An EC global chemical data assimilation system Yves J. Rochon Modelling and Integration Research Section, ARQI Downsview

Data assimilation and fusion R&D slide January 2012 Outline Objective  Overview of current global 3D-Var for chemical assimilation  Development/migration plans for the next version - with mechanism for its extension to EnKF and En-VAR Overview of data assimilation methodology Current global 3D-Var weather+chemical assimilation system Two recent applications examples Current and near-term chemical DA projects (AQRD and CRD) Operational NWP systems at EC Future plans (over next ~2.5 years)

Data assimilation and fusion R&D slide January 2012 Overview of data assimilation methodology Use of the analysis in data assimilation cycles  Improved model initialization for launching short-term (e.g. 6-hourly or hourly) and medium-term (2-10 days) forecasts on a regular time interval.  Analysis: Solution from the least-squares minimization of the cost function J ( x ) Input to the analysis (assimilation or data fusion) step  x b : Improved short-term model forecast  B : Background (forecast) error covariances  z : Observations (QCed, ideally bias corrected, and possibly thinned)  R : Observation error variances (or covariances)

Data assimilation and fusion R&D slide January D-variational assimilation  Analysis produced from the application of an iterative solver of the least-squares minimization problem using all target observations simultaneously.  Analysis produced for the central time of the observation time window (e.g. 00, 06, 12, and 18 UTC) Differences with OI, EnKF and En-Var OI: Measurement by measurement or region by region assimilation EnKF: (1) Multiple model forecasts to udpate background error covariances (2) OI assimilation for multiple forecasts En-Var: (1) Multiple model forecasts to udpate background error covariances (2) 3D-Var assimilation with single deterministic forecast Overview of data assimilation methodology

Data assimilation and fusion R&D slide January 2012 Current global 3D-Var weather+chemical assimilation system Chemical assimilation capability added to the EC ‘upper air’ 3D-Var NWP assimilation package (~240 modified and 60 new modules) Takes advantage of existing infrastructures  also provides greater commonality of working environment with existing EC ‘upper air’ weather prediction system at the expense of portability. Current version based on 3D-Var v (FORTRAN 77 and 90)  but NWP 3D-Var reg. assim. capability introduced starting at ~v  Assimilation modes: weather, chemical, or weather+chemical Hands-on use for weather-only assimilation unaffected. Generalized to handle user-specified species/variables and obs. (other than particularities of observation models) Extended for assimilation of AIRS and IASI ozone BT channels.

Data assimilation and fusion R&D slide January 2012 Weather and chemistry obs simulation capability for OSSEs. R&D only system (3D-Var-Chem)  not integrated to the ARMA supported 3D-Var development stream  must play catch-up to the supported NWP system  Setup limitations  Assumes Gaussian error distributions  Incremental analysis (i.e. Analysis – Forecast) grid of 240x120  Background error correlations currently represented in spectral space at T108 (~170 km resolution; higher resolution possible).  Other background error covariance specifications: ▪Monthly static background error covariances ▪Static error variances a function of latitude and vertical. ▪Homogeneous and isotropic background error correlations.  Currently not compatible with GEM4 staggered grid.  Not tested/migrated to IBM p7 computer  Ozone/constituent obs bias correction issue not yet addressed.

Data assimilation and fusion R&D slide January 2012 Two recent application examples : Assimilation of SBUV/2, MLS and GOME-2 ozone using GEM-LINOZ. CSA funded GRIP project on ozone global strato. ozone modelling, assimilation, and radiative coupling project. Period covered: Summer 2008 and Winter 2009 Verification sources: above plus ozonesondes (from WOUDC) OSIRIS, MIPAS, ACE-FTS, MAESTRO, Brewers/Dobsons, OMI, Ozone column amounts from blended sources (WOUDC) : ESA funded Observation System Simulation Experiments (OSSEs) for the proposed ESA PREMIER mission (T, H2O, O3) Period covered: Summer 2005 and Winter 2006 Other previous work included use of CO, CH4, N2O, HNO3, NO2, CO observations and the GEM-BACH, GEM-AQ and CMAM models.

