Transcript Satellite Data Assimilation for meso-scale models Hans Huang National Center for Atmospheric Research

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,
Chunhua Zhou, Syed Rizvi,Yaodeng Chen, Craig Schwartz,
Thomas Nehrkorn, Bill Skamarock, …
Acknowledge:
NCAR/NESL/MMM/DAS, NCAR/RAL/JNT/DAT, DTC
AFWA, USWRP, NSF-OPP, NASA, AirDat, PSU,
KMA, CWB, CAA, BMB, EUMETSAT
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Outline
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WRFDA – DA for WRF
DART and WRFDA
GSI and WRF
Future Directions?
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WRFDA
http://www.mmm.ucar.edu/wrf/users/wrfda
Goal: Community WRF DA system for
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regional/global,
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research/operations, and
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deterministic/probabilistic
applications.
Techniques:
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3D-Var
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4D-Var (regional)
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Ensemble DA,
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Hybrid Variational/Ensemble DA.
Model: WRF (ARW, NMM, Global)
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WRFDA Observations
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In-Situ:
•Bogus:
- Surface (SYNOP, METAR, SHIP, BUOY).
–TC bogus.
- Upper air (TEMP, PIBAL, AIREP, ACARS, TAMDAR).
–Global bogus.
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):
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HIRS
AMSU-A
AMSU-B
MHS
AIRS
SSMIS
from NOAA-16, NOAA-17, NOAA-18, NOAA-19, METOP-2
from NOAA-15, NOAA-16, NOAA-18, NOAA-19, EOS-Aqua, METOP-2
from NOAA-15, NOAA-16, NOAA-17
from NOAA-18, NOAA-19, METOP-2
from EOS-Aqua
from DMSP-16
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WRFDA Radiance Assimilation
(Liu and Auligne, MMM)
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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
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NCAR/RAL/JNT/DAT: Atlantic Testbed
(AFWA T8)
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361*325*57L, 15km
Model top: 10mb
Full cycling exp. for 6 days
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GTS: assimilate NCAR conventional obs
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Select similar data type used by AFWA
GTS+AMSU+MHS (use NCEP BUFR rad.)
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15 ~ 20 August 2007
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
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Land Use Category
00Z, 12Z
(Liu, MMM and Shao, RAL)
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48h forecast error vs. sound
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2/22/08
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4DVAR vs. 3DVAR
45km resolution
(4DVAR is still very slow)
model top = 10mb
Only assimilate radiance data
(AMSU/MHS), 6h time window
2/22/08
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(Adjoint based) Observation Impact: Conventional Data
(Auligne, MMM)
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Observation Impact: Satellite radiances
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Outline
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WRFDA – DA for WRF
DART and WRFDA
GSI and WRF
Future Directions?
Radiance Data Assimilation with DART
Zhiquan Liu, Craig Schwartz, Xiang-Yu Huang (NCAR/MMM)
Yongsheng Chen (York University)
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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
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Vertical Localization
Tb (K/K)
T
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
Tb
T
(K/K)
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Bias Correction and QC
Bias correction coefficients from the end of 3DVAR experiment.
Use Ensemble Mean as reference for BC and QC.
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Typhoon Morakot
Red: Typhoon
Blue: Tropical storm or depression
0808
Numbers refer to date at 0000 UTC:
(0806…06 Aug 2009)
Produced very heavy precip. over Tai
wan at landfall.
0809
0807
0806
0805
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Outline
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WRFDA – DA for WRF
DART and WRFDA
GSI and WRF
Future Directions?
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DTC (GSI) tasks
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http://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.
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DTC GSI T&E: end-to-end testing system
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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
AFWA T8 Domain
• 15 km horizontal resolution
• Global Background Errors
• Full 6-hr cycling
• AFWA T8 domain
Two Experiments:
AMSUA: assimilating PrepBUFR + AMSU_A,
updated air-mass satbias from previous
cycle, all channels included
CONV: assimilating PrepBUFR data only
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against ECMWF (T8,+48h 2007081512-2007082212)
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Bias Correction
OMB without BC
OMB with BC
OMA
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Outline
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WRFDA – DA for WRF
DART and WRFDA
GSI and WRF
Future Directions?
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Consider diurnal cycle or descending/ascending
orbit issue with VarBC for regional applications
Bias with diurnal cycle.
Morning (12Z): -0.60K
Evening (00Z): -0.15K
Use one set of BC coefs for 00Z/12Z
(oscillation still exists after BC)
(Related to Descending/Ascending nodes)
Use separated BC coefs for 00Z/12Z
(Oscillation is removed after BC)
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Still need to work on BE (Rizvi, Krysta, Chen, Huang)
CV3
CV5
Increments from single T observatio
n at 5th level , 15N
CV6
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Displacement DA Approach
Conceptual view of using displacements to characterize errors
Partition:
background error
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displacements of coherent
features
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additive (residual) error
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Towards Cloudy Radiance Assimilation
Representativeness Error
Simulated mismatch in resolution:
observations (high resolution)
•Perfect
•Perfect Background (lower resolution)
New interpolation scheme:
Background
1. Automatic detection of sharp gradients
2. New “proximity” for interpolation
New Innovations
Innovations
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Beyond WRF: MPAS - Summary
3D Solvers
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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
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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
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NWP testing by the end of this year.
Friendly-user release summer 2011.
WRF Workshop June 2010
(Bill Skamarock)
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Summary
WRFDA – DA for WRF
DART and WRFDA
GSI and WRF
Future Directions?
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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)
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