1 Satellite Data Assimilation for meso-scale models Hans Huang National Center for Atmospheric Research (NCAR is sponsored by the National Science Foundation)

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Presentation transcript:

1 Satellite Data Assimilation for meso-scale models Hans Huang National Center for Atmospheric Research (NCAR is sponsored by the National Science Foundation) Acknowledge: NCAR/NESL/MMM/DAS, NCAR/RAL/JNT/DAT, DTC AFWA, USWRP, NSF-OPP, NASA, AirDat, PSU, KMA, CWB, CAA, BMB, EUMETSAT Slides are collected from: Zhiquan Liu, Thomas Auligne, Xin Zhang, Hui Shao, Chunhua Zhou, Syed Rizvi,Yaodeng Chen, Craig Schwartz, Thomas Nehrkorn, Bill Skamarock, …

2 Outline 1.WRFDA – DA for WRF 2.DART and WRFDA 3.GSI and WRF 4.Future Directions?

3 WRFDA 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)

4 WRFDA Observations  In-Situ: -Surface (SYNOP, METAR, SHIP, BUOY). -Upper air (TEMP, PIBAL, AIREP, ACARS, TAMDAR). Remotely sensed retrievals: -Atmospheric Motion Vectors (geo/polar). -SATEM thickness. -Ground-based GPS Total Precipitable Water/Zenith Total Delay. -SSM/I oceanic surface wind speed and TPW. -Scatterometer oceanic surface winds. -Wind Profiler. -Radar radial velocities and reflectivities. -Satellite temperature/humidity/thickness profiles. -GPS refractivity (e.g. COSMIC).  Radiative Transfer (RTTOV or CRTM): –HIRS from NOAA-16, NOAA-17, NOAA-18, NOAA-19, METOP-2 –AMSU-A from NOAA-15, NOAA-16, NOAA-18, NOAA-19, EOS-Aqua, METOP-2 –AMSU-B from NOAA-15, NOAA-16, NOAA-17 –MHS from NOAA-18, NOAA-19, METOP-2 –AIRS from EOS-Aqua –SSMIS from DMSP-16 Bogus: –TC bogus. –Global bogus.

5 WRFDA Radiance Assimilation ( Liu and Auligne, MMM) BUFR 1b radiance ingest. RTM interface: RTTOV (v9.3) or CRTM (v2.0.2) NESDIS microwave surface emissivity model Range of monitoring diagnostics. Quality Control for HIRS, AMSU, AIRS, SSMI/S. Bias Correction: Adaptive or Variational Variational observation error tuning Parallel: MPI Flexible design to easily add new satellite sensors

6 NCAR/RAL/JNT/DAT: Atlantic Testbed (AFWA T8) Land Use Category 361*325*57L, 15km Model top: 10mb Full cycling exp. for 6 days 15 ~ 20 August 2007 GTS: assimilate NCAR conventional obs Select similar data type used by AFWA GTS+AMSU+MHS (use NCEP BUFR rad.) NOAA-15/16/18, AMSU-A, ch. 5~10 NOAA-15/16/17, AMSU-B, ch. 3~5 NOAA-18, MHS (similar to AMSU-B) Radiance used only over water thinned to 120km +-2h time window Bias Correction (H&K, 2001) 48h forecast twice each day 00Z, 12Z (Liu, MMM and Shao, RAL)

7 48h forecast error vs. sound

8

9 2/22/089

10 2/22/0810 4DVAR vs. 3DVAR 45km resolution (4DVAR is still very slow) model top = 10mb Only assimilate radiance data (AMSU/MHS), 6h time window

11 (Adjoint based) Observation Impact: Conventional Data (Auligne, MMM)

12 Observation Impact: Satellite radiances

13 Outline 1.WRFDA – DA for WRF 2.DART and WRFDA 3.GSI and WRF 4.Future Directions? Radiance Data Assimilation with DART Zhiquan Liu, Craig Schwartz, Xiang-Yu Huang (NCAR/MMM) Yongsheng Chen (York University)

14 Practical Implementation Make use of observation operators built in the WRFDA-3DVAR. –Obs. prior is calculated/QCed/output from WRFDA-3DVAR –For both conventional observations and radiances Convert 3DVAR output files into the modified DART obs_seq files. Modify DART to directly use obs prior calculated from 3DVAR –DART built-in observation operators are only applied after analysis (step for diagnosing obs. posterior) For radiances, also output Jacobian from CRTM in addition to obs prior. –For vertical localization

15 Vertical Localization (K/K) Take the height of peak levels of J acobian as vertical coordinate Use DART built-in vertical locali zation AMSU-A Jacobian w.r.t. T (K/K)

16 Bias Correction and QC Bias correction coefficients from the end of 3DVAR experiment. Use Ensemble Mean as reference for BC and QC.

