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. 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 http://www.msc.ec.gc.ca/aq_smog/analysis_e.html Near real-time ozone objective analysis

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

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

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

10. 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 1247855320 Number of sites 103.256.276.9876.357.1 Observation error variance 308.3313.7334.1333.4412.1 Correlation length scale 297.6275.8286.5278.6212.8 Forecast error variance 400.8332363.5354.9269.9Total variance INDUSTRIAL AGRICULTURALRESIDENTIALCOMMERCIAL FOREST

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 statisticschi-square

15. 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)

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

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

19. 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)

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

21. AVG. FLUX OF OZONE TO SURFACE VD*[ozone] – Aug. 7-30 2002 NO O 3 ASSIMILATION WITH O3 ASSIMILATION ppb*m/s

22. Incremental analysis vs cloud Case study. May 02 2004 20Z

23. Prediction (assimilation) ON OFFON

24. Impact of assimilating ozone on other species

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

28. 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

29. Vertically resolved measurements

33. Forecast error variance ORACLE TOVS ORACLE + TOVS

34. 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 Environment Canada Meteorological Service of Canada Environnement Canada Service Météorologique du Canada

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

36. Development of General Circulation and Chemistry Model (GCCM) 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 H 2 O, CO 2, O 3, N 2 O, CH 4, CFC-11, CFC-12, CFC-113, CFC-114 sulfate, sea salt, and dust aerosols. non-orographic gravity wave drag (Hines) Dynamics and physics

37. 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 Chemistry

38. 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 CMCNCEPUK MetOfficeECMWF

39. 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) Meteorological 4D Var (operational since 03/05)

40. 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

41. 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

42. 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

43.  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 xxx interpolation weights C i (x) are the same for all advected species e.g. cubic Lagrange interpolation Computational Issues D M A

44. Data assimilation issues 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 Cross-error covariance models e.g. Temperature-Ozone

45. 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.

46. 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