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A NEW OPERATIONAL AIR QUALITY PREDICTION SYSTEM OVER ITALY

8 th Annual CMAS Conference, Chapel Hill, NC, October 19-21, 2009. A NEW OPERATIONAL AIR QUALITY PREDICTION SYSTEM OVER ITALY.

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A NEW OPERATIONAL AIR QUALITY PREDICTION SYSTEM OVER ITALY

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  1. 8th Annual CMAS Conference, Chapel Hill, NC, October 19-21, 2009 A NEW OPERATIONAL AIR QUALITY PREDICTION SYSTEM OVER ITALY Maria Chiara Metallo1*, Attilio A. Poli2, Fabrizio De Fausti1, Luca Delle Monache3, Pierluca Di Giovandomenico1, Cristina Faricelli1, Margherita Moreno1, Alessandra Scifo1 1Environmental System Analysis srl, Bracciano, Rome, Italy 2Take Air srl, Bracciano, Rome, Italy 3National Center for Atmospheric Research, Research Applications Laboratory, Boulder, CO CONTACTS: Chiara Metallo (c.metallo@takeair.info), Luca Delle Monache (lucadm@ucar.edu)

  2. Outline • LaMiaAria modeling system • Nesteddomains • Model configurations • The Dustmodel • Emissions • Operationaltimelines • Preliminary Results • Future work

  3. LaMiaAria Modeling System Structure

  4. NestedDomains Domain CoverageSpatialresolution Domain1 Europe + North Africa 54 km (77 X 111) Domain2 intermediate 18 km (84 X 78) Domain3 Italy 6 km ( 177 X 213)

  5. CMAQ CONFIGURATION Current operational CMAQ forecast still uses static profile lateral boundary condition (LBC). The initial conditions (IC) for CMAQ are set from the previous forecast cycle. • ADOPTED SCHEMES: • Yamartino global mass-conserving scheme to calculate horizontal and vertical advection • diffusion coefficient based on local wind deformation • calculate vertical diffusion using the Asymmetric Convective Model version 2 • deactivate plume in grid model • 2nd generation CMAQ aerosol deposition velocity routine • RADM-based cloud processor that uses the asymmetric convective model to compute convective mixing • Aerosol module : the 3rd generation modal CMAQ aerosol model (AERO 3)

  6. MM5 CONFIGURATION NestedDomains D54 80 x 114 D18 94 x 91 D6 193 x 216 Vertical Layers 29 sigma pressure • ADOPTED SCHEMES: • NCEP /GFS data • No gridanalysisnudging • No observationnudging • Reisnermixedphase • Kain-fritschcumulusparameterization (54 and 18 km grid) • MRF pbl (Troen and Mahrt, 1986) • Atmosphericradiationscheme: CLOUD (Dudhia) • Shallowconvectivescheme • Multi-layer soil model

  7. THE DUST MODEL The algorithm used to assess surface dust flux is based on the Dust Entrainment and Deposition model (DEAD, Zender, 2002). The fluxofdust, expressed in Kg/m2s, released in the atmosphere and thantransportedby CMAQ (in 2 bins fine/coarsefractioned following D’Almeida [1987] sizedistribution) isgivenby: F = α Q(u*, u*t) · Am · T · S S Erodibility factor (toreveal Hot Spots) [Ginoux, 2001] T TunableFactor Am Bare soilfraction[Zender, 2003] α = f(soiltexture)= 100exp[(13.4 Mclay-6.0)ln10] MobilityEfficiency Q = Q(u*,u*t) =const· u*t3[1- (u*t/ u*)2] [1+ u*t/ u*] HorizontalFlux u*= (/ ρ)1/2 FrictionVelocity u*t= f(D, Re*t, ρp ) · FcThresholdFrictionVelocity [Iversen & White, 1982]

  8. SaharandustOutbreakoverSicily 15-16/05/09

  9. 54 and 18 km GRID EMISSIONS • For the 54 and 18 km grid, the contributions of the anthropogenic sources (road transport, non road transport, industry, agricultural sources, etc.) are implemented using the last available version of: • European Monitoring and Evaluation Programme (EMEP) emission database; • Emission Database for Global Atmospheric Research (EDGAR), excluded particulate matter, for north African areas; • European Pollutant Emission Register (EPER) for industrial point sources. The spatial disaggregation is evaluated according to the methodology of the surrogate variables, using geographic data in a GIS platform (primary traffic, CORINE land cover by European Environment Agency) related to the emissions sources.

  10. 6km GRID EMISSIONS The inventory of emissions for the Italian national territory (6 km grid) is carried out using the National Emission Inventory provided by the Institute for Environmental Protection and Research (ISPRA), available according to the CORINAIR classification The municipal spatial disaggregation is carried out from the emissive data on a provincial base according to the methodology of the proxy (or surrogate) variables.

  11. Operational Timelines

  12. 50 40 30 20 10 0 NO2Modeled (blue) VS Observed (red) values PM10 Modeled (blue) VS Observed (red) values O3Modeled (blue) VS Observed (red) values O3Modeled (blue) VS Observed (red) values Preliminary results: hourly values 35 30 25 20 15 10 5 0 Ponzone (Al, 14 May 2009 ) for PM10, Cremona (15 June 2009) for NO2, Alessandria (20 June 2009) and Acqui Terme (8 May 2009) for O3. µg/m3 µg/m3 140 120 100 80 60 40 20 0 140 120 100 80 60 40 20 0 µg/m3 µg/m3

  13. Preliminary Results FractionalBias (114 stations) (83 stations) (53 stations)

  14. Results European directive for modeling uncertainty LM: European AQ Limit value (Target value for O3) EVA with values exceeding the regulatory target (50%) depicted in red

  15. Results Scatter plots 24-h Mean for PM10 in 53 stations. 24-h Max for O3 and NO2 in 83 and 114 stations respectively. Analyzed Period: 1 July- 30 September Summer O3 daily-max modeled values are 96.4% inside the range ±30% and 86.7% inside ±20%

  16. AQ Forecast samples High summer ozone in Italy

  17. Tomorrow NO2 and O3 forecast NO2 O3

  18. Tomorrow SO2 and PM10 forecast SO2 PM10

  19. LaMiaAria web site: www.lamiaaria.it

  20. Region maps

  21. Future works Short-term (1 year): * WRF *AERO4 *CB-V * Emission improvements road transport and natural sources Medium-term (1-3 years): * Postprocessing, i.e., bias correction (e.g., KF-based algorithm) * Global CTM BC Long-term (3-5 years): * Probabilistic prediction system based on ensemble data assimilation

  22. 8th Annual CMAS Conference, Chapel Hill, NC, October 19-21, 2009 A NEW OPERATIONAL AIR QUALITY PREDICTION SYSTEM OVER ITALY Thank you for your attention Luca Delle Monache (lucadm@ucar.edu) National Center for Atmospheric Research, Research Applications Laboratory Boulder, CO CONTACTS: Chiara Metallo (c.metallo@takeair.info), Luca Delle Monache (lucadm@ucar.edu)

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