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Chemical Data Assimilation at the Meteorological Service of Canada

Chemical Data Assimilation at the Meteorological Service of Canada. Richard Ménard, Alain Robichaud Paul-Antoine Michelangelli, Pierre Gauthier, Yan Yang, and Yves Rochon. Operations - Assimilation of surface ozone measurements Observation simulation experiment

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Chemical Data Assimilation at the Meteorological Service of Canada

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  1. Chemical Data Assimilation at the Meteorological Service of Canada Richard Ménard, Alain Robichaud Paul-Antoine Michelangelli, Pierre Gauthier, Yan Yang, and Yves Rochon

  2. Operations • - Assimilation of surface ozone measurements • Observation simulation experiment • - vertical profile lidar/total column scanning • Research • - development of coupled meteorology-chemistry • model and data assimilation

  3. Models • CHRONOS Limited area CTM • gas phase chemistry • operational since 2001 • North America domain: 24 km • emission inventory • (forest fires emissions) • AURAMS Limited area CTM • gas phase, PM , aqueous chemistry • operational (parallel run) since 2004 • Online coupling with operational meteorological • weather forecast model GEM

  4. Near real-time ozone objective analysis http://www.msc.ec.gc.ca/aq_smog/analysis_e.html • Assimilation and objective • analysis using the model • CHRONOS • Objective analysis, each • hour, 24/7, year round • On the web since • (experimental) June 2004 • Multiyear analyses • since the summer 2002 • Plans for operational • implementation for • spring 2006

  5. Enhancement of the observation network and real-time data transmission Additional rural and remote sites US EPA AirNow ground level ozone observations ~ 1500 hourly observations

  6. Four additional ozone sondes in southern Canada for 2004 summer measurement campaign Data available on WOUDC and NATChem

  7. Distribution of TEOM site across Canada. Distribution of AEROCAN

  8. obs – model (obs loc) = (true + obs error) - (true + model error) = obs error – model error · distance (km) Error statistics

  9. Observations Emissions Chemical model Analysis increment Objective analysis Met fields Error statistics Ozone objective analysis and assimilation using CHRONOS

  10. FOREST COMMERCIAL RESIDENTIAL AGRICULTURAL INDUSTRIAL Total variance 269.9 354.9 363.5 332 400.8 Forecast error variance 212.8 278.6 286.5 275.8 297.6 Correlation length scale 412.1 333.4 334.1 313.7 308.3 Observation error variance 57.1 76.3 76.98 56.2 103.2 Number of sites 20 53 85 47 12 • Best overall fit with first order auto-regressive correlation • model (FOAR) • Fit of observation error variance, forecast error variance and • correlation length scale • Classification in terms of land use was found to be most significant

  11. Observation error variance – CHRONOS v2.5.0 15 EDT - August 2004

  12. Forecast error variance – CHRONOS V2.5.0 15 EDT- August 2004

  13. Monitoring of the error statistics chi-square

  14. Verification Verifying against observations not used to produce the analysis 1/3 of observations used for verification (red) 2/3 of observation used to produce the analysis (blue)

  15. Monitoring of the error statistics in operational mode using previous year statistics

  16. Analysis error variance. Reduction due to observations Provide a method for observation network design

  17. Applications • Real-time best analysis for surface ozone (tool for environmental forecaster available on a hourly basis) • Ozone climatology (concentrations, dose, cumulative index, SUM60,AOT40, flux, etc.) • Give insight into possible model bugs & errors • Optimal design of measurement network • Forecasting • Re-analysis (using CHRONOS in a 24H assimilation hindcast mode)

  18. Maps of SUM60 (cumul. Sum > 60 ppb)(Summer 2002) MODEL OBSERVATIONS OBJECTIVE ANALYSIS

  19. AVG. FLUX OF OZONE TO SURFACEVD*[ozone] – Aug. 7-30 2002 ppb*m/s ppb*m/s NO O3 ASSIMILATION WITH O3 ASSIMILATION

  20. Incremental analysis vs cloudCase study. May 02 2004 20Z

  21. Prediction (assimilation) ON OFF ON

  22. Impact of assimilating ozone on other species

  23. Impact of assimilating ozone on other species

  24. Impact of assimilating ozone on other species

  25. Ongoing and future work • Use of new biogenic emissions (AURAMS)

  26. OSSE capabilities • Simulate an observation system (e.g. a new instrument) in a • data assimilation environment to assess the impact of the • observation system • Simulated truth, i.e. nature run, is created by a different model: • SEF with CMAM chemistry • The “observations” are drawn from the nature run • 3D Var + GEM_Tracer is used as the assimilation system • ORACLE space-based Differential Absorption Lidar (DIAL) • Ozone ; 1 km vertical resolution from 500 hPa to 1 hPa • TOVS total column ozone

  27. Vertically resolved measurements

  28. Forecast error variance ORACLE TOVS ORACLE + TOVS

  29. Environment Canada Meteorological Service of Canada Environnement Canada Service Météorologique du Canada Chemical-Dynamical Coupling in Data Assimilation Richard Ménard, Simon Chabrillat(*), Martin Charron, Dominique Fonteyn(*), Pierre Gauthier, Bin He, Jerzy Jarosz(**), Alexander Kallaur, Jacek Kaminski (**), Mike Neish, John McConnell, Alain Robichaud, Yves Rochon and Yan Yang Meteorological Service of Canada *Belgium Institute for Space Aeronomy **York University

