Upload
nay
View
52
Download
0
Embed Size (px)
DESCRIPTION
Satellite Data Assimilation for meso-scale models Hans Huang National Center for Atmospheric Research (NCAR is sponsored by the National Science Foundation). Slides are collected from: Zhiquan Liu, Thomas Auligne, Xin Zhang, Hui Shao, - PowerPoint PPT Presentation
Citation preview
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, DTCAFWA, 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 WRF2. DART and WRFDA3. GSI and WRF4. Future Directions?
3
WRFDAhttp://www.mmm.ucar.edu/wrf/users/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
92/22/08 9
102/22/08 10
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 WRF2. DART and WRFDA3. GSI and WRF4. Future Directions?
Radiance Data Assimilation with DARTZhiquan 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
Jacobian as vertical coordinate
Use DART built-in vertical
localization
AMSU-A Jacobian w.r.t. T
∂Tb∂T
(K/K)∂Tb∂T
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
08050806
0808
0809 0807
Red: Typhoon
Blue: Tropical storm or depression
Numbers refer to date at 0000 UTC:
(0806…06 Aug 2009)
Produced very heavy precip. over Taiwan at
landfall.
18
19
20
21
Outline
1. WRFDA – DA for WRF2. DART and WRFDA3. GSI and WRF4. Future Directions?
22
DTC (GSI) taskshttp://www.dtcenter.org/com-GSI/users
• 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
v3.2• 15 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 domainTwo 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
26
Bias Correction
OMB without BC
OMB with BC
OMA
27
Outline
1. WRFDA – DA for WRF2. DART and WRFDA3. GSI and WRF4. 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 observation 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=> +
3131
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
32WRF Workshop June 2010
Beyond WRF: MPAS - Summary3D 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 2011.
(Bill Skamarock)
33
Summary1. WRFDA – DA for WRF2. DART and WRFDA3. GSI and WRF4. 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)