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Acknowledge: NCAR/ESSL/MMM/DAG, NCAR/RAL/JNT/DATC, AFWA, USWRP, NSF-OPP, NASA, AirDat,

The recent developments of WRFDA (WRF-Var) Hans Huang, NCAR WRFDA: WRF Data Assimilation WRF-Var: WRF Variational data assimilation. Acknowledge: NCAR/ESSL/MMM/DAG, NCAR/RAL/JNT/DATC, AFWA, USWRP, NSF-OPP, NASA, AirDat, KMA, CWB, CAA, BMB, EUMETSAT. Outline. WRFDA overview

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Acknowledge: NCAR/ESSL/MMM/DAG, NCAR/RAL/JNT/DATC, AFWA, USWRP, NSF-OPP, NASA, AirDat,

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  1. The recent developments of WRFDA(WRF-Var)Hans Huang, NCARWRFDA: WRF Data AssimilationWRF-Var: WRF Variational data assimilation Acknowledge: NCAR/ESSL/MMM/DAG, NCAR/RAL/JNT/DATC, AFWA, USWRP, NSF-OPP, NASA, AirDat, KMA, CWB, CAA, BMB, EUMETSAT

  2. Outline WRFDA overview A few new capabilities Incremental formulation and outer-loop Forecast error sensitivity to observations Future plan and Summary

  3. WRF-Var (WRFDA) Data Assimilation Overview • Goal: Community WRF DA system for • regional/global, • research/operations, and • deterministic/probabilistic applications. • Techniques: • 3D-Var • 4D-Var (regional) • Ensemble DA, • Hybrid Variational/Ensemble DA. • Model: WRF (ARW, NMM, Global) • Support: • NCAR/ESSL/MMM/DAG • NCAR/RAL/JNT/DATC • Observations: Conv.+Sat.+Radar

  4. The WRF-Var Program • NCAR staff: 15FTE • Non-NCAR collaborators: ~10FTE. • Community users: ~30 (more in 6000 general WRF downloads?).

  5. KMA Pre-operational Verification: (with/without radar) Threat Score Bias WRF-Var Observations • In-Situ: • Surface (SYNOP, METAR, SHIP, BUOY). • Upper air (TEMP, PIBAL, AIREP, ACARS). • Remotely sensed retrievals: • Atmospheric Motion Vectors (geo/polar). • Ground-based GPS Total Precipitable Water. • SSM/I oceanic surface wind speed and TPW. • Scatterometer oceanic surface winds. • Wind Profiler. • Radar radial velocities and reflectivities. • Satellite temperature/humidities. • GPS refractivity (e.g. COSMIC). • Radiative Transfer: • RTTOVS (EUMETSAT). • CRTM (JCSDA).

  6. WRF-Var Radiance Assimilation StatusLiu and Auligne • BUFR 1b radiance ingest. • RTM interface: RTTOV or CRTM • NESDIS microwave surface emissivity model • Range of monitoring diagnostics. • Quality Control for HIRS, AMSU, AIRS, SSMI/S. • Bias Correction (Adaptive, Variational in 2008) • Variational observation error tuning • Parallel: MPI • Flexible design to easily add new satellite sensors NOAA (HIRS, AMSU) Aqua (AMSU, AIRS) DMSP(SSMI/S)

  7. The first WRF-Var tutorial July 21-22, 2008 9 hours lectures and 4 hours hands on 53+ participants, US and international WRF-Var tutorial agenda http://www.mmm.ucar.edu/events/tutorial_708/agenda/agenda.php WRF-Var tutorial presentations http://www.mmm.ucar.edu/wrf/users/tutorial/tutorial_presentation.htm WRF-Var online tutorial and user guide http://www.mmm.ucar.edu/wrf/users/docs/user_guide_V3/users_guide_chap6.htm

  8. Outline WRFDA overview A few new capabilities Incremental formulation and outer-loop Forecast error sensitivity to observations Future plan and summary

  9. AIRS assimilation: Experiments • T8 domain • 45km horizontal resolution • 57 vertical levels (up to 10hPa) • Domain-specific background error covariances (ensemble-based) • 48h forecasts at 00UTC and 12UTC • Experiments: • Conventional Data (CONV) • Conventional + AIRS (AIRS)

  10. AIRS assimilation: Impact on forecast 24h forecasts minus conventional observations over a 30-day period U V U V T Q T Q BIAS RMSE CONV AIRS

  11. 3D-Var 4D-Var WRF 4D-Var Summary • 4D-Var included within WRF-Var. • Linear/adjoint models based on WRF-ARW. • Status: • Parallel code, JcDFI, limited physics. • Delivered to AFWA in 2006 and 2007. (2008) • Current focus: PBL/microphysics, optimization. • Advantages of 4D-Var • Flow-dependent response to obs • Better treatment of cloud/precip obs • Forecast model as a constraint • Obs at obs-times => Xin’s 4D-Var talk

  12. WRF-Var and NMM (Pattanayak and Rizvi)Analysis increments

  13. Global WRF-Var (Rizvi and Duda)Analysis increments

  14. Use ensemble information in QC (Yongsheng Chen) Number of rejected observations 2007.08

  15. Outline WRFDA overview A few new capabilities Incremental formulation and outer-loop Forecast error sensitivity to observations Future plan and summary