  30. Outline • Objectives of the study • Implementation • Issues / Challenges • development of GCCM • development of coupled meteorology-chemistry • data assimilation system • computational • data assimilation

  31. Development of General Circulation and Chemistry Model (GCCM) Dynamics and physics • Global Environmental Multiscale (GEM) model • operational NWP model at Meteorological Service of Canada • semi-Lagrangian, adjoint + TLM • global uniform/variable resolution • stratospheric version • hybrid vertical coordinate • 80 levels, top 0.1 hPa • 240 × 120 (1.5 degree) • radiation, k-correlated method (Li and Barker 2003) uses as input • H2O, CO2, O3, N2O, CH4, CFC-11, CFC-12, CFC-113, CFC-114 • sulfate, sea salt, and dust aerosols. • non-orographic gravity wave drag (Hines)

  32. Chemistry • Kinetic PreProcessor (KPP) symbolic computation to generate • production and loss terms • jacobian, hessian, LU decomposition matrices • Online J calculation (MESSy code, Landgraf and Crutzen 1998) • All species advected and gas phase chemistry solved • with Rosenbrock or Fully implicit chemical solver (45 min time step) • Implementation of TLM and adjoint. • Choice of species and chemical reaction (gas phase) • CMAM / BIRA-IASB • Choice of bulk or sized-resolved PSC’s and aerosols • (heterogeneous chemistry) • Canadian Middle Atmosphere Model (CMAM) • Danish Meteorological Institute

  33. CMC NCEPUK MetOfficeECMWF Data assimilation system • Stratospheric assimilation inherits the characteristics of the operational assimilation 3D Var and 4D Var • AMSU-A (channel 10-14 added) and AMSU-B microwave channels • GEOS infrared radiances • Data quality control with BG check and QC-Var • Conventional meteorological data

  34. Meteorological 4D Var (operational since 03/05) • 4D Var offers a more natural framework for the assimilation of time • series of data, such satellite data • Decomposition of assimilation algorithms in basic operations, e.g. PALM • Modular approach to the development of 4D-Var • 3D-Var: observation operators, background-error representation, etc. • GEM: direct (nonlinear), tangent linear and adjoint models • Coupling of those modules is insured by an external coupler • Assimilation is now running on the IBM-p690 • Current cycle: 5 nodes (40 PEs)

  35. Chemical data assimilation • MSC: Real-time assimilation • of surface ozone since 2003 • http://www.msc.ec.gc.ca/aq_smog/analysis_e.html • York University-MSC : Coupled meteorology-chemistry data assimilation • MOPITT CO • Siberian forest • fires August 2002 • http://www.maqnet.ca • BASCOE : • Belgium Assimilation System for • Chemical Observation from Envisat • (operational 4D Var CTM) • http://www.bascoe.oma.be

  36. Development of the coupled dynamical-chemical data assimilation system • 3D Var-CHEM • Addition to an abritrary number of chemical tracer • in the operational 3D Var • Can accommodate cross-error covariance • either operator form or explicit form

  37. Not all chemical species are observed • Analysis splitting ? only observed variables in control vector • The problem of minimizing • with respect to x and u is mathematically equivalent to minimizing • followed by the update (Ménard et al. 2004) • 4D Var extension • Uses same solver as in 3D Var tangent linear integration

  38. Computational Issues • Distributed computing / distributed memory • GCCM OpenMP , MPI • VAR-CHEM OpenMP , MPI (temp. solution analysis splitting ) • Transport • Can save computation in semi-Lagrangian advection transport • • upstream point (D or M) is the same for all advected species • x x x • x x x • x x x • • interpolation weights Ci(x) are the same for all advected species • e.g. cubic Lagrange interpolation A M D

  39. Data assimilation issues Cross-error covariance models e.g. Temperature-Ozone • Because the ozone production • rate increases with decreasing • temperatures, in regions • dominated by photochemistry • (above 35 km) a negative • correlation between temperature • and ozone would occur • Haigh and Pyle (1982), Froideveau • et al. 1989, Smith 1995, Ward 2002

  40. For data at a given level, perturbations can fit an expression • of the form • with a correlation that can be up to 0.92 above 42 km, and • increase linearly from zero to 0.92 between 37 km to 42 km.

  41. Where we are after five months • Development of the GCCM with York chemistry completed, and • heterogeneous chemistry well underway. • Kinetic preprocessor completed • Validation of stratospheric meteorology has been made in both • climate and assimilation mode • 3D Var-CHEM is completed and operational • Constructing the error statistics using differences of forecast (Rochon’s method) • Development of 4D Var underway Short term plans (next three months) • Validation of York (gas phase and heterogeneous) chemistry • Completion of the chemical interface, and implementation of BIRA chemistry • Validation of the error statistics using innovations and NMC method • Validation of the coupled chemistry-dynamics assimilation over selected period • of time • Implementation of coupled chemical-dynamical 4D Var • Start of monitoring of MIPAS observations – development of bias correction

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