NASA/GMAO Contributions to GSI

OUTLINE• GSI Infrastructure• New Instruments• Methodologies• Closing Remarks

Questions/Comments: Ricardo.Todling@nasa.gov

Ricardo TodlingGlobal Modeling and Assimilation OfficeGSI Workshop, DTC/NCAR, 28 June 2011

Contributions from: A. da Silva, A. El Akkraoui, W. Gu, J.Guo, D. Herdies, W. McCarty, D. Merkova, M. Sienkiewicz, A. Tangborn, Y. Tremolet, K. Wargan, P. Xu, & B. Zhang

Ongoing Development• GSI Infrastructure:

– Revisit ChemGuess_Bundle– Introduce MetGuess_Bundle– Generalize Jacobian– Introduce interfaces to GSI-Jacobian/CRTM for Aerosols and Clouds– Revisit interface to TLM and ADM for 4D-Var

• New Observation Types and State-Variables:– MOPITT– SSMI– CrIS and ATMS– OMPS– Doppler Wind Lidar

• Methodologies:– Use of cloud-cleared moisture background to assimilate IR instruments– GMAO-GOCART Aerosols influence on radiance assimilation– Add Bi-CG minimization and corresponding Lanczos pre-conditioning– Estimation of tendency-based Q (system error covariance)

GSI Infrastructure

Revisit ChemGuess_BundleIntroduce MetGuess_BundleGeneralize JacobianIntroduce interfaces to GSI-Jacobian/CRTM for

Aerosols and CloudsRevisit interface to TLM and ADM for 4D-Var

• GSI_Chem_Bundle renamed to ChemGuess_Bundle• Introduce MetGuess_Bundle as a means to ingest

meteorological guesses into GSI: – presently working for clouds-related fields– being extended to work with basic fields (u, v ,tv, etc)

• anavinfo file:– Updates made to chem_guess table– Add met_guess table to control contents for

MetGuess_Bundle• Future work includes:

– Instantiation of ChemGuess and MetGuess Bundles

GSI Infrastructure: ChemGuess and MetGuess Bundles

GSI Infrastructure

Interfaces to Aerosols & Clouds

• Adding aerosols and clouds to Guess Bundle allows for these to be passed to CRTM; parameter in anavinfo tables determines what’s to feed to CRTM and how.

• Add flexible interface to allow for user-specific controls to handle aerosols and clouds (see Tutorial)

Interface to AD/TL models• Revisit to support ESMF• Available interfaces exist

now for at least three global AD/TL models:– GEOS-5 FV-dynamics– GEOS-5 FV-cubed-dynamics– NCEP Perturbation model

New Instruments

MOPITT Carbon MonoxideSSMISCrIS and ATMSOMPS O3 (OSSE-like)Doppler Wind Lidar (OSSE-like)

New Instruments: MOPITT CO

Changes entail:- mild change to obsmod- add usual suspects when handling new observing types, e.g.:

- readCO - setupCO - intCO - stpCO- Estimate and set B(co).

• Four profiles of MOPITT CO are randomly placed on the globe and assimilated using GSI. Preliminary results are consistent with shape of averaging kernel.• Cycling experiments are on the way.

MOPITT - Measurements Of Pollution In The Troposphere

(from Andrew Tangborn)

New Instruments: OMPS O3 (OSSE)OMPS – Ozone Mapping and Profiler Suite

• High Fidelity Measurements:- Total column (like TOMS)- Vertical profiles (like SBUV)

• OSSE Setting:- Generate truth: MLS-O3 & OMI/TC- Simulate Radiances – Forward RT- Apply Instrument Models - Retrieve Profiles- Assimilate Retrievals (GEOS-5 DAS)- 1 degree resolution

Results show:- Data are ingested into GSI at all levels - QC control works (but rate of rejection can be adjusted)- Analysis works effectively- Penalties are in good range- Time series show fast convergences - OMA and OMF are all very small and OMA are smaller than OMF

(from Philippe Xu)

New Instruments: OMPS O3 (OSSE)OMPS – Ozone Mapping and Profiler Suite

(from Philippe Xu)

a) 5 hPa b) 100 hPa

Analysis error (%) of retrieved ozone assimilation from TRUTH

- At 5 hPa errors are small in most of region; orbit tracks of OMPS analysis are noticeable.

- At 100 hPa errors are large where retrievals are most difficult: Tropics as the ozone value are very small (<0.1ppmv).

New Instruments: OMPS O3 (OSSE)OMPS – Ozone Mapping and Profiler Suite

(from Philippe Xu)

Retrieved vs MLS TRUTH (%) OMPS sampled vs MLS TRUTH (%)

Monthly Zonal Mean analysis errors

• The results show that OMPS data agree well with MLS in the stratosphere and in most of the troposphere.

• In the tropical UT and LS there is large discrepancy (%) between MLS and OMPS, where the ozone mixing ratio are very small (<0.1 ppmv); needs more work.