Data assimilation and fusion R&D slide January 2012 Ozone background and obs errors from the Desroziers method. Ozone background error correlations (homogeneous, isotropic, separable, non-negative) – Source: 6 hour time differences (48-24 hr forecast differences also considered – but would have to be re-done) – Fitted to the Third Order AutoRegressive (TOAR) correlation model – smoothing of half-width at half-max (HWHM) along the vertical Vertical correlation HWHM before smoothing (approx. converted to km via ideal gas law for 220 Kelvin) Horizontal correlation HWHM hr diff Model vertical resolution HWHM ~ 2.33L for TOAR

Data assimilation and fusion R&D slide January 2012 Sample ozone observation distribution Tangent point orbit tracks for a 6 hour period (centered about 0 UTC) on 25 July Total column amounts Thinning: 1 degree separation Day only cloud free points km along track ~ 2.5 km in the vertical (NRT: 0.2 to 68 hPa) 20 usable partial column layers with ~5 ‘no-impact’ tropo. layers ~3.2 km layers Day only

Data assimilation and fusion R&D slide January 2012 SH Jan-Feb, 2009 EQ NH Ozone (%) Cases: No assimilation MLS+SBUV/2 MLS “Obs minus forecast” Assessment of ozone forecasts: GRIP project

Data assimilation and fusion R&D slide January 2012 Assessment of ozone analyses/forecasts Total column ozone (July, 2008) –Relative to blended sources Without ozone assimilation With SBUV/2 and MLS

Data assimilation and fusion R&D slide January 2012 PREMIER observations Tangent point orbit tracks for a sample 6 hour period (centered about 0 UTC) Across-tracks 1, 4, 7, 10

Data assimilation and fusion R&D slide January 2012 July time mean 6hr forecast H 2 O errors (100x  lnq)

Data assimilation and fusion R&D slide January 2012 July time mean 6hr forecast ozone errors (%)

Data assimilation and fusion R&D slide January 2012 Current and near-term chemical DA projects (AQRD and CRD) Common features Numerical model: GEM+chemistry such as GEM-MACH Stats. weighted least-squares solutions: Gaussian error distributions Projects Global 3D-Var ozone assimilation (target: operational application)  Global ozone assimilation in support of regional forecasting – Improving UV index forecasting and also GEM-MACH15 ozone by providing upper (and lateral) boundary conditions  Assimilation of IASI and AIRS ozone channels (ARMA & ARQI) Regional surface Objective Analyses for O 3, PM 2.5, and NO 2 (OI)  Data fusion system to be presented towards operations in Spring 2012  Extension to complete assimilation cycles. Initiating 3D-Var global and regional assimilation of AOD EC Carbon Assimilation System (EC-CAS; CRD, UofT, UofW) Assimilation of TES and MLS ozone: impact on surface (with UofT)

Data assimilation and fusion R&D slide January 2012 Current systems GDPS: Global Deterministic weather Prediction System  4D-Var v (from 3D/4D-Var assimilation code) GEPS: Global Ensemble weather Prediction System (EnKF) RDPS: Regional Deterministic weather Prediction System  Regional assimilation using 3D-Var v  Driver: Lateral and initial boundary conditions from a global assimilation and forecast system with rotated 55km grid (3D-Var) Possible/likely eventual future directions for operational NWP at EC GDPS & GEPS ––––> GEPS or hybrid (En-Var) RDPS ––––> RDPS (4D-Var reg. assim. with 3D-Var global driver; Spring2012) ––––> REPS or hybrid (En-Var) Related initiative: Unification project to unify the codes of the MRD/CMC 3D-Var/EnKF/En-Var assimilation systems. Operational NWP systems at EC

Data assimilation and fusion R&D slide January 2012 Future plans (over next ~2.5 years) As part of the 3D-Var global ozone assimilation project: 3D-Var-Chem migration  Part of the intent: resulting code to automatically benefit from future improvements to the NWP assimilation code. ▪drastically reduce effort to catch-up (on the chem-DA side)  A related consequence: addition of regional chemical assim. capability  Migrate to the 3D-Var/EnKF/En-Var of the unification development project (in consultation with ARMA) ▪Advantages –faciliate transition towards EnKF capability (+ En-Var, 3D-Var) –environment commonality with the future supported operational NWP system. ▪Disadvantage: possibly delayed regional chemical assim. capability. ▪2012: Migrate 3D-Var development trunk to start

Data assimilation and fusion R&D slide January 2012  Testing/validation of new version for 3D-Var global ozone assim. (and eventually also EnKF and/or En-Var)  Ozone assimilation issues  Finalizing choices for sources of observations (and setting NRT data acquisition – and archiving)  Ozone obs. bias correction study  Specification of ozone background error covariances.  Experimentation of ozone only and ozone+weather assimilations in the context of a global driver for the RAQDPS (GEM-MACH15 regional forecasting)  Developments (with CMDA if funding permits) towards a setup for its operational-like application as a global ozone+weather driver for GEM-MACH15 (and improved UV index forecasting). System to be usable for other future chemical (and weather) DA research projects (other species and purposes). Extension to other operational uses TBD.

