Presentation is loading. Please wait.

Presentation is loading. Please wait.

NASA/GMAO Contributions to GSI OUTLINE GSI Infrastructure New Instruments Methodologies Closing Remarks Questions/Comments: Ricardo.

Similar presentations


Presentation on theme: "NASA/GMAO Contributions to GSI OUTLINE GSI Infrastructure New Instruments Methodologies Closing Remarks Questions/Comments: Ricardo."— Presentation transcript:

1 NASA/GMAO Contributions to GSI OUTLINE GSI Infrastructure New Instruments Methodologies Closing Remarks Questions/Comments: Ricardo Todling Global Modeling and Assimilation Office GSI 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

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_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

5 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

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

7 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)

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 (from Philippe Xu) a) 5 hPab) 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).

10 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.

11 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 resolution Results show: -Diminished impact toward surface - less observations - large contamination - Nearly neutral in NH/SH - winds larger determined by balance

12 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

13 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)

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 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)

16 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

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 (from A da Silva and Dirceu Herdies) Temperature Analysis:  T = T aero - T contro l

19 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

20 Methodologies: Lanczos Bi-Conjugate Gradient Objective: 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/ ortho Double CG w/ ortho Double CG CG w/ ortho Lanczos BiCG Lanczos 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)

21 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)

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.

23

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: O 3 P&L in GCM -MLS O 3 profiles ( hPa) and OMI TC assimilated -Hourly analysis output Generate TRUTH -GEOS (MERRA tag) -1x1.25°L72 resolution -Conventional data & satellite radiances impact meteorology -Simple chemistry: O 3 P&L in GCM -MLS O 3 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 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 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 The o3lev observer is used, same as for MLS -QC flag for retrievals Assimilate Retrievals -OMPS/LP data added to GSI in GEOS The o3lev observer is used, same as for MLS -QC flag for retrievals Apply Inst. Models -Instrument Simulator Model -Deconvolution Model -Consolidation Model Apply Inst. Models -Instrument Simulator Model -Deconvolution Model -Consolidation Model Validation


Download ppt "NASA/GMAO Contributions to GSI OUTLINE GSI Infrastructure New Instruments Methodologies Closing Remarks Questions/Comments: Ricardo."

Similar presentations


Ads by Google