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Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure (FUMAPEX ) Integrated Systems for Forecasting Urban Meteorology,

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Presentation on theme: "Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure (FUMAPEX ) Integrated Systems for Forecasting Urban Meteorology,"— Presentation transcript:

1 Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure (FUMAPEX ) Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure (FUMAPEX ) Alexander Baklanov, Danish Meteorological Institute, and the FUMAPEX consortium Workshop on Real time air pollution data exchange and forecast in Europe EEA, Copenhagen, 7-8 April 2005

2 METEOROLOGICAL ADVANCES AND SYSTEMS FOR URBAN AIR QUALITY FORECASTING Modern numerical weather prediction (NWP) and meso- meteorological models (MetM) able to resolve urban-scale processes are considered to be the main tools in future urban air pollution (UAP) forecasting because they allow for sufficiently high spatial and temporal resolution and can trace back the linkages between sources and impacts. Improved urban meteorology and suggested nesting MetM-UAP integrated strategy are discussed. This work is realised as a part of the activities within the EC FUMAPEX project.

3 Two levels of the integration strategy: Off-line integration of Urban Meteorology, Air Pollution and Population Exposure models for urban air pollution forecast and emergency preparedness, which is the FUMAPEX aim. On-line integration of meso-scale meteorological models and atmospheric aerosol & chemical transport models (CTM) with consideration of the feedbacks of air pollution (e.g. urban aerosols) on meteorological processes and urban climate. This direction is developed at DMI and considered in the new COST Action 728. This will lead to a new generation of models for chemical weather forecasting.

4 5FP EC project FUMAPEX: Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure 22 teams from 10 European countries are involved Project objectives: (i) the improvement of meteorological forecasts for urban areas, (ii) the connection of NWP models to urban air quality (UAQ) and population exposure (PE) models, (iii) the building of improved Urban Air Quality Information and Forecasting Systems (UAQIFS), and (iv) their application in cities in various European climates.

5 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.

6 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 ABL and its various characteristics (e.g. internal boundary layers, urban heat island, precipitation patterns). Higher grid resolution / downscaling / nesting Improved Urban physiographic data / Land use / RS data Calculation of urban effective roughness Calculation of urban heat fluxes Urban canopy model Mixing height in urban areas Urban measurement assimilation in NWP models Urban Scale NWP modeling needs:

7 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, LES, box models

8 DMI-HIRLAM runs for the Helsinki episode #1 WP3 NWP model intercomparison and verification (Fay et al., 2004): Helsinki, Oslo, Bologna, Valencia episodes – LM, HIRLAM, MM5, RAMS models Increase of the NWP model resolution from 20 km up to 1 km – it improves, but its not enough Necessity of urbanized and high-resolution soil and surface layer parameterizations

9 Higher grid resolution / Downscaling / Urban maps High resolution DMI-HIRLAM NWP forecast for Copenhagen area CORINE and PELCOM data up to 250 m resolution (21 classes are used) AIS Land-use database with the resolution 25 x 25 meters (DMU) GIS databases of urban structure (BlomInfo A/S) TOP10DK KMS database (Kort og Matrikelstyrelsen) Increased resolution and surface data-bases for the roughness length calculation in operational NWP models are considered and tested in down-scaled DMI-HIRLAM, LM, MM5 and RAMS models, used by FUMAPEX partners.

10 Land-Use Classification / Modification Dominating Class Copenhagen Metropolitan Area Residential District Industrial Commercial District (ICD) City Center (CC)/ High Building (HBD) Vegn - Green 2.74% Vega & Art – White 0.08% + 0.12% Nat - Black 1.83% Bare -Yellow 21.79% Bat - Red 2.25% Eau - Blue 56.58% BAT = 0.4 BAT old ART = ART old + 0.6 BAT old BAT = 0.8 BAT old ART = ART old + 0.2 BAT old BARE = 0.5 BARE old VEGN = VEGN old + 0.5 BARE old BARE = 0.5 BARE old ART = ART old + 0.5 BARE old FUMAPEX - SM2 U: 106: bat - buildings -1 Magenta 14.60% 0 Yellow 75.86%

11 Integrated Fumapex urban module for NWP models including 4 levels of complexity of the NWP 'urbanization'

12 Module 1: DMI urban parameterisation (i)Displacement height, (ii)Effective roughness and flux aggregation, (iii)Effects of stratification on the roughness, (iv)Different roughness for momentum, heat, and moisture; (v)Calculation of anthropogenic and storage urban heat fluxes for NWP models; (vi)Prognostic MH parameterisations for SBL; (vii)Parameterisation of wind and eddy profiles in canopy layer (Zilitinkevich and Baklanov, 2004).

