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

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_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

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

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

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

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)

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 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)

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)

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)

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)

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)

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

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)

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)

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 Temperature Analysis: DT = Taero - Tcontrol (from A da Silva and Dirceu Herdies)

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)

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)

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)

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