. Modelling system general features General requirements: Target resolution resolving urban area features : 1 km; Considered pollutants: SO 2, NO 2, CO, PM10, O 3 and Benzene; Limiting models inter-dependence, modularity; User oriented results production and management

35. Scheme of the different elements composing the UAQIFS for Turin city

36. Target Areas Grid 2 Grid 3

37. CO forecast CO 1st day h 22 - a Grid 1 FUMAPEX test case 3 1-way nested grids Grid 2 Contour interval: 50 ppb Grid 3

38. The predicted concentration of NO 2 in the greater Helsinki area (  g/m 3 ) © Helsingin kaupunki, Kaupunginmittausosasto 576§/1997, ©Aineistot: Espoon, Helsingin, Kauniaisten ja Vantaan mittausosastot Environmental Office Prediction of population exposure for Helsinki by KTL/FMI/YTV

39. The predicted population activity (number of persons). © Helsingin kaupunki, Kaupunginmittausosasto 576§/1997, ©Aineistot: Espoon, Helsingin, Kauniaisten ja Vantaan mittausosastot Environmental Office

40. The predicted exposure of population to NO 2 (  g/m 3 *persons). © Helsingin kaupunki, Kaupunginmittausosasto 576§/1997, ©Aineistot: Espoon, Helsingin, Kauniaisten ja Vantaan mittausosastot Environmental Office

41. Early warning and emergency preparedness: The availability of reliable UAQIFS with urban scale weather and pollution forecasts could be of relevant support for emergency management: (i) fires, (ii) accidental radioactive or toxic emissions, (iii) potential terrorist attacks with radioactive, chemical or biological matter releases, etc. Copenhagen Metropolitan Area

42. DMI recent activity and achievements: high resolution (up to 1.4 km horizontal resolution) numerical modelling of regional meteorological processes; using fields of effective roughness length, satellite-based sea surface temperature and albedo in DMI-HIRLAM-E model; new algorithms for the SBL mixing height in atmospheric models; highly accurate advection scheme in atmospheric models; atmospheric chemistry and aerosol dynamics and deposition models; on-line coupling of meteorological and atmospheric pollution models; mini-ensemble of meteorological forecast; integrated operational modelling for emergency preparedness and the ARGOS system.

43. Structure of the Danish nuclear emergency modelling system DMI-HIRLAM system G:0.45° E and N:0.15° D:0.05° L: 0.014° ECMWF global model DERMA model 3-D trajectory model Long-range dispersion Deposition of radionuclides Radioactive decay ARGOS system Radiological monitoring Source term estimation Local-Scale Model Chain Health effects

44. HIRLAM-ARGOS-RIMPUFF simulation of a hypothetical accident at the Barsebæk NPP for the Copenhagen metropolitan area DMI-HIRLAM forecast meteo-fields, 1.4 km horiz. resolution ARGOS forecast: Xe-135Time Integr. Air Conc. Xe-135 Time Integr. Air Conc.

45. For more information: FUMAPEX web-site: http://fumapex.dmi.dk FUMAPEX progress report: http://www.dmi.dk/f+u/publikation/vidrap/2003/Sr03-12.pdf COST715 web-site: http://www.fmi.fi/ENG/ILA/COST715/ Thank you !