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GSI developments and plans at NCAR/MMM. Tom Aulign é Aimé Fournier, Hans Huang, Andy Jones, Hui-Chuan Lin, Zhiquan Liu, Yann Michel, Arthur Mizzi, Thomas Nehrkorn, Syed Rizvi, Hongli Wang, Xin Zhang National Center for Atmospheric Research NCAR is supported by the National Science Foundation - PowerPoint PPT Presentation
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GSI developments and plans at NCAR/MMM
Tom Auligné
Aimé Fournier, Hans Huang, Andy Jones, Hui-Chuan Lin, Zhiquan Liu, Yann Michel,
Arthur Mizzi, Thomas Nehrkorn, Syed Rizvi, Hongli Wang, Xin Zhang
National Center for Atmospheric Research
NCAR is supported by the National Science Foundation
GSI Data Assimilation Workshop - June 28, 2011
Focus at NCAR/MMM– Regional GSI– WRF-ARW model (NetCDF files)
Projects funded by AFWA– AFWA Coupled Analysis and Prediction System (ACAPS)– AFWA Data Assimilation– AFWA Aerosols
Collaboration with – GSI developers (EMC, GMAO, GSD, DTC)– JCSDA
Introduction
• Background and Observation Errors
• Variational/Ensemble Hybrid
• Displacement Pre-processing
• WRF Adjoint: 4DVar and Observation Impact
• Aerosol and Cloud Satellite Observations
• Verification
Outline
• Background and Observation Errors
• Variational/Ensemble Hybrid
• Displacement Pre-processing
• WRF Adjoint: 4DVar and Observation Impact
• Aerosol and Cloud Satellite Observations
• Verification
Outline
Background and Obs Errors: Community tools
“Community GEN_BE” utility: https://svn-wrf-var.cgd.ucar.edu/branches/gen_be
– Includes all the features of WRFDA V3.2.2– Multi-variate humidity– Generation of WRF-ARW background errors for GSI
Extension of GEN_BE to include– Aerosol concentrations (univariate)– Cloud parameters (Qcloud, Qrain, Qice, Qsnow)
Expansion of GSI control variable
Observation error tuning with the diagnostic equations (Desroziers 2005)
E dba (db
o )T⎡⎣ ⎤⎦=HBHT E dao(db
o )T⎡⎣ ⎤⎦=R
E dba (da
o )T⎡⎣ ⎤⎦=HAH TE dbo(db
o )T⎡⎣ ⎤⎦=HBH T + R
Background Error Covariances: Masked Statistics
Michel et al. (MWR, 2011)
Background Error Covariances: Masked Statistics
Background Error Covariances: Wavelets
Background Error Covariances: Wavelets
• Background and Observation Errors
• Variational/Ensemble Hybrid
• Displacement Pre-processing
• WRF Adjoint: 4DVar and Observation Impact
• Aerosol and Cloud Satellite Observations
• Verification
Outline
Variational/Ensemble Hybrid
• WRF/GSI Regional Hybrid• Testing package: https://svn-mmm-hybrid-testbed.cgd.ucar.edu/HYBRID_TRUNK
Cf. presentation by Arthur Mizzi
• Background and Observation Errors
• Variational/Ensemble Hybrid
• Displacement Pre-processing
• WRF Adjoint: 4DVar and Observation Impact
• Aerosol and Cloud Satellite Observations
• Verification
Outline
Conceptual view of using displacements to characterize errors
background error
displacements of coherent features
additive (residual) error
=> +
Displacement Pre-Processing
Initial time:08-28-05 06:00:00z
Vortex displaced forward along track
18 Hour forecast time:08-29-05 00:00:00z
18 hours later vortex maintains forward position
Collaboration between AER, MIT and NCAR
Integration of displacements– Build on the existing API, with enhancements to add:– Support for multiple displacement algorithms
Algorithmic developments– Constraints formulated and evaluated specifically for cloud-related fields
• Candidates: smoothness, non-divergence of displacements• Application in: grid point, spectral, or wavelet space
– Time evolution of displacements• Characterize and model the time evolution of displacements• Prepare for integration with 4D-Var
– Figures of Merit for cloud-related fields
Displacement Pre-Processing: Status and Plans
• Background and Observation Errors
• Variational/Ensemble Hybrid
• Displacement Pre-processing
• WRF Adjoint: 4DVar and Observation Impact
• Aerosol and Cloud Satellite Observations
• Verification
Outline
WRF Adjoint: WRF/GSI 4DVar
New TL/AD code: WRFPLUS– Consistent with latest WRF-ARW (v3.3) – Includes simplified physics (surface drag, large-scale condensation,
cumulus scheme, Kessler microphysics)
WRF/GSI 4DVar– Based on GMAO 4DVar framework– New coupling between GSI and WRF/WRFPLUS
Initial testing looks good.
