Chemical Weather Forecasting: A New Concept and Methodology - - PowerPoint PPT Presentation

Chemical Weather Forecasting: A New Concept and Methodology

Alexander Baklanov, Ulrik Korsholm, Alexander Mahura, Allan Gross, Claus Petersen

Danish Meteorological Institute (DMI), Research Department, Copenhagen, Denmark

In linkage with activities on COST-728, COST-ES0602 Actions, and HIRLAM consortiums

CITES-2009, Jul 2009, Krasnoyarsk, Russia

Chemical Weather Forecast: Common Concept (I)

• Chemical weather forecasting (CWF) - is a new quickly developing and

growing area of atmospheric modelling.

• Possible due to quick growing supercomputer capability and operationally

available NWP data as a driver for atmospheric chemical transport models (ACTMs).

• The most common simplified concept includes only operational air quality

forecast for the main pollutants significant for health effects and uses numerical ACTMs with operational NWP data as a driver.

• Such a way is very limited due to the off-line way of coupling the ACTMs

with NWP models (which are running completely independently and NWP does not get any benefits from the ACTM) and not considering the feedback mechanisms.

• Many experimental studies and research simulations show that atmospheric

processes (meteorological weather, including the precipitation, thunderstorms, radiation budget, cloud processes and PBL structure) depend on concentrations

• f chemical components (especially aerosols) in the atmosphere.
• Therefore, ACTMs have to be run together at the same time steps using on-line

coupling and considering two-way interaction between the meteorological processes 1 chemical transformation and aerosol dynamics.

• New concept and methodology considering the chemical weather as two-way

interacted meteorological weather and chemical composition of the atmosphere.

• CWF should include not only health-effecting pollutants (air quality components)

but also GHGs and aerosols affecting climate, meteorological processes, etc.

• Strategy of new generation on-line integrated meteorology and ACT modelling

systems for predicting atmospheric composition, meteorology and climate change (as a part of and a step to Earth Modelling Systems, EMS).

Chemical Weather Forecast: Common Concept (II)

Mesoscale Meteorological Modelling Capabilities for Air Pollution and Dispersion Applications (I)

Main Tasks / Sub-groups:

• Off-line models and interfaces;
• On-line coupled modeling systems and feedbacks;
• Model down-scaling/ nesting and data assimilation;
• Models unification and harmonization.

Aim:

• to identify requirements for unification of MetM and ACTM/ADM modules, and
• to propose recommendations for European strategy for integrated mesoscale

modelling capability. NWP Communities Involved:

• HIRLAM, COSMO, ALADIN/AROME, UM communities;
• MM5/ WRF/ RAMS users/developers.

COST Action 728: http://cost728.org (2006-2009) Working Group 2: Integrated systems of MetM and ACTM – strategy, interfaces and module unification

2008 - Overview of Existing Integrated (off- and on-line) Mesoscale Systems in Europe

COST-728 / WMO Sci. Report, Geneva, Switzerland, 122p.

2009 - Meteorological and Air Quality Models for Urban Areas

Baklanov, Grimmond, Mahura, Athanassiadou (Eds), Springer, 169p. – In Press (ISBN 978-3-642-00297-7)

2009 - Integrated Systems of Meso-Meteorological and Chemical Transport Models

Baklanov, Mahura, Sokhi (Eds), Springer, 186p. – Uncorrected Proofs.

Outcome " to COST Action ES0602: Chemical Weather Forecasting (2008-2012)

Mesoscale Meteorological Modelling Capabilities for Air Pollution and Dispersion Applications (II)

2008 – Young Scientists Summer School and Workshop – 1st YSSS+W

Integrated Modelling of Meteorological and Chemical Transport Processes / Impact of Chemical Weather on Numerical Weather Prediction and Climate Modeling Zelenogorsk (near St.Petersburg) Russia, 7-15 Jul 2008 see details at http://netfam.fmi.fi/YSSS08/

2010 – 2nd YSSS+W

Univ of Tartu / Vilnus (Baltic States), Summer 2010

Integrated Atmospheric System Model Structure

One-way:

• NWP meteo-fields as a driver for ACTM (off-line);
• ACTM chemical composition fields as a driver for R/GCM (or NWP).

