1. NASA/GMAO Contributions to GSI
Ricardo Todling
Global Modeling and Assimilation Office
GSI Workshop, DTC/NCAR, 28 June 2011
OUTLINE
GSI Infrastructure
New Instruments
Methodologies
Closing Remarks
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
Questions/Comments:

2. 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)

3. 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

4. GSI Infrastructure: ChemGuess and MetGuess Bundles
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

5. GSI Infrastructure Interface to AD/TL models
Interfaces to Aerosols & Clouds
Interface to AD/TL models
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)
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

6. New Instruments MOPITT Carbon Monoxide SSMIS CrIS and ATMS
OMPS O3 (OSSE-like)
Doppler Wind Lidar (OSSE-like)
MOPITT - Measurements Of Pollution In The Troposphere (EOS Terra)
OMPS – Ozone Mapping and Profiler Suite (NPP-NPOESS)
MLS – Microwave Limb Sounder (EOS Aura)
CrIS – Cross-track Infrared Sounder
ATMS – Advanced Technology Microwave Sounder
SSMIS - Special Sensor Microwave Imager Sounder

7. New Instruments: MOPITT CO
MOPITT - Measurements Of Pollution In The Troposphere
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.
(from Andrew Tangborn)

8. 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)

9. New Instruments: OMPS O3 (OSSE)
OMPS – Ozone Mapping and Profiler Suite
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).
(from Philippe Xu)

10. New Instruments: OMPS O3 (OSSE)
OMPS – Ozone Mapping and Profiler Suite
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.
(from Philippe Xu)

11. New Instruments: Doppler Wind Lidar (OSSE)
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 resolution
Results show:
Diminished impact toward surface
less observations
large contamination
Nearly neutral in NH/SH
winds larger determined by balance
(from Will McCarty)

12. New Instruments: Doppler Wind Lidar (OSSE)
Changes entail:
mild change to obsmod
And typical
- read_lidar
- setupdw
- intdw
- stpdw
Reduction in RMS
by adding DWL
Increase in RMS
by adding DWL
(from Will McCarty)

13. New Instruments: Doppler Wind Lidar (OSSE)
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)
(from Will McCarty)

14. Methodologies
Use of cloud-cleared moisture background to assimilate IR instruments
GOCART Aerosols influence on radiance
Bi-CG minimization and Lanczos pre-conditioning
Estimation of tendency-based Q (model error)
MOPITT - Measurements Of Pollution In The Troposphere (EOS Terra)
OMPS – Ozone Mapping and Profiler Suite (NPP-NPOESS)
MLS – Microwave Limb Sounder (EOS Aura)
CrIS – Cross-track Infrared Sounder
ATMS – Advanced Technology Microwave Sounder
SSMIS - Special Sensor Microwave Imager Sounder

15. Methodologies: Cloud-cleared q variable for IR
Changes entail:
add cloud frac to guess
cloud frac to crtm_interface
(water-vapor)
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.
(from Dagmar Merkova & A da Silva)

16. Methodologies: Aerosol Radiance Contamination
AOD Validation
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:
GSI observation operators:
15 GOCART species
Concentrations
Effective radius
CRTM internal optical parameters
MISR
GEOS-5
GEOS-5 overestimates dust
(from A da Silva and Dirceu Herdies)

17. Methodologies: Aerosol Radiance Contamination
Dust Distribution for July 2008 event off West Coast of Africa
(from A da Silva and Dirceu Herdies)

18. Methodologies: Aerosol Radiance Contamination
Temperature Analysis: DT = Taero - Tcontrol
(from A da Silva and Dirceu Herdies)

19. Methodologies: Aerosol Radiance Contamination
Observation Count
Residual Statistics
Control
Aero effects
Neutral impact to residual error statistics
About 3% more AIRS observations are accepted
(from A da Silva and Dirceu Herdies)

20. Methodologies: Lanczos Bi-Conjugate Gradient
Objective: aid general formulation of WC-4dVar
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
BiCG
BiCG w/ ortho
Double CG w/ ortho
Double CG
Changes entail:
- add Bi-CG driver
mild glbsoi update
mild gsimod update
mild gsi_4dvar update
CG w/ ortho
Lanczos BiCG
Lanczos CG
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
(from Amal El Akkraoui)

21. Methodologies: Estimation of Q (model error)
Q-st
B-st
B-vp
Q-vp
Figure above shows normalized impact of
observations within analysis window for
SC and no-B WC.
Q-t
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.
(from Banglin Zhang & Wei Gu)

22. 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.

24. New Instruments: OMPS O3 (OSSE)
OMPS – Ozone Mapping and Profiler Suite
Generate TRUTH
GEOS (MERRA tag)
1x1.25°L72 resolution
Conventional data & satellite radiances impact meteorology
Simple chemistry: O3 P&L in GCM
MLS O3 profiles ( hPa) 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