Cf. presentation by Xin Zhang
WRF Adjoint: Observation Impact
Observation(y)
WRFDA/GSIData
Assimilation
WRF-ARWForecast
Model
Forecast(xf)
DeriveForecastAccuracy
Background(xb)
Analysis(xa)
Adjoint of WRF-ARW
ForecastTL Model
(WRF+)
ObservationSensitivity(F/ y)
BackgroundSensitivity(F/ xb)
AnalysisSensitivity
(F/ xa)
Observation Impact<y-H(xb)> (F/ y)
Adjoint of WRFDA/GSI
Data Assimilation
Obs Error Sensitivity(F/ ob)
Gradient of F
(F/ xf)
DefineForecastAccuracy
ForecastAccuracy
(F)
Bias CorrectionSensitivity
(F/ k)
Figure adapted from Liang Xu (NRL)
• Background and Observation Errors
• Variational/Ensemble Hybrid
• Displacement Pre-processing
• WRF Adjoint: 4DVar and Observation Impact
• Aerosol and Cloud Satellite Observations
• Verification
Outline
Aerosol Satellite Observations
Assimilation of MODIS Aerosol Optical Depth in GSI– Process MODIS AOD data (HDF to BUFR converter)– Use CRTM-AOD (Quanhua Liu)– Couple with WRF-Chem GOCART (14 aerosol species)
Status and plans– Assimilate surface PM2.5 (ongoing)– Assimilate MODIS Visible/NIR radiances (planned, pending)
Cf. presentation by Zhiquan Liu
Cloud Satellite Observations: Retrievals
MODIS cloud retrieval products– Cloud liquid/ice water path, cloud optical depth, particle effective radius
(1km resolution observations)– Cloud top properties: pressure, temperature, fraction/emissivity
(5km resolution observations)
Assimilation of MODIS Cloud Water Path– Process MODIS CWP data (HDF to BUFR converter)– Observation Operator (+ TL & AD)
Status and plans– Assimilate MODIS CWP at convective scale (ongoing)– Assimilate MODIS Cloud Optical Depth (planned)
Very first shot at cloudy radiances, still needs a lot more work…
Cloud parameters from WRF-ARW first-guess
CRTM forward model and Jacobian
Inclusion of cloud (microphysical) parameters in control variable (implemented in both WRFDA and GSI)
Cloud Satellite Observations: Radiance Assimilation
ObservationAIRS (12micron)
Background(WRF-DART)
Observation – Background
Simple B Matrix forcloud parameters copied from humidity
Ensemble assimilation usingthe alpha control variable(no tuning)
Cloud Satellite Observations: Radiance Assimilation
Clear observations only
Cloud Satellite Observations: Radiance Assimilation
Simple B Matrix forcloud parameters copied from humidity
Remaining issues include:
- Bias Correction
- Quality Control
- Non-linearities in the observation operator
- Representativeness Error
Cloud Satellite Observations: Radiances
Pixel
Nk1
Nk2 Nk3
No
Cloud Top Pressure (hPa)
MODIS Level2
AIRS MMR
with
[ ]nkN k ,0,10 ∈∀≤≤
∑=
=+n
k
kNN1
1o
Cloud fractions Nk are ajusted variationally to fit observations:
Cloud Satellite Observations: Radiances
CloudSat Reflectivity
AIRS MMR Effective Cloud Fraction
Cloud Satellite Observations: Radiances
RνObs −Rν
CldRνObs −Rν
CldRνObs −Rν
oRνObs −Rν
o
Towards Cloudy Radiance Assimilation
Pixel
Nk1
Nk2 Nk3
No
Cloud Satellite Observations: Radiances
31
Towards Cloudy Radiance Assimilation
Simulated mismatch in resolution:
- Perfect observations (high resolution)- Perfect Background (lower resolution)
Innovations
Background
Cloud Satellite Observations: Representativeness
32
Towards Cloudy Radiance Assimilation
New interpolation scheme:
1. Automatic detection of sharp gradients 2. New “proximity” for interpolation
Innovations
Background
New Innovations
Cloud Satellite Observations: Representativeness
Cloud Satellite Observations: Representativeness
The raw yo− yb (left) includes errors due to yo and yb coming from completely different representations, that (hypothetically) have been reconciled by the foregoing wavelet-coefficient selection procedure.
Cloud Satellite Observations: Representativeness
• Background and Observation Errors
• Variational/Ensemble Hybrid
• Displacement Pre-processing
• WRF Adjoint: 4DVar and Observation Impact
• Aerosol and Cloud Satellite Observations
• Verification
Outline
Period: 4-17 June 2009Analyses and 6 hr forecasts from 50-member ensembles using
Data Assimilation Research Testbed (DART) system
Verification: Test Case (courtesy Glen Romine)
15 km mesoscale, 3 km storm-scale
Verification: Validation Data
World-Wide MergedCloud Analysis (WWMCA)
Main Archive: •Quality-controlled, GOES East and GOES West over CONUS•Covers January 1998 – December 2009 •Resolution – 4x4 km for all channels except #3 which is 4x6 km•Monthly/hourly cloud cleared background for all visible hours•Monthly/hourly Cloud % using visible threshold•Monthly/every other hour Cloud % using IR threshold since 2003•Addition hours of QC’d GOES West for May-Sept 1999-2009
Example of GOES 8 background image
• New WRF Adjoint for GSI 4DVar and Observation Impact
• Community tool for Background Error calculation (GEN_BE)
• Specific developments for Cloud and Aerosol assimilation
• Opportunity for inter-comparison
Conclusion