Online integrated meteorology-chemistry models for air quality forecasting (EuMetChem experience) A. Baklanov (1), H. Schluenzen (2), P. Suppan (3), M. Gauss (4), Ch. Seigneur (5), D. Brunner (6), S. Joffre (7), X. Kong (8) (1) Danish Meteorological Institute, Copenhagen ([email protected], +45 391 57460), (2) Meteorologisches Institut, Universitat Hamburg, (3) Karlsruhe Institute of Technology (KIT), Institute for Meteorology and Climate Research, (4) Norwegian Meteorological Institute, (5) CEREA, École des Ponts ParisTech, (6) Empa, Swiss Federal Laboratories for Materials Science and Technology, (7) Finnish Meteorological Institute, (8) University of Hertfordshire and the COST ES1004 team: Aksoyoglu S., Baldasano J.M., Carmichael G., Douros J., Flemming J., Forkel R., Galmarini S.,Grell G.,Hirtl M.,Kaas E., Kaasik M., Kallos G., Korsholm U., Lohmann U., Manders A., Maurizi A., Moussiopoulos N., Orba O., Rao S.T., Savage N., R. Sokhi, Sørensen B., Vignati E., Vogel B., Zakey A., Zhang Y. 4th International Workshop on Air Quality Forecasting Research (IWAQFR) 4th International Workshop on Air Quality Forecasting Research (IWAQFR)
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Online integrated meteorology-chemistry
models for air quality forecasting
(EuMetChem experience)
A. Baklanov (1), H. Schluenzen (2), P. Suppan (3), M. Gauss (4), Ch. Seigneur (5), D. Brunner (6), S. Joffre (7), X. Kong (8)
(1) Danish Meteorological Institute, Copenhagen ([email protected], +45 391 57460), (2) Meteorologisches Institut, Universitat Hamburg, (3) Karlsruhe Institute of Technology (KIT), Institute for
Meteorology and Climate Research, (4) Norwegian Meteorological Institute, (5) CEREA, École des Ponts ParisTech, (6) Empa, Swiss Federal Laboratories for Materials Science and
Technology, (7) Finnish Meteorological Institute, (8) University of Hertfordshire
and the COST ES1004 team: Aksoyoglu S., Baldasano J.M., Carmichael G., Douros J., Flemming J., Forkel R., Galmarini S.,Grell
G.,Hirtl M.,Kaas E., Kaasik M., Kallos G., Korsholm U., Lohmann U., Manders A., Maurizi A., Moussiopoulos N., Orba O., Rao S.T., Savage N., R. Sokhi, Sørensen B., Vignati E., Vogel B.,
Zakey A., Zhang Y.
4th International Workshop on Air Quality Forecasting Research (IWAQFR)
4th International Workshop on Air Quality Forecasting Research (IWAQFR)
Physical and Chemical Weather: Off-line coupling
• Numerical Weather Prediction (Meteorological) and Air Quality Modelling – two independent problems and research communities.
• Chemical weather forecasting (CWF) - is a new, quickly developing and growing area of atmospheric modelling.
The most common simplified concept of CWF 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 (no feedbacks).
NWP:
CWF:
European operational CWF models
• Kukkonen et al, ACP, 2011: overview of 18 selected regional CWF models used in Europe: ALADIN-CAMx (Austria), CAMx-AMWFG (Greece), EURAD-RIU (Germany), Enviro-HIRLAM (Denmark and others), FARM (Italy), LOTOS-EUROS (The Netherlands), MATCH (Sweden), MM5-CAMx (Greece), MM5-CHIMERE (France and Portugal), MM5/WRF-CMAQ (Spain, UK), MOCAGE (France, Spain, Romania), NAME (United Kingdom), OPANA (Spain and others), RCG (Germany), SILAM (Finland, Estonia, Russia, Lithuania and Spain), SKIRON/Dust (Greece), THOR (Denmark), WRF-Chem (Spain and others)
• Only two of them (Enviro-HIRLAM and WRF-Chem) are online coupled
• Recently published Springer book (Baklanov et al., 2011) with description of main coupled ACT-MM models => 15 online =>
ISBN: 978-3-642-13979-6
• European CWF portal: http://www.chemicalweather.eu/Domains
Physical and Chemical Weather Forecast: New concept
• Many experimental studies and research simulations show that atmospheric processes (meteorological weather, incl. precipitation, thunderstorms, radiation, clouds, fog, visibility and PBL structure) depend on concentrations of chemical components (especially aerosols) in the atmosphere.
• Meteorological data assimilation (in particular assimilation of radiances) depends on the chemical composition.
• Studies also show that air quality forecasts loose accuracy when CTM’s are run offline.
