Integrated Systems for Forecasting Integrated Systems for Forecasting Urban Meteorology, Air Pollution Urban Meteorology, Air Pollution
and Population Exposure and Population Exposure (FUMAPEX )(FUMAPEX )
Alexander Baklanov, Danish Meteorological Institute, and the FUMAPEX consortium
Workshop onReal time air pollution data exchange and forecast in Europe
EEA, Copenhagen, 7-8 April 2005
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.
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”.
5FP EC project FUMAPEX: 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.
Urban Air Pollution models
Population Exposure models
Populations/
Groups Indoor concentrations
Outdoor concentrations
Time activity
Micro-
environments E x p o s u r e
Urban heat flux parametrisation
Soil and sublayer models 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 Urban Air Pollution models
WP4: Meteorological models for urban areas
WP7:
http://fumapex.dmi.dk
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.
Bologna (#5)
Oslo (#1)
Vienna
Turin (#2) Milano
Graz
Brussels
London Hanover
Copenhagen (#6)
Budapest
Prague
Paris
Lisboa
Berlin
Helsinki (#3)
Castellón/Valencia (#4)
Frankfurt
Vilnius
Marseille
Moscow
Map of the selected European cities for air pollution episode analysis. The target city candidates for UAQIFS implementation in FUMAPEX are marked by a # and blue background. Potential target cities for applying the FUMAPEX technique in future are marked with a dark-blue shaded border.
Basel
Athens
Shortcomings of existing NWP: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:
Current regulatory (dash line) and suggested (solid and Current regulatory (dash line) and suggested (solid and dash lines) ways for systems of forecasting of urban dash lines) ways for systems of forecasting of urban
meteorology for UAQIFSmeteorology for UAQIFS
Release / EmissionData
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 km ECMWF/ HIRLAM, GME 1-5 km LM, HIRLAM, > 0.1 km MM5, RAMS, LM ~ 1-10 m CFD, LES, box models
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 it’s not enough
• Necessity of urbanized and high-resolution soil and surface layer parameterizations
Higher grid resolution / Downscaling / Urban maps High resolution DMI-HIRLAM NWP forecast for Copenhagen areaHigh 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.
Land-Use Classification / Modification
Dominating Class
Copenhagen Metropolitan AreaResidential 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 BATold
ART = ARTold + 0.6
BATold
BAT = 0.8 BATold
ART = ARTold + 0.2 BATold
BARE = 0.5 BAREold
VEGN = VEGNold + 0.5
BAREold
BARE = 0.5 BAREold
ART = ARTold +
0.5 BAREold
FUMAPEX - SM2 U: 106: bat - buildings-1 Magenta 14.60%0 Yellow 75.86%
Integrated Fumapex urban module for NWP modelsincluding 4 levels of complexity of the NWP 'urbanization'
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).
Module 2 (BEP model): Urban effects in the Martilli et al. (2002) parameterization:
RoofWall
Street
Momentum Turbulence Heat
DragWake
diffusionRadiation
Module 3: SM2-U: Soil-Canopy-AtmosphereEnergy Budget Model Adapted to Urban Districts
(Mestayer et al., 2003)(Mestayer et al., 2003)
Built surface Artificial Surface Natural soil
Bare soil Water
2th soil layer
Superficial layer
3th soil layer
Superficial layer
Thermal budget
Sensible heat flux
Latent heat flux
Net radiation: solar, atmospheric, and earth
radiations
Heat storage flux
Anthropogenic heat flux
Hsens i LEi Gs i Qanth i
Rn i
Built surface Artificial surface Natural soil
Bare soil Water
2nd soil layer
Superficial layer
3th soil layer
Superficial layer
Precipitations
Water budget
Evapotranspiration
cansT
T in t
Q w a ll
T s r o o f
cannR H s e n s c a n L E c a n
canG
+
+Canopy
QH + QE + QG = Q* = K - K + L - L
dTs/dt = CTQG- (2/)(Ts - Tsoil)
QG = Ground flux ; = 24 h
Sensitivity Study on City RepresentationSA : 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)
5
5
10
10
15
15
20
20
25
25
time (H)
-200 -200
-100 -100
0 0
100 100
200 200
300 300
400 400
500 500
600 600
700 700
800 800
Heatfluxes(W/m2)
SC
(UA)
5
5
10
10
15
15
20
20
25
25
time (H)
-200 -200
-100 -100
0 0
100 100
200 200
300 300
400 400
500 500
600 600
700 700
800 800
Heatfluxes(W/m2)
RnLEHsensGstorage
(UA)
SA
Mean fluxes(whole urban area)
Potential temperature (K)
Alti
tude
(m)
288 289 290 2910
100
200
300
400
500
600
700
800
900
1000
Western Rural AreaRDHBDCCICDEastern Rural Area
2400 UTCSA
Potential temperature (K)
Alti
tude
(m)
288 289 290 2910
100
200
300
400
500
600
700
800
900
1000
Western Rural AreaRDHBDCCICDEastern Rural Area
2400 UTCSC
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
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).