Two-way:

• Driver + partly feedback NWP (data exchange via an interface with a limited

time period: off-line or on-line access coupling, with or without second iteration with corrected fields);

• Full feedbacks included on each time step (on-line coupling).

Aerosol Dynamics Model Transport & Chemistry Models Atmospheric Dynamics / Climate Model Ocean and Ecosystem Models Atmospheric Contamination Models Climate / Meteorological Models

Interface / Coupler

Off-Line Models:

• separate ACTMs driven by meteorological input data from meteo-pre-

processors, measurements or diagnostic models,

• separate ACTMs driven by analysed or forecasted meteodata from NWP

archives or datasets,

• separate ACTMs reading output-files from operational NWP models or

specific MetMs with a limited periods of time (e.g. 1, 3, 6 hours).

On-Line Models:

• on-line access models, when meteodata are available at each time-step (it

could be via a model interface as well),

• on-line integration of ACTM into MetM, when ACTM is called on each

time-step inside MetM and feedbacks are available (will use this definition as on-line coupled modelling).

Definitions of Integrated / Coupled Models

On-Line Coupling

• Only one grid;
• No interpolation in space
• No time interpolation
• Physical parameterizations are the

same; No inconsistencies

• Harmonised advection schemes for

all variables (meteo and chemical)

• Possibility to consider aerosol

forcing mechanisms

• All 3D met. variables are available

at the right time (each time step); No restriction in variability of met. fields

• Possibility of feedbacks from

meteorology to emission and chemical composition

• Does not need meteo- pre/post-

processors Off-Line

• Possibility of independent

parameterizations;

• Low computational cost (if NWP

data are already available and no need to run meteorological model);

• More suitable for ensembles and
• perational activities;
• Easier to use for the inverse

modelling and adjoint problem;

• Independence of atmospheric

pollution model runs on meteorological model computations;

• More flexible grid construction and

generation for ACT models,

• Suitable for emission scenarios

analysis and air quality management.

Advantages of On-Line & Off-Line Modeling

• Direct effect " decrease solar/ thermal-IR radiation and visibility;
• Semi-direct effect " affect PBL meteorology and photochemistry;
• First indirect effect " affect cloud drop size, number, reflectivity, and
• ptical depth via CCN;
• Second indirect effect " affect cloud liquid water content, lifetime, and

precipitation;

• All aerosol effects

⇒ High-resolution on-line models with a detailed description of the

PBL structure are necessary to simulate such effects ⇒ On-line integrated models are necessary to simulate correctly the effects involved 2nd feedbacks

Aerosol Effects to be Considered

Approaches: Normal distribution, Bin approach Physics:

• Condensation
• Evaporation
• Emission
• Nucleation
• Deposition
• Coagulation

Aerosol Module

1. Gas Phase 2. Aqueous phase 3. Chemical equil. 4. Climate Modeling

Chemical Solvers

ECMWF DMI -HI RLAM Eulerian trans- port 0.2-0.05 lat-lon, 25-40

• vert. layer,

3-D regional scale Stochastic Lagrangian transport, 3-D regional scale On-Line Chemical Aerosol Trans. Enviro-HI RLAM Off-Line Chemical Aerosol Trans. CAC Emergency Pre- parednes & Risk Assess-

• ment. DERMA

Nuclear, veterinary and chemical. Regional (European) to city scale air pollution: smog and ozone. Regional (European) to city scale air pollution: smog and ozone.

• Tropo. Trans. Models
• Met. Models

Micro-Scale Obstacle Re- solved CFD-type Model M2UE (TSU)

Atmospheric Chemical Transport Modeling (DMI)

Urban Air Pollution models Population Exposure models

Populations/ Gro ups Indoo r concentratio ns Ou tdoor concentrat ions Tim e activit y Micro- env iro nments

E x p o s u r e Urban heat flux parame trisation Soil and sublayer models for urban areas Urban roug hness classific ation & parameterisation Usage of satellite information on surface