  16. 3D-Var (4D-Var replace H by HM) The incremental formulation (in the general form, !) The first outer-loop: xg = xb Outer-loop: d (and QC, etc) … nonlinear! Inner-loop: minimization update xg

  17. Investigate impact on observation rejection algorithm due to multiple “outer-loops”. - Led by Rizvi Syed First-guess & Observation • Code Development • Activated analysis “outer-loop”; • Added observation rejection check to outer-loop; • Generated statistics for data utilization/rejection for each outer-loop; • Graphic tools developed to monitor data utilization/rejection. Minimization No i≥ntmax or |Jnew|< eps• |J| Yes Inner Update first-guess Update first-guess & Observation rejection No j ≥ max_ext_its Outer Yes

  18. b) Investigate impact on observation rejection algorithm due to multiple “outer-loops”. Outer-loop 1 Outer-loop 2 Number of rejected sondes Outer-loop 1 Outer-loop 2 Analysis difference between the first and second outerloop at the 10th Eta level • Rejected Observation locations and number The obs. rejection can be monitored either in tabular form & graphical form.

  19. Outline WRFDA overview A few new capabilities Incremental formulation and outer-loop Forecast error sensitivity to observations Future plan and summary

  20. Simply Math: Linear assumption: Analysis: Forecast error: Forecast error sensitivity to initial state: Forecast error sensitivity to observations:

  21. Adjoint sensitivity (Thomas Auligne) Analysis (xa) Observation (y) Forecast (xf) WRF-VAR Data Assimilation WRF-ARW Forecast Model Define Forecast Accuracy Background (xb) Forecast Accuracy (F) Observation Impact <y-H(xb)> (F/ y) Gradient of F (F/ xf) Observation Sensitivity (F/ y) Analysis Sensitivity (F/ xa) Adjoint of WRF-ARW Forecast TL Model (WRF+) Adjoint of WRF-VAR Data Assimilation Derive Forecast Accuracy Background Sensitivity (F/ xb) Obs Error Sensitivity (F/ eob) Bias Correction Sensitivity (F/ k)

  22. Adjoint of WRF-VAR DA: Introduction • Analysis increments x = xa - xb = K.[y-H(xb)] = K.d • Adjoint of analysis F/y = KT.F/xa • Various methods • Ensemble Transform Kalman Filter (ETKF, Bishop et al. 2001) • Dual approach (PSAS, Baker and Daley 2000) • Exact calculation of adjoint of DA (Zhu and Gelaro, 2007) • Leading eigenvectors of Hessian (Fisher and Courtier 1995) • Lanczos algorithm (Fisher 1997, Tremolet 2008)

  23. Adjoint of WRF-VAR DA: Lanczos Algorithm • New minimisation package Solve variational problem with Lanczos algorithm: • Minimize Cost Function • Estimate Analysis Error • EXACT adjoint of analysis gain KT • Link with current Conjugate Gradient Due to theoretical similarities b/w the two approaches, the convergence and solutions are IDENTICAL • Computer issues • Adjoint of analysis is calculated during minimization with no overhead • Extra storage is required during minimization • New orthonormalization of gradients results in faster convergence • Products • Observation Sensitivity & Impact ! • Estimation of condition number of DA system !! • Efficient preconditioner for multiple outer loops & ensemble DA !!! • Estimation of Analysis Error covariance matrix !!!!

  24. 500 hPa 200 hPa Impact (Jb) per observation

  25. Adjoint of WRF-VAR DA: Observation Impact Impact (Jb) per observation type SHIP SSMIS AIREP PILOT GPSRF SOUND GEO AMV PROFILER N15 AMSUA N15 AMSUB N17 AMSUB SONDE`_SFC METOP AMSUA N16 AMSUA METAR N16 AMSUB BUOY SATEM SYNOP

  26. Outline WRFDA overview A few new capabilities Incremental formulation and outer-loop Forecast error sensitivity to observations Future plan and summary

  27. Future Plans General Goals: Unified, multi-technique WRF DA system. Retain flexibility for research, multi-applications. Leverage international WRF community efforts. WRF-Var Development (MMM Division): 4D-Var (additional physics, optimization). Sensitivities tools (adjoint, ensemble, etc.). EnKF within WRF-Var -> WRFDA. Instrument-specific radiance QC, bias correction, etc. Data Assimilation Testbed Center (DATC): Technique intercomparison: 3/4D-Var, EnKF, Hybrid Obs. impact: AIRS, TMI, SSMI/S, METOP. New Regional testbeds: US, India, Arctic, Tropics. Applications: Hurricanes/Typhoons OSEs and OSSEs Reanalysis (Arctic System Reanalysis)

  28. Summary WRFDA overview Unified system: 3D-Var, 4D-Var, ETKF, Hybrid Observations: conventional, satellite, radar Community support A few new capabilities AIRS Optimization of 4D-Var (Xin’s talk) WRF-NMM interface global ARW interface Ensemble QC Incremental formulation and outer-loop Background and guess(es) Handling of nonlinear aspects: QC, M and H Resolution changes between inner- and outer-loops Forecast error sensitivity to observations Sensitivity to initial state: adjoint of forecast model Sensitivity to observations: need adjoint of analysis

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