Roof level Roof level Roof level
simulation with urban parameterisation
simulation with rural parameterisation
measurements
© Clappier et al., 2003
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)
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 Urban Air Pollution models
The predicted exposure of population to NO2 (g/m3 *persons).
Environmental OfficeKansanterveyslaitosFolkhälsoinstitutetNational Public Health Institute
Population Exposure Models in the UAQIFS
Prediction of population exposure for Helsinki by KTL/FMI/YTV
ENVIRONMENT
MAN
Emission
Environmental contamination
Microenvironmental contamination
Exposure
Whole body dose
Target organ dose
Health impact
Scheme of the different elements composing the UAQIFS for Turin city
Emission Data
Urban Meteorological
Models
Concentration Fields
Geographic & Physiographic Data
Downscaling & Interfaces
UAQ
Models
Large Scale Forecast
& Meteo Data
Local DTM
and CORINE
land cover
RAMS
LOKAL MODELL
(provided by Piedmont Region Weather Service)
Regional emission inventory - INEMAR
FARM
Target Areas
Grid 2
Grid 3
CO forecast
CO 1st day h 22 - a
Grid 1
FUMAPEX test case3 1-way nested grids
Grid 2
Contour interval: 50 ppb
Grid 3
NILU-DNMI urban forecasting system for OsloNILU-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.
Meteorological Interpretation
NWP model output (regional)
MM5 1-3km (~05:30)
AirQUIS (~06:30)
MODEL OUTPUT PLOTS ON WEB FOR END-USERS (~06:30)
Global weather information
HIRLAM10 (~05:00)
Met-preprocessor
Meteorology and Air Pollution:Meteorology and Air Pollution:as a joint problemas 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
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
Module of feedback
mechamisms:
- Direct gas & aerosol forcing
- Cloud condensa-tion nuclei model
- Other semidirect & indirect effects
FUMAPEX UAQIFS:
Urban Air Pollution models
Population Exposure models
Populations/
Groups Indoor concentrations
Outdoor concentrations
Time activity
Micro-
environments E x p o s u r e
Urban heat flux parametrisation
Soil and sublayer models 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 Urban Air Pollution models
WP4: Meteorological models for urban areas
WP7:
Many thanks to my FUMAPEX colleagues !!!
For more information:
FUMAPEX web-site: http://fumapex.dmi.dk
CLEAR web-site: http://www.nilu.no/clear
COST715 web-site: http://www.fmi.fi/ENG/ILA/COST715/
Thank you for the attention !
FUMAPEX Project participantsDanish Meteorological Institute, DMI A. Baklanov (co-ordinator), A. RasmussenGerman Weather Service, DWD B. FayHamburg University, MIHU M.SchatzmannCentro De Estudios Ambientales Del Mediterrano, CEAM M. MillanEcole Centrale de Nantes, ECN P. MestayerFinnish Meteorological Institute, FMI J. KukkonenARIANET Consulting, ARIA NET S. FinardiEnviron. Protection Agency of Emilia Romagna, ARPA M. DesertiThe Norwegian Meteorological Institute, DNMI N. BjergeneNorwegian Institute for Air Research, NILU L.H. SlordalUniversity of Hertfordshire, UH R.S. SokhiINSA CNRS-Universite-INSA de Rouen, CORIA A. CoppalleFinnish National Public Health Institute, KTL M. JantunenEnvironmental Protection Agency of Piedmont, ARPAP F. LollobrigidaEnvironment Institute - Joint Research Center, JRC EI A. SkouloudisSwiss Federal Institute of Technology, ETH A. Clappier, M. RotachUniversity of Uppsala, MIUU S. ZilitinkevichUniversité catolique de Louvain, UCL G. SchayesDanish Emergency Management Agency, DEMA S.C. HoeHelsinki Metropolitan Area Council, YTV P. AarnioNorwegian Traffic Authorities, NTA P. RoslandMunicipality of Oslo, MO O.M. Hunnes
WRF-Chem:
Online versus offline averaged concentration over half of the domain,
At 21Z
Georg Grell
Integrated (on-line coupled) modeling system structure for predicting climate change and atmospheric composition
Radiative & optic properties models
General Circulation & Climate models
Cloud condensation nuclei (CCN) model
Aerosol dynamics models
Ecosystem
models
Ocean dynamics
model
Atmospheric chemistry and transport models
Emission databases, models and scenarios
Inverse methods and adjoint models
WP7
Integrated Assessment Model
WP5
Baklanov et al., 2003
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
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)
Early warning and Early warning and emergency emergency
preparedness for preparedness for CopenhagenCopenhagen
• accidental radioactive or toxic emissions,
• potential terrorist attacks with radioactive, chemical or biological matter releases, etc.,
• fires.
Structure of the Danish nuclear emergency modelling system
DMI-HIRLAM systemDMI-HIRLAM system
• T version: 0.15°• S version: 0.05°• L version:
0.014°
ECMWF global modelECMWF global model
DERMA modelDERMA model
• 3-D trajectory model
• Long-range dispersion
• Deposition of radionuclides
• Radioactive decay
ARGOS systemARGOS system
• Radiological monitoring
• Source term estimation
• Local-Scale Model Chain
• Health effects