An EC global chemical data assimilation system Yves J. Rochon Modelling and Integration Research...

An EC global chemical data assimilation system

Yves J. Rochon Modelling and Integration Research Section, ARQI

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Data assimilation and fusion R&D slide 2 16-17 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)


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 xb: 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)

J H Hb bx x x B x x z x R z x 1

2

1

21 1T T

( ) ( )


• 3D-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


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 v10.2.2+ (FORTRAN 77 and 90) but NWP 3D-Var reg. assim. capability introduced starting at ~v10.3.2

• 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.


• 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.


Two recent application examples

• 2008-2011: 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)

• 2010-2011: 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.


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

– 1-1-1 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

48-24

6 hr diff.

6 hr diff.

48-24

Model vertical resolution

HWHM ~ 2.33L for TOAR


Sample ozone observation distributionTangent point orbit tracks for a 6 hour period

(centered about 0 UTC) on 25 July 2008

1748584

5502

Total column amounts

Thinning: 1 degree separation

Day only cloud free points

165-300 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


SH

Jan-Feb, 2009

EQ

NH

Ozone (%)

Cases:

No assimilation

MLS+SBUV/2

MLS

“Obs minus forecast”

Assessment of ozone forecasts: GRIP project


Assessment of ozone analyses/forecasts

• Total column ozone (July, 2008)– Relative to blended sources

Without ozone assimilation With SBUV/2 and MLS


PREMIER observationsTangent point orbit tracks for a sample 6 hour period

(centered about 0 UTC)

57161429

1748584

Across-tracks 1, 4, 7, 10


July time mean 6hr forecast H2O errors (100xlnq)


July time mean 6hr forecast ozone errors (%)


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 O3, PM2.5, and NO2 (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)


Current systems

• GDPS: Global Deterministic weather Prediction System 4D-Var v11.2.0 (from 3D/4D-Var assimilation code)

• GEPS: Global Ensemble weather Prediction System (EnKF)

• RDPS: Regional Deterministic weather Prediction System Regional assimilation using 3D-Var v11.2.0 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


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


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 observationsfor 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


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


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


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 NAPSMODIS & 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.


• GHG data sources for assimilation and verification (EC-CAS) NOAA oversees global network of surface stations and hosts data centre

(http://www.esrl.noaa.gov/gmd/ccgg/) including CarbonTracker and GLOBALVIEW products

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 CO2 and CH4

www.tccon.caltech.edu


GHG: Satellite instruments

Instrument Data avail

TANSO-FTS (GOSAT)

2009-

IASI 2007-

AIRS 2002-

HIRS 1978-

SCIAMACHY 2003-

ACE-FTS 2004-

TES 2006-

All are nadir except ACE-FTS which is occultation (limb)

Instrument Data avail

IASI/Metop-B,C 2012- 2016-

OCO-2 2014-

TanSat 2015-

CarbonSat 2018-

PHEMOS-FTS 2018-

CO2 Lidar ASCENDS

2020-

Present Future

IRLS (PREMIER) ?


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


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


Thank you

An EC global chemical data assimilation system Yves J. Rochon Modelling and Integration Research...

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