Скачать презентацию PREV AIR An operational system for large Скачать презентацию PREV AIR An operational system for large

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PREV ’AIR : An operational system for large scale air quality monitoring and forecasting PREV ’AIR : An operational system for large scale air quality monitoring and forecasting over Europe http: //www. prevair. org

Two main objectives • Diagnostic and prospective studies in the field of atmospheric air Two main objectives • Diagnostic and prospective studies in the field of atmospheric air pollution at the European scale § Technical support to the French Ministry for Ecology in the framework of international negotiations about transboundary air pollution (implementation of integrated policies aiming at reducing pollutant emissions in Europe) • Delivery of a daily information related to air quality over France and Europe § To complement the national air quality monitoring network (~40 AASQA, >2000 sensors, >700 stations) • 18/03/2018 • 2 / 2

Architecture of the PREV’AIR System 3 D Chemistry Transport Models. . . • CHIMERE Architecture of the PREV’AIR System 3 D Chemistry Transport Models. . . • CHIMERE / IPSL-CNRS-INERIS • MOCAGE / METEO-FRANCE … Driven by meteorological forecasts • AVN / NCEP global data (+ MM 5 higher resolution forecasts) • ARPEGE, ALADIN Other input data • Emissions inventory § EMEP § EDGAR • Chemical forecasts Forecast s (maps) Analysis (maps) NRT observations data from local AQ monitoring associations (BASTER / ADEME database. . . ) Numerical AQ forecast data Observations (maps), scores BASTER Boundary concentrations § MOZART+GOCART § MOCAGE • Low and high resolution meteorological forecasts Other NRT data AA S Q A

The BASTER Near Real-Time Data Base 41 French AQ related organisms (AASQA) The BASTER Near Real-Time Data Base 41 French AQ related organisms (AASQA) "real time" national database BASTER O 3 680 monitoring stations NO 2 SO 2 PM 10 PM 2. 5 hour Local servers ADEME AASQA web sites National web site • LOCAL 18/03/2018 • 2 / 2 PREV ’AIR NATIONAL

The CHIMERE Model Set-Up in PREV’AIR • Domains Over Europe Over France § Horizontal The CHIMERE Model Set-Up in PREV’AIR • Domains Over Europe Over France § Horizontal resolution: 0. 5°x 0. 5° 0. 15° x 0. 1° § Vertical resolution: 8 levels from surface pressure up to 500 h. Pa • Meteorological forecast data § AVN / NCEP for initialisation and boundary conditions § MM 5 higher resolution forecasts (36 km) • Chemical scheme § MELCHIOR reduced (~45 species, ~120 reactions) • Aerosol module § Dust, PPM, SOA, nitrates, sulphates, ammonium, Water Contents n 25 reactions (aqueous and heterogeneous phase) Other model configurations: è meteorology: ECMWF, ARPEGE / ALADIN èBC: MOZART, LMDZ-INCA, GOCART è See http: //euler. lmd. polytechnique. fr/chimere

The MOCAGE Model Set-Up in PREV’AIR • Domains Global Model / Over Europe / The MOCAGE Model Set-Up in PREV’AIR • Domains Global Model / Over Europe / Over France § Horizontal resolution: 4°x 4° 0. 5° x 0. 5° 0. 1° x 0. 1° § Vertical resolution: 47 levels from surface pressure up to 5 h. P • Meteorological forecast data § ARPEGE and ALADIN • Chemical scheme § RACMOBUS (118 species, 381 reactions) • No aerosol module

PREV’AIR Outputs: Daily Forecasts • Available at D+0, 01 -06 h LT • Daily PREV’AIR Outputs: Daily Forecasts • Available at D+0, 01 -06 h LT • Daily peak and averaged concentration maps § D+0, D+1, D+2 • Pollutants: O 3, NO 2, particulate matter • Global scale, Europe, France User part: - Output from both models over Europe and France - In summer, statistically adapted O 3 fields O 3, peak forecast(µg/m 3) 20040729 D+0

PREV’AIR Outputs: Scores Summer 2004, PM 10 daily mean concentrations User part: - Maps PREV’AIR Outputs: Scores Summer 2004, PM 10 daily mean concentrations User part: - Maps - Time series Summer 2004 O 3 daily peak concentrations

PREV ’AIR Outputs: Numerical Forecast Data • PREV’AIR numerical data § Extraction over user-defined PREV ’AIR Outputs: Numerical Forecast Data • PREV’AIR numerical data § Extraction over user-defined domains § Available through the Internet for AQ related institutions (user accounts) • Applications § 3 D data: BC for local AQ simulations : ESMERALDA (Ile-de-France area), AIRES (PACA region), … § 2 D data: for AQ monitoring and forecast (interpolation, statistical modelling) • Feed-back from PREV’AIR users § Meetings with AQ networks for system improvement § Day-by-day debriefing about forecasts . . . Up to now, about 40 users

PREV ’AIR Outputs: Maps of NRT observations • Maps of measured daily peak and PREV ’AIR Outputs: Maps of NRT observations • Maps of measured daily peak and mean concentrations: § O 3, NO 2, PM 2. 5 and PM 0 § Observations performed between 0 h and t § For D+0 and the previous six days Daily mean concentrations of PM 10 at surface measured on the 12 nd of September 2004

PREV ’AIR Outputs: Analyses 03 simulationcorrected by observations(µg/m 3) • Modelled concentrations corrected with PREV ’AIR Outputs: Analyses 03 simulationcorrected by observations(µg/m 3) • Modelled concentrations corrected with observations: § Observations collected in real-time for 150 stations § Statistical adaptation: kriging method § Available at D+0 for D+0 and D-1 O 3 simulation(µg/m 3) 3 O 3 correctivefield (µg/m ) July 31, 2004

PREV’AIR Analyses: Scores Summer 2003, O 3 daily peak concentrations 13 rural stations 67 PREV’AIR Analyses: Scores Summer 2003, O 3 daily peak concentrations 13 rural stations 67 suburban stations 179 urban stations • 18/03/2018 • 2 / 2

PREV ’AIR Outputs: Annual Budgets 3) Ozone, averaged peak (µg/m during summer 2003 Ozone, PREV ’AIR Outputs: Annual Budgets 3) Ozone, averaged peak (µg/m during summer 2003 Ozone, # of hours ofexceedance of the 180 µg/m 3 threshold during summer 2003 Ozone, # of hours ofexceedance of the 240 µg/m 3 threshold during summer 2003

Future developments • Coming soon: § Set up of summer 2005 forecast season § Future developments • Coming soon: § Set up of summer 2005 forecast season § Implementation of the last CHIMERE version (V 200501 G) with LMDZ-INCA BC • Better accounting for observations: § Analyzed concentrations to initialize the forecast § Feasability study for PM 10 concentrations analysis § Feasability study for data assimilation of 3 D data (LIDAR, satellites) • Ensemble forecast: § Multi model forecast (hybridization methods)