New Instruments: Doppler Wind Lidar (OSSE)

(from Will McCarty)

• Measurements ESA/Aeolus:- Rayleigh backscatter (clear sky)- Mie backscatter (clouds/aerosols)

• OSSE Setting:- ECMWF Nature Run (NR)- Errico’s simulated observations- Simulated obs:

- KNMI Lidar Perf Anal Simul (LIPAS)

- LOS: GEOS-5 replay with GOCART forced with NR

- Experiments assimilate- DWL (Rayleigh and Mie)- Rayleigh only- Mie only

- 1/2 degree resolutionResults show:-Diminished impact toward surface

- less observations- large contamination

- Nearly neutral in NH/SH- winds larger determined by balance

New Instruments: Doppler Wind Lidar (OSSE)

(from Will McCarty)

Increase in RMS by adding DWL

Reduction in RMS by adding DWL

Changes entail:-mild change to obsmod-And typical - read_lidar - setupdw - intdw - stpdw

New Instruments: Doppler Wind Lidar (OSSE)

(from Will McCarty)

Results indicate:- Upper-troposphere

- Mie impact neutral away from tropics; mildly positive in tropics - Rayleigh impact positive throughout; dominates in tropics

- Lower-troposphere- Mie and Rayleigh give redundant impact: either provides all information

- All-in-all OSSE tends to over-state impact of observing system- Obs error need to be better adjusted (esp. for Mie)

Methodologies

Use of cloud-cleared moisture background to assimilate IR instruments

GOCART Aerosols influence on radianceBi-CG minimization and Lanczos pre-conditioningEstimation of tendency-based Q (model error)

Methodologies: Cloud-cleared q variable for IR

Changes entail:- add cloud frac to guess- cloud frac to crtm_interface

Picture displays mean OmF for AIRS calculated using full q variable (red)and cloud-clear q variable; some reduction in bias is observed when new is used – results are still preliminary.

(water-vapor)

(from Dagmar Merkova & A da Silva)

Methodologies: Aerosol Radiance Contamination

• CRTM allows for the inclusion of (GOCART) aerosols• The GEOS-5 GOCART aerosol species have been

introduced as state variables in GSI– No aerosol analysis for now– Aerosol effects included in the observation operators for IR

instruments: AIRS, HIRS, IASI, etc

• Control Experiment:– Fully interactive GEOS-5 GOCART aerosols– Standard global GSI– ARCTAS period: Summer 2008– Resolution: ½ degree

• Aerosol Experiment:– Fully interactive GEOS-5 GOCART aerosols– GSI observation operators:

• 15 GOCART species– Concentrations– Effective radius

• CRTM internal optical parameters

(from A da Silva and Dirceu Herdies)

MISR

GEOS-5

AOD Validation

GEOS-5 overestimates dust

Methodologies: Aerosol Radiance Contamination

Dust Distribution for July 2008 event off West Coast of Africa

(from A da Silva and Dirceu Herdies)

Methodologies: Aerosol Radiance Contamination

(from A da Silva and Dirceu Herdies)

Temperature Analysis: DT = Taero - Tcontrol

Methodologies: Aerosol Radiance Contamination

(from A da Silva and Dirceu Herdies)

Control

Aero effects

About 3% more AIRS observations are accepted

Neutral impact to residual error statistics

Observation Count Residual Statistics

Methodologies: Lanczos Bi-Conjugate GradientObjective: aid general formulation of WC-4dVar

Changes entail:- add Bi-CG driver- mild glbsoi update- mild gsimod update- mild gsi_4dvar update

Results highlight two aspects of CG:- Orthogonalization of gradients consi-

derably improves convergence- Lanczos BiCG same as Lanczos CG, but

former applies for non-symmetric case

BiCG

BiCG w/ orthoDouble CG w/ ortho

Double CG

CG w/ ortho

Lanczos BiCGLanczos CG

Remarks:- CG solves symmetric case- Double CG solves non-symmetric case- Double CG uses B-precond- Lanczos CG uses sqrt(B)-precond- BiCG solves non-symmetric case- Lanczos BiCG uses B-precond

(from Amal El Akkraoui)

Methodologies: Estimation of Q (model error)

B-stQ-st

Q-vp

Q-t

B-vp

B-t

Plots show horizontal scales for B and prototype Q for stream function, velocity potential, and temperature at 45N obtained over a four-month sample of forecast full fields and tendencies, respectively.

Figure above shows normalized impact of observations within analysis window for SC and no-B WC.

(from Banglin Zhang & Wei Gu)

Closing Remarks

• Completing comparison of SC and WC-4dVar in prototype GEOS-5 4dVar system.

• Making progress in bringing GEOS-5 Cubed-Sphere TLM and ADM to maturity.

• Started working on hybrid ensemble components for GEOS-5 3d- and 4d-Var.

Collaboration with NCEP is ongoing and fundamental for the success of these implementation.

New Instruments: OMPS O3 (OSSE)OMPS – Ozone Mapping and Profiler Suite

•Generate TRUTH- GEOS-5.2.0 (MERRA tag)- 1x1.25°L72 resolution- Conventional data & satellite

radiances impact meteorology- Simple chemistry: O3 P&L in GCM- MLS O3 profiles (215-0.1hPa) and

OMI TC assimilated- Hourly analysis output

•Simulate Radiances- Interpolate TRUTH to OMPS/LP

observation points to 1-km profile- RT with pseudo-spherical

atmosphere, multiple scattering, refraction, tangent shift, etc.

- Random surface reflectance, cloud-top height simulated and aerosol selected from SAGE-II database

•Retrieve Profiles- Rodgers’ Optimal Estimation - Climatology as a-priori- First retrieve cloud-top

height, tangent height, surface reflectance and aerosol distributions

- Ozone profile retrievals

•Assimilate Retrievals- OMPS/LP data added

to GSI in GEOS-5.6.1- The o3lev observer is

used, same as for MLS- QC flag for retrievals

• Apply Inst. Models

- Instrument Simulator Model

- Deconvolution Model

- Consolidation Model

Validation