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