17 Typhoon Morakot Red: Typhoon Blue: Tropical storm or depression Numbers refer to date at 0000 UTC: (0806…06 Aug 2009) Produced very heavy precip. over Tai wan at landfall.

18

19

20

21 Outline 1.WRFDA – DA for WRF 2.DART and WRFDA 3.GSI and WRF 4.Future Directions?

22 DTC (GSI) tasks Provide current operational GSI capability to the research community (O2R); Provide a framework for distributed development of new capabilities & advances in data assimilation. Provide a pathway for data assimilation research to operations process. (R2O). Provide rational basis to operational centers and research community for enhancement of data assimilation technique and systems and, eventually, numerical weather forecast systems.

23 DTC GSI T&E: end-to-end testing system

24 DTC GSI T&E – Radiance Assimilation (Chunhua Zhou and Hui Shao) GSI candidate code (Q1FY11) v1.2 coupled with WRF-ARW v August 2007 (12 UTC) – 22 August 2007 (12 UTC) GDAS PrepBUFR and AMSU_A data 57 vertical levels, 10 mb model top 15 km horizontal resolution Global Background Errors Full 6-hr cycling AFWA T8 domain Two Experiments: AFWA T8 Domain AMSUA: assimilating PrepBUFR + AMSU_A, updated air-mass satbias from previous cycle, all channels included CONV: assimilating PrepBUFR data only

25 Verification against ECMWF (T8,+48h )

26 Bias Correction OMB without BC OMB with BC OMA

27 Outline 1.WRFDA – DA for WRF 2.DART and WRFDA 3.GSI and WRF 4.Future Directions?

28 Use one set of BC coefs for 00Z/12Z (oscillation still exists after BC) Use separated BC coefs for 00Z/12Z (Oscillation is removed after BC) Bias with diurnal cycle. Morning (12Z): -0.60K Evening (00Z): -0.15K (Related to Descending/Ascending nodes) Consider diurnal cycle or descending/ascending orbit issue with VarBC for regional applications

29 CV5 CV6 Increments from single T observatio n at 5th level, 15N Still need to work on BE (Rizvi, Krysta, Chen, Huang) CV3

30 Displacement DA Approach Conceptual view of using displacements to characterize errors Partition: background error displacements of coherent features additive (residual) error =>+

31 Towards Cloudy Radiance Assimilation Simulated mismatch in resolution: Perfect observations (high resolution) Perfect Background (lower resolution) New interpolation scheme: 1. Automatic detection of sharp gradients 2. New “proximity” for interpolation Representativeness Error Innovations Background New Innovations

32 WRF Workshop June 2010 Beyond WRF: MPAS - Summary 3D Solvers Hydrostatic 3D SVCT solver (pressure coordinate). Nonhydrostatic 3D SVCT solver (height coordinate). Both solvers work on the sphere and on 2D and 3D Cartesian domains. Tests results confirm viability of Voronoi C-grid discretization at large scales (global) and cloud-permitting scales for both solvers. Variable-resolution grid results are encouraging. Future Development Weather, regional climate and climate physics suites. Further testing of variable resolution meshes, physics development. Further development and testing of higher-order transport schemes. Expectations NWP testing by the end of this year. Friendly-user release summer (Bill Skamarock)

33 Summary 1.WRFDA – DA for WRF 2.DART and WRFDA 3.GSI and WRF 4.Future Directions? Regional radiance DA, BC (Bias Correction) Improving BE 4D-Var Optimization; EnKF; Hybrid 4D-Var/EnKF Beyond WRF - MPAS ACPAS (AFWA Coupled Prediction and Assimilation System)