NASA/GMAO Contributions to GSI

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: Ricardo.Todling@nasa.gov

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 Interfaces to Aerosols & Clouds 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 • 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)

6. New Instruments • MOPITT Carbon Monoxide • SSMIS • CrIS and ATMS • OMPS O3 (OSSE-like) • Doppler Wind Lidar (OSSE-like)

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 LidarPerf 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)

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 MISR • 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 GEOS-5 GEOS-5 overestimates dust (from A da Silva and DirceuHerdies)

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

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

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 DirceuHerdies)

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 - LanczosBiCG uses B-precond • Changes entail: • - add Bi-CG driver • mild glbsoi update • mild gsimod update • mild gsi_4dvar update BiCG Double CG BiCGw/ ortho CG w/ ortho Double CG w/ ortho Lanczos CG LanczosBiCG • Results highlight two aspects of CG: • Orthogonalization of gradients consi- • derably improves convergence • LanczosBiCG 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-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 Validation • 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 • Retrieve Profiles • Rodgers’ Optimal Estimation • Climatology as a-priori • First retrieve cloud-top height, tangent height, surface reflectance and aerosol distributions • Ozone profile retrievals • Apply Inst. Models • Instrument Simulator Model • DeconvolutionModel • ConsolidationModel