Chemical Data Assimilationat the

Meteorological Service of Canada

Richard Ménard, Alain RobichaudPaul-Antoine Michelangelli, Pierre Gauthier,

Yan Yang, and Yves Rochon

• 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

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

• 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

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

Meteorological Service of Canada Meteorological Service of Canada

Brewer and ozonesonde sites in Canada

Four additional ozone sondes in southern Canadafor 2004 summer measurement campaignData available on WOUDC and NATChem

AEROCAN NETWORK

Resolute

Saturna IslandBratt’s Lake

WaskesiuThompson

Churchill

Kuujjuarapik

Halifax

KejimkujikSherbrooke

Egbert

Pickle Lake

Ft. McMurray

Kelowna

Chapais

New Sites (2004)

Existing Sites

AEROCAN NETWORK

Resolute

Saturna IslandBratt’s Lake

WaskesiuThompson

Churchill

Kuujjuarapik

Halifax

KejimkujikSherbrooke

Egbert

Pickle Lake

Ft. McMurray

Kelowna

Chapais

New Sites (2004)

Existing Sites

Distribution of TEOM site across Canada.

Distribution of AEROCAN

Error statistics

obs – model (obs loc) =

(true + obs error) - (true + model error) =

obs error – model error

2obs

)0(2 xm

distance (km)

Objective analysis

Observations

Analysis increment

Error statistics

Emissions

Met fields

Chemical model

Ozone objective analysisand assimilation usingCHRONOS

• 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

1247855320Number of sites

103.256.276.9876.357.1Observation error variance

308.3313.7334.1333.4412.1Correlation length scale

297.6275.8286.5278.6212.8Forecast error variance

400.8332363.5354.9269.9Total variance

INDUSTRIAL AGRICULTURALRESIDENTIALCOMMERCIAL FOREST

Observation error variance – CHRONOS v2.5.015 EDT - August 2004

Forecast error variance – CHRONOS V2.5.015 EDT- August 2004

Monitoring of the error statistics

chi-squarechi-square pTfT

12 RHHP

VerificationVerifying 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)

Monitoring of the error statistics in operational mode

pTfT

12 RHHP

using previous year statistics

Analysis error variance. Reduction due to observations

Provide a method for observation network design

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)

Maps of SUM60 (cumul. Sum > 60 ppb)

(Summer 2002)

MODEL

OBSERVATIONS

OBJECTIVE ANALYSIS

AVG. FLUX OF OZONE TO SURFACEVD*[ozone] – Aug. 7-30 2002

NO O3 ASSIMILATION WITH O3 ASSIMILATION

ppb*m/s ppb*m/s

Incremental analysis vs cloudCase study. May 02 2004 20Z

Prediction (assimilation)

ON OFF ON

Impact of assimilating ozone on other species

Impact of assimilating ozone on other species

Impact of assimilating ozone on other species

Ongoing and future work

• Use of new biogenic emissions (AURAMS)

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

Vertically resolved measurements

Forecast error variance

ORACLE

TOVS

ORACLE + TOVS

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 CanadaMeteorological Service of Canada

Environnement CanadaService Météorologique du Canada

Outline

Objectives of the study

Implementation

Issues / Challenges

• development of GCCM• development of coupled meteorology-chemistry data assimilation system

• computational• data assimilation

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 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)

Dynamics and physics

• 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

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

CMC NCEP UK MetOffice ECMWF

• 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)

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

Development of the coupled dynamical-chemical data assimilation system

• 3D Var-CHEMAddition to an abritrary number of chemical tracer

in the operational 3D Var

Can accommodate cross-error covariance either operator form or explicit form

u

suu

u

s

O

q

pT

IFM

I

IN

IE

I

O

q

pT

33 ln

ln

),(

00

0000

000

000

0000

ln

ln

),(

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 extensionUses same solver as in 3D Var

)()(2

1

2

1),( 1

1

xyRxyuuxxPP

PP

uu

xxux HHJ Tff

fuu

fux

fxu

fxx

T

f

f

xHyRxHyxxPxxx 11

2

1

2

1)( Tf

xx

TfJ

faxxux

fa xxPPuu 1

LHyRLHy 1

2

1

2

1)( TfTfJ

tangent linear integration

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

Computational Issues

D

M

A

4

4

4

1 )(

)()( tswith weigh)()(

ikki

ikk

ii

ii

xx

xxxCxCx

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 modelse.g. Temperature-Ozone

T

BO exp3

• 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.

T

T

c

O

O 2

3

3

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