New generation of online integrated meteorology and chemistry modelling systems for predicting atmospheric composition, meteorology and climate change is really needed.
Online integrated or online access Atmospheric Chemistry-
Meteorology (ACM) models developed or applied in Europe
Page 1:
Model, Country, Web-site
Meteorology component
Gas phase chemistry and aerosol component
Feedback of pollutants to meteorology
Applications Scale
1 BOLCHEM, Italy http://bolchem.isac.cnr.it/
BOLAM SAPRC90 gas phase chemistry, AERO3 aerosol module
15 REMO, Germany Langmann, 2000, Langmann et al., 2008
REMO RADM gas phase, Walcec&Taylor liguid phase, M7 (Vignati et al., 2004)
none, but possible Episodes (particularly volcanic ash)
Continental to regional
16 WRF/Chem, US (used in Germany, UK, Spain, etc.), Grell et al., 2005; Fast et al., 2006; Zhang 2008; Zhang et al., 2010a, b, 2011a, 2012c, d, e, f; Chuang et al., 2011)
WRF RADM, RACM, RACM-MIM with modal aerosol module or CBM-Z/CB05 with sectional aerosol modules MOSIAC and MADRID, liquid phase chemistry
Direct aerosol effect, indirect aerosol effect
Episodes, CWF, climate-chemistry
Continental to regional , urban, cloud-resolving and LES
17 WRF-CMAQ Coupled System, USA (used in UK) , http://www.cmaq-model.org/cmaqwiki
WRF Gas-phase mechanisms: CB05 with updated toluene chemistry, SAPRC07TB; Modal aerosols based on the AERO6 CMAQ module
Direct aerosol effects on radiation and photolysis
Episodes to annual
Urban to Hemispheric
Online integrated or online access Atmospheric Chemistry-
Meteorology (ACM) models developed or applied in Europe
Page 3:
Main meteorology and chemistry/aerosols interacting
weather calling for an integrated approach (Collaboration with COST ES0602 and ES0603)
Definitions:
• Physical weather is defined as the short-term atmospheric state and variation, characterized by physical atmospheric variables, such as solar radiation, temperature, humidity, pressure and wind speed and direction.
• Chemical weather is defined as the short-term (less than two weeks) state and variation of the atmospheric chemical composition.
• By analogy, the short-term state and variation of concentrations of bioaerosols, such as allergenic pollen and fungal spores, can be defined as the biological weather.
Top: concentration as function of time at F15 and DK02 for different coupling intervals: 30, 60, 120, 240,
360 minutes. Bottom: concentration after 36 hours with the same coupling intervals
ETEX: On-line/Off-line Comparison
False peak due to
off-line coupling
Korsholm, Baklanov, Gross, Sørensen, AE, 2009
Changes in: temperature – up to 2-3 (mon. av. 0,5) deg C, wind speed –
up to 2-4 m/s, urban boundary layer height – up to 200 m; local thermally
induced circulations redistribute the aerosols and trace gases
Enviro-HIRLAM: Indirect Aerosols Feedbacks
665 km
445
km
Paris Case Study:
28 Jun – 3 Jul 2005
Difference (reference - perturbation)
Temp 2 m, deg C
Wind 10 m, m/s
(Korsholm et al., 2010)
Vertical NO2 profile in point of max.
increase (49.2N;2.7E) during daytime
2005-06-29 at 12 UTC for the REF
simulation (red) and the simulation
including the indirect effects (green)
Dominating process in this case:
Paris Aerosols Increased Cloud cover (2nd aerosol indirect effect)
Daytime cooling, night time heatingShortwave, long wave response
Pre
ssur
e (p
a)
concentration (μg m-3)
Pre
ssur
e (p
a)
concentration (μg m-3)
WRF/Chem: Aerosol Effects on Meteorology & Chemical Composition
Differences in ozone (left) and PM10 (right) concentrations in July 2006 between two WRF/CHEM simulations.
Simulation BASE does not consider interactions between aerosols and meteorology. Simulation RFBC considers
both direct and indirect effects (RFBC).
Forkel et al. (Atmos. Environ. 2012).
a b c
West to East cross-section of rain mixing ratio (colour palette in g kg-1) and ice mixing ratio (red line contours in g kg-1) at the time of highest cloud
top over Haifa. a) 9 UTC 29 January 2003 assuming 5% hygroscopic dust. b) 10 UTC 29 January 2003 assuming 20% hygroscopic dust. c) 9 UTC
29 January 2003 assuming 5% hygroscopic dust and INx10.
Solomos et al. (ACP, 2011)
RAMS/ICLAMS: Effects of pollution on precipitation
• Suggested concept - chemical weather as two-way interacted meteorological weather and chemical composition of the atmosphere => COST Action ES1004 EuMetChem.