Meso- / City - scale NWP models

Mixing height and eddy diffusivity est imatio n Down-scaled m

• dels or ABL

para met eris ations Estima tion of addit ional a dvanced meteo rological param eters fo r UAP Grid a da ptatio n and interpol ation, a ssimilatio n of NWP data

Interface to Urban Air Pollution models Meteorological models for urban areas

Module of feedback mechamisms:

• Direct gas &

aerosol forcing

• Cloud condensa-

tion nuclei model

• Other semidirect

& indirect effects FUMAPEX UAQIFS: All 3D meteorological & surface fields are available at each time step

• Off-line integrated

urbanised UAQIFS in FUMAPEX

• On-line integrated new

generation system with feedbacks – starting Enviro-HIRLAM

Types of Integrated Urban Air Quality Modeling (from FP6 FUMAPEX Experience)

• There is a number of on-line coupled MMM and ACTM model systems in Europe.
• However, many of them were not built for the meso-meteorological scale,

most of them do not consider feedback mechanisms or include only direct effects of aerosols on meteorological processes.

• Only two meso-scale on-line integrated modelling systems (WRF-Chem and Enviro-HIRLAM)

consider feedbacks with indirect effects of aerosols.

Integrated (On-line Coupled) Modeling System for Predicting Atmospheric Composition

Enviro-HIRLAM : Environment – HIgh Resolution Limited Area Model

Started by DMI Environmental Meteorology Team + joined by countries of the HIRLAM Consortium " HIRLAM Chemical Branch + joined: Russian State HydroMet Univ, Univ Tartu, Univ Vilnus, Odessa State Envir Univ

False peak due to

• ff-line coupling

Enviro-HIRLAM: On-Line vs. Off-Line Comparison

(Korsholm et al., 2008)

ETEX: concentration at DK02 station for different coupling intervals: 10-360 min ETEX: concentration after 36 h for different coupling intervals

Changes in:

• temperature – up to 2-3 deg C,
• wind speed – up to 2-4 m/s,
• urban boundary layer height – up to 200 m,
• dry and wet deposition – up to 7%.

Enviro-HIRLAM: Aerosols Feedbacks

665 km 445 km

Case Study: 28 Jun – 3 Jul 2005

Difference (reference - perturbation) Temp 2 m, deg C Wind 10 m, m/s

(Korsholm et al., 2009)

Copenhagen, Denmark Normalized concentrations 24 hours forecast 48 hours forecast Normalized concentrations 24 hours forecast 48 hours forecast Copenhagen, Denmark

Collaboration – Danish Asthma Allergy Association, Finish Meteorological Institute

Enviro-HIRLAM: Birch Pollen Forecasting

Emission rate & fractions of birch trees Phenological model output Enviro-HIRLAM model output

Source: silam.fmi.fi

(Mahura et al., 2008)

Collaboration – Danish Road Directorate

Enviro-HIRLAM: Road Forecasting

(Mahura et al., 2009)

Add line source traffic emissions (daily and weekly variability)

781 road stretches in Ribe Amt region

16637 stretches for 296 roads

(at distances of 1 km)

34 32 26 41

• Concept: the chemical weather as two-way interacted meteorological

weather and chemical composition of the atmosphere.

• On-line integration of MesoMetMs and ACTMs enables the utilisation of all

meteorological 3D fields in ACTMs at each time step and the consideration

• f the feedbacks of air pollution on meteorological processes and climate

forcing.

• New generation of integrated models => not only for the chemical weather

forecasting, but also for climate change modelling, weather forecasting (e.g., in urban areas, severe events, etc.), air quality analysis and mitigations, long- term assessment chemical composition, etc.

• Main advantages:

– Only one grid for MMM and ACTM, no interpolation in space and time; – Physical parameterizations are the same, no inconsistencies; – All 3D meteorological variables are available at each time step; – No restriction in variability of meteorological fields; – Possibility to consider two-way feedback mechanisms; – Does not need meteo- pre/post-processors.

• Conclude - feedback mechanisms are important in CWF modelling and

quantifying direct and indirect effects of aerosols, and it is supported by simulation results.

Conclusions

Publications:

• Baklanov A., 2008: Integrated Meteorological and Atmospheric Chemical Transport Modeling:

Perspectives and Strategy for HIRLAM/HARMONIE. HIRLAM Newsletter, 53.

