evaluation of fy2e reprocessed amvs in grapescimss.ssec.wisc.edu/iwwg/iww13/talks/01_monday/...slide...
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Evaluation of FY2E Reprocessed AMVs IN GRAPES
Wei Han, Xiaomin Wan and Jiandong GongNWP/CMA
IWW13, June 2016
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Slide 2
Outline
l FY2E reprocessed AMVs
l Observation Error and Number
l Experiments in GRAPES global forecast system
l Conclusion and discussion
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Slide 3
Reprocessed FY2E AMVs: algorithm changes
l A new calibration system
l Preprocess of satellite image- Eliminate the influence of abnormal
- Remove noise in the satellite image
l Second tracking algorithm
l Height assignment
- inversion layer
- target box full of cloud
Zhang Xiaohu et al.,2014: Status of operational AMVs from FY-2 satellites since the 11th winds workshop, IWW12.
1st: 32 X 32
2nd:16 X 16the slow biases are reduced
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Slide 4
1000-700hPa 700-400hPa 400-100hPa
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Slide 5
201308: IR winds
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Slide 6
O-A(ERA_INTERIM)
l 20130801~20130831
O-A(U wind, unit: m/s)
RMSE
BIAS
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Slide 7
1999
20012003
2005
20062008
2009 201120132014
ü Initiation of GPAPES Programme
ü Sponsored by the MOST and CMA
ü Unified dynamic frameü Regional physics package
ü Regional 3DVar (P-Level)
üOperation ofGRAPES-MesoV1.0 üPrototype of GRAPES-GFS
ü Regional 3DVar (M-Level)
ü Pre-operation ofGRAPES-GFS V1.0 üUpgrade of GRAPES-Meso V3.0
ü Operation of GRAPES-RAFSü Prototype of Regional 4DVar
üOperation ofGRAPES-TYMüUpgrade of
GRAPES-Meso V4.0üUpgrade of GRAPES-GFS V1.4Milestone of GRAPES
20162015
üOperation ofGRAPES-GFS V2.0On June 1st 2016
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Slide 8
N.H. Day-5 ACC at 500hPa timeseries
GRAPES global forecat system: performanceT639: CMA operational global systemGRAPES V1.4 (2013)GRAPES V2.0 (2015)
20130501-20150801
N.H. 500hPa
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Slide 9
Main features of GRAPES 3DVarGRAPES 3DVar Model-level: M3DVar Press-lev:P3DVar
VerticalcoordinateHeight-based terrainfollowingcoordinate &Charney-Phillipsstaggered grid
Pressure coordinate
Horizontalgrid Arakawa-C Arakawa-A
Analysisvariable π,u,v,q/rh/rh*/normalized rh* φ,u,v,rh/q
Backgrounderrorcovariance
Horizontal and vertical correlation separated/non-separated,NCEP(NMC) method for error co-variances
Controlvariabletransformation
Horizontal filter and vertical EOF transformation
Balanceconstraint Linear balanced + pressure-wind balance statistic regression
Spatialinterpolation Bi-linear interpolation (horizontal), linear or 3rd splineinterpolation (vertical)
Minimization Limited memory BFGS method
Programdesign Modular and parallel design
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Slide 10
Data Assimilation Experiments in GRAPES
l GRAPES global forecast system- LAT/LON grids, Terrain-following Height
- 3D-Var, 0.25*0.25degree, L60(model top=3hPa)
- Sonde, Synop, GPS_RO, AMSU-A, IASI , AIRS, MWHS2(FY3C)
- Become operational on June 1st 2016
l Experiments(0.5*0.5 degree)
- CNTL: All+FY2E AMVs(old)
- EXP: All+FY2E AMVs(new)
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Slide 11
GEO+LEO+GEO_LEO windsAssessment and Assimilation of high latitude Geo_Leo winds in GRAPES
Thanks Dr. Matthew A. Lazzara, Dr. David Santek ,Dr. Brett Hoover at SSEC!for providing Leo_Geo Winds
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Slide 12
Bias(O-B) time series
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Slide 13
400hPa-100hPa 700hPa-400hPa 1000hPa-700hPa
FY2E OLD
FY2E NEW
<O-B> of FY2E old and new AMVs(IR), 201308
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Slide 14
Geometrical interpretation of analysis
Desroziers, G. et.al,Diagnosis of observation, background and analysis-error statisticsin observation space, Q. J. R. Meteorol. Soc. (2005), 131, pp. 3385–3396
l Practical Implementation- Multi. Variable and Obs.
- QC
l Monitoring of Obs. Error
- based onO-B and O-A
- Easy to use
2
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o ooa b
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Slide 15
FY2E OLD
FY2E NEW—FY2E OLD
FY2E NEW
Observation Error Estimation using Desroziers’s method
Diagnosed Obs. Error Obs. Number(used)
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Slide 16
Verification of wind analyses against NCEP
global analysis: RMSE time series at 150hPa
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Slide 17
Verification of wind analyses against NCEP
global analysis: RMSE vertical profile
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Slide 18
Impact on Wind forecasts in East Asia
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Slide 19
Impact on Wind forecasts
N.H
S.H
Tropics
Global
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Slide 20
Impact on forecasts
S.H.
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Slide 21
Conclusions and Discussions
l The reprocessed FY2E IR AMVs- Observation number Increased (about 3 times )
- The mean biases are reduced
- The diagnosed observation error reduced
- The impact on GRAPES analyses and forecasts is Positive
l Ongoing work
- Tuning of the observation error of the AMVs
- Height adjustment of AMVs in DA
- Assimilation of Hourly AMVs in GRAPES Global 4D-Var
- Use of the reprocessed FY2 AMVs in China Reanalysis