• On-line integration of MesoMetMs and ACTMs enables: utilisation of all meteorological 3D fields in ACTMs at each time step;
consideration of feedbacks of air pollution on meteo-processes & climate forcing.
• New generation of integrated models - not only for CWF, but also for climate change modelling, NWP (e.g., in urban areas, severe events, etc.), air quality and mitigations, long-term assessment of chemical composition, etc.
• Main advantages of on-line coupling: 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.
• Feedback mechanisms - important (supported by simulation results) in CWF modelling and quantifying direct and indirect effects of aerosols (and probably GHGs).
• Indirect aerosol feedbacks: sensitivity of meteorology and chemistry: full chain is needed, strong non-linearity, e.g. indirect effects induce large changes in precipitation and chemistry.
• Evaluation of online models with feedbacks is needed: joint study within AQMEII Phase2
Conclusions
Future needs and recommendations
• Although we may continue to develop and run modeling systems of Earth system components separately, a scientific perspective would argue for an eventual migration to integrated modeling systems that allow two-way interaction of physical and chemical components of chemical weather forecasting systems.
• While this may be the obvious approach for air quality forecasting, more research and discussion may be needed for NWP.
• AQ and NWP communities should work more closely together.
• National weather centers are advised to include chemistry/aerosol interactions into NWP systems extending their forecasts to the chemical weather as well.
• Centers that are responsible for AQ forecasting should seriously consider online modeling as a necessary part of their suite of forecasts. Additional advantages will arise from cross evaluations for both disciplines. Chemical species will allow identification of short comings in currently used forecast models as well as lead to better use of meteorological data assimilation.
• Other outcomes from such collaboration and the online coupling may include benefits for:
(i) meteorological weather forecasting (e.g., in urban areas, severe weather events, fog, and visibility, UV-radiation and solar energy, etc.),
(ii) chemical weather/air quality and bio-meteorology forecasting,
(iii) seasonal and decadal air quality/climate prediction,
(iv) global and regional projections of the climate/Earth system.
(Grell and Baklanov, 2011)
Relevant publications: • Baklanov, A., 2010: Chemical weather forecasting: a new concept of integrated modelling, Advances in Science
and Research, 4, 23-27.
• Grell, G. and A. Baklanov, 2011: Integrated Modeling for Forecasting Weather and Air Quality: A Call for Fully Coupled Approaches, Atmospheric Environment, 8, 2895–2932, doi:10.1016/j.atmosenv.2011.01.017.
• Baklanov, A., A, Mahura and Sokhi, R. (Eds.) 2011: "Integrated Systems of Meso-Meteorological and Chemical Transport Models, Springer Book, ISBN:978-3-642-13979-6, p. 242.
• Kukkonen, J., Olsson, T., Schultz, D. M., Baklanov, A., Klein, T., Miranda, A. I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K. E. J., Karatzas, K., San José, R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., and Eben, K, 2012: A review of operational, regional-scale, chemical weather forecasting models in Europe, Atmos. Chem. Phys., 12, 1-87, doi:10.5194/acp-12-1-2012, 2012.
• Klein, T., J. Kukkonen, Å. Dahl, E. Bossioli, A. Baklanov, A.F. Vik, P. Agnew, K.D. Karatzas, M. Sofiev, 2012: Interactions of physical, chemical and biological weather calling for an integrated assessment, forecasting and communication of air quality, Ambio , 05/2012, DOI: 10.1007/s13280-012-0288-z
• Zhang, Y., M. Bocquet, V. Mallet , Ch. Seigneur, A. Baklanov, 2012: Real-Time Air Quality Forecasting: part I: History, techniques, and current status; part II: State of the Science, Current Research Needs, and Future Prospects. Atmospheric Environment, 82 p., http://dx.doi.org/10.1016/j.atmosenv.2012.02.041 and ...06.31
• Alapaty, K., R. Mathur, J. Pleim, Ch. Hogrefe, S. T. Rao, V. Ramaswamy, S. Galmarini, M. Schaap, R. Vautard, P. Makar, A. Baklanov, G. Kallos, B. Vogel, R. Sokhi, 2012: New Directions: Understanding Interactions of Air Quality and Climate Change at Regional Scales. Atmospheric Environment. 49: 419–421
• Forkel, R., J. Werhahn, A.B. Hansen, S. McKeen, S. Peckham, G. Grell, P. Suppan. 2012: Effect of aerosol-radiation feedback on regional air quality - A case study with WRF/Chem. Atmospheric Environment 53, 202-211.