• Korsholm U.S., A. Baklanov, A. Gross, A. Mahura, B.H. Sass, E. Kaas, 2008: Online coupled chemical

weather forecasting based on HIRLAM – overview and prospective of Enviro-HIRLAM. HIRLAM Newsletter, 54.

• Baklanov A., U. Korsholm, A. Mahura, C. Petersen, A. Gross, 2008: ENVIRO-HIRLAM: on-line

coupled modelling of urban meteorology and air pollution. Advances in Science and Research, 2, 41-46

• Chenevez, J., Baklanov, A. and Sorensen, J. H., 2004. Pollutant transport scheme s integrated in a

numerical weather prediction model: model description and verification results. Meteorological Applications, 11, 265-275.

• Wyser K., L. Rontu, H. Savijärvi, 1999: Introducing the Effective Radius into a Fast Radiation Scheme
• f a Mesoscale Model. Contr. Atmos. Phys., 72(3): 205-218.
• Li, J., J.G.D. Wong, J.S. Dobbie, P. Chylek, 2001: Parameterisation of the optical properties of Sulfate
• Aerosols. J. of Atm. Sci., 58: 193-209.
• Seinfeld, J.H., S.N. Pandis, 1998: Atmospheric chemistry and physics. From air pollution to climate
• change. A Wiley-Interscience Publication. New-York.
• Baklanov A., Sorensen, H., J., 2001: Deposition parameterisation in ACT models, Physics and Chemistry
• f the Earth, vol. 26, No. 10, 787-799
• Baklanov, A. and U. Korsholm: 2007: On-line integrated meteorological and chemical transport

modelling: advantages and prospective. In: ITM 2007: 29th NATO/SPS International Technical Meeting

• n Air Pollution Modelling and its Application, 24 – 28.09.2007, University of Aveiro, Portugal, 21-34.
• Baklanov, A., B. Fay, J. Kaminski, R. Sokhi, 2007: Overview of Existing Integrated (off-line and on-

line) Mesoscale Meteorological and Chemical Transport Modelling Systems in Europe, WMO GAW Report No. 177, Joint Report of COST Action 728 and GURME, 107 pp. Available from: http://www.cost728.org

• Baklanov, A., A. Mahura, R. Sokhi (eds.), 2008: Integrated systems of meso-meteorological and

chemical transport models, Materials of the COST-728/NetFAM workshop, DMI, Copenhagen, 21-23 May 2007, 183 pp. Available from: http://www.cost728.org

• Direct effect - Decrease solar/thermal-IR radiation and visibility

– Processes needed: radiation (scattering, absorption, refraction, etc.) – Key variables: refractive indices, ext. coeff., SSA, asymmetry factor, AOD, visual range – Key species: cooling: water, sulfate, nitrate, most OC warming: BC, OC, Fe, Al, polycyclic/nitrated aromatic compounds

• Semi-direct effect - Affect PBL meteorology and photochemistry

– Processes needed: PBL/LS, photolysis, met-dependent processes – Key variables: T, P, cloud frac, stability, PBL height, photolysis rates, emission rates of met- dependent primary species (dust, sea-salt, biogenic)

• First indirect effect – Affect cloud drop size, number, reflectivity, and optical depth via CCN

– Processes needed: aero. activation/resuspension, cld. microphysics, hydrometeor dynamics – Key variables: int./act. frac, CCN size/comp., cld drop size/number/LWC, COD, updraft vel.

• Second indirect effect - Affect cloud LWC, lifetime, and precipitation

– Processes needed: in-/below-cloud scavenging, droplet sedimentation – Key variables: scavenging efficiency, precip. rate, sedimentation rate

• All aerosol effects

– Processes needed: aero. thermodynamics/dynamics, aq. chem., precursor emi., water uptake – Key variables: aerosol mass, number, size, comp., hygroscopicity, mixing state

⇒ High-resolution on-line models with a detailed description of the PBL structure are

necessary to simulate such effects ⇒ Online integrated models are necessary to simulate correctly the effects involved 2nd feedbacks

Aerosol Effects to be Considered