Sources of chemical observations for assimilation at EC Yves J. Rochon Contributions from Alain Robichaud, David Anselmo, Ray Nassar, Saroja Polavarapu, Jean de Grandpré, Irena Paunova, Chris McLinden, Robert Vet, Randall Martin

Data assimilation and fusion R&D slide January 2012 Outline Introduction Non-satellite data sources for current projects GHG: satellite of interest AOD: satellites of interest Ozone: satellites of interest Others constituents (CO, N2O, (CH4 for AQRD), SO2, NO2, HCHO, H2O, BrO) TBD: see –See Monday’s presentations by R.Martin and C.McLinden/ R.Vet for some satellite sources

Data assimilation and fusion R&D slide January 2012 Introduction Data uses in chemical data assimilation  Assimilation  Depended and independent verification of analyses, forecasts and other obs (including bias identifiation) Datasets covering free troposphere and stratosphere in addition surface/near-surface obs. – Impact on surface AQ related forecasts (including UV index) – Intrusions from the stratosphere – Long-range pollutant transport – Potential contributions to NWP: impact on upper tropo and strato radiation, temperature, winds? Data acquisition and archiving for NRT or long-term applications – For possible discussion with CMC

Data assimilation and fusion R&D slide January 2012 Non-satellite data sources for current projects Regional surface assimilation: AIRNow and NAPS data as previously described plus surface level ozonesonde data for indep. verification Most likely verification sources for other assimilation projects (ozone and AOD)  ozonesondes (ozonesonde based climatology?) (WOUDC),  Brewer/Dobson total column and UV index measurements,  ozone column amounts from blended sources (WOUDC)  Aeronet/Aerocan (also assimilated) and NAPS  MODIS & MISR AOD and CERES radiat. flux climatologies  AIRNow & NAPS if not also NatChem (ozone and PM2.5) Assimilation impact of ozonesonde and Brewer/Dobson ozone to be examined.

Data assimilation and fusion R&D slide January 2012 GHG data sources for assimilation and verification (EC-CAS)  NOAA oversees global network of surface stations and hosts data centre ( including CarbonTracker and GLOBALVIEW productshttp://  Other providers: EC, CSIRO, JMA, Universities, Euro organizations - Surface Flasks - currently ~70 active sites globally - Continuous surface in situ instruments ~ 25 active sites globally - CARIBIC (Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrumented Container) - CONTRAIL (Comprehensive Observation Network for Trace Gases by Airliner) - HIPPO (Hiaper Pole-to-Pole Observations) - Aircraft obs profiles / single sites - Other: TCCON – Total Column Carbon Observing Network: Network of FTIRs focusing on CO 2 and CH 4

Data assimilation and fusion R&D slide January 2012 GHG: Satellite instruments InstrumentData avail TANSO-FTS (GOSAT) IASI2007- AIRS2002- HIRS1978- SCIAMACHY2003- ACE-FTS2004- TES2006- All are nadir except ACE-FTS which is occultation (limb) InstrumentData avail IASI/Metop-B,C OCO TanSat2015- CarbonSat2018- PHEMOS-FTS2018- CO2 Lidar ASCENDS Present Future IRLS (PREMIER) ?

Data assimilation and fusion R&D slide January 2012 AOD assimilation: Satellite instruments Present Future sensors of possible interest (?) Assimilation: MODIS (Terra - daily; 500nm) GOES (NRT) Later assimilation: MODIS (Aqua), AERONET Verification: AERONET Others for consideration (?) MISR, CALIOP, MISR, PARASOL, VIIRS, GOME- 2, OMI, SCIAMACHY EarthCare, GCOM-1,2,3 (sat) ACE (NASA – Lidar+radar) TROPOMI, SENTINEL-4,5, PHEMOS, GEO-CAPE

Data assimilation and fusion R&D slide January 2012 Ozone assimilation: Satellite instruments Present Future sensors of possible interest (?) Assimilation: (1) GOME-2, SBUV/2 IASI, AIRS OMPS & CrIS (NPP) (2) TES, MLS, OMI (Aura) Verification: OSIRIS, MLS ACE-FTS, MAESTRO Others for verification (?) MIPAS, SCIAMACHI GOME-2, SBUV/2 IASI, TROPOMI, SENTINEL-4,5, IRLS & MWLS PHEMOS/PCW, GEO-CAPE CLARREO, GACM (2020), Next generation ACE-FTS

Data assimilation and fusion R&D slide January 2012 Thank you