13 Module 2 (BEP model): Urban effects in the Martilli et al. (2002) parameterization: Roof Wall Street MomentumTurbulenceHeat Drag Wake diffusion Radiation

14 (Mestayer et al., 2003) Module 3: SM2-U: Soil-Canopy-Atmosphere Energy Budget Model Adapted to Urban Districts (Mestayer et al., 2003) Thermal budget Sensible heat flux Latent heat flux Net radiation: solar, atmospheric, and earth radiations Heat storage flux Anthropogenic heat flux H sens i LE i G s i Q anth i R n i Precipitations Water budget Evapotranspiration + + Canopy Q H + Q E + Q G = Q* = K - K + L - L dT s /dt = C T Q G - (2 / )(T s - T soil ) Q G = Ground flux ; = 24 h

15 Sensitivity Study on City Representation SA : Detailed city SB : Homogeneous mean city SC : Mineral city (used in LSM, no buildings, dry bare soil) ALL : Different behavior SC vs SA : stores & releases less energy (no radiative trapping) ; Rn is weaker (higher albedo) Mean fluxes (whole urban area) Temperature profiles (above districts) SA : at 00h neutral stratification above CC & HBD, stable - others. Urban Heat Island is seen (Surface air temperature above the city higher than on the rural area). SC : Stable stratification & temperature homogeneity for all. Importance of urban surface characteristics description

16 Verification of the BEP model versus the BUBBLE experiment Vertical profiles of wind velocity (left), friction velocity (middle), and potential temperature (right) measured (points), simulated with MOST (dashed line) and with the urban parameterization of Martilli et al. (2002) (solid line). © Clappier et al., 2003

17 Development of meteo-processor and interface between urban scale NWP and UAP models Guidelines and improvement of interfaces (Finardi et al., 2004) Interface vs. pre-processors for modern UAQ models BEP urbanization module as a post-processor (Clapier et al., 2004) DMI new urban meteo-preprocessor (Baklanov and Zilitinkevich, 2004) MH methods for urban areas (WG2 COST715)

18 The predicted exposure of population to NO2 ( g/m3 *persons). Environmental Office Population Exposure Models in the UAQIFS Prediction of population exposure for Helsinki by KTL/FMI/YTV

19 Scheme of the different elements composing the UAQIFS for Turin city

20 Target Areas Grid 2 Grid 3

21 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

22 NILU-DNMI urban forecasting system for Oslo Observed and modelled wind speeds at Valle Hovin, a meteorological station in Oslo, for 22 days during the winter 1999/2000.

23 Meteorology and Air Pollution: as a joint problem Meteorology is a main source of uncertainty in APMs => needs for NWP model improvements Complex & combined effects of meteo- and pollution components (e.g., Paris, Summer 2003) Effects of pollutants/aerosols on meteo&climate (precipitation, thunderstorms, etc) Three main stones for Atmospheric Environment modelling: 1.Meteorology / ABL, 2.Chemistry, => Integrated Approach 3.Aerosol/pollutant dynamics (chemical weather forecasting) 4.Effects and Feedbacks

24 Extended FUMAPEX scheme of the UAQIFS including feedbacks Improvements of meteorological forecasts (NWP) in urban areas, interfaces and integration with UAP and population exposure models following the off-line or on-line integration

25 Many thanks to my FUMAPEX colleagues !!! For more information: FUMAPEX web-site: CLEAR web-site: COST715 web-site: Thank you for the attention !

26 FUMAPEX 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 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

27 WRF-Chem: Online versus offline averaged concentration over half of the domain, At 21Z Georg Grell

28 Integrated (on-line coupled) modeling system structure for predicting climate change and atmospheric composition Baklanov et al., 2003

29 COST 728: Enhancing meso-scale meteorological modelling capabilities for air pollution and dispersion applications (2004-2009) WG2: Integrated systems of MetM and CTM: strategy, interfaces and module unification WG2 overall aim - to identify the requirements for the unification of MetM and CTM/ADM modules and to propose recommendations for a European strategy for integrated mesoscale modelling capability. WG2 activities will include: Forecasting models Assessment models

30 Why we need to build the European integration strategy? European mesoscale MetM/NWP communities: ECMWF HIRLAM COSMO ALADIN/AROME UM --------- WRF MM5 RAMS European CTM/ADMs: a big number problem oriented not harmonised (??) ….. NWP models are not primarily developed for CTM/ADMs and there is no tradition for strong co-operation between the groups for meso/local-scale the conventional concepts of meso- and urban-scale AQ forecasting need revision along the lines of integration of MetM and CTM US example (The models 3, WRF-Chem) A number of European models … A universal modelling system (like ECMWF in EU or WRF-Chem in US) ??? an open integrated system with fixed architecture (module interface structure)

31 Early warning and emergency preparedness for Copenhagen accidental radioactive or toxic emissions, potential terrorist attacks with radioactive, chemical or biological matter releases, etc., fires.

32 Structure of the Danish nuclear emergency modelling system DMI-HIRLAM system T version:0.15° S version:0.05° L version: 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

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