for the NCEP hybrid GSI EnKF data - University Of … 4DVAR for the NCEP hybrid ... Model: GFS T190...
Transcript of for the NCEP hybrid GSI EnKF data - University Of … 4DVAR for the NCEP hybrid ... Model: GFS T190...
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Ensemble 4DVAR for the NCEP hybrid GSI‐EnKF data assimilation system and observation impact study with the
hybrid system
Xuguang WangSchool of Meteorology
University of Oklahoma, Norman, OK
1University of Maryland, Weather & Chaos seminar
Feb. 20, 2012
OU: Ting Lei, Govindan Kutty, Yongzuo Li, Ming Xue, Brian Holland, Terra ThompsonNOAA/NSSL: Lou WickerNOAA/ESRL: Jeff WhitakerNOAA/NCEP/EMC: Daryl Kleist, Dave Parrish, Russ Treadon, John Derber
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Outline
Part 1: Ensemble 4DVAR for the NCEP hybrid GSI‐EnKF data assimilation system
Part 2: Observation impact study with the hybrid DA system
Part 3: Improving high resolution TC prediction using hybrid DA
Part 4: LETKF related work
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Part 1: Ensemble 4DVAR for NCEP hybrid GSI‐EnKF data assimilation system
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Hybrid GSI‐EnKF DA system
member 1 forecast
member 2 forecast
member k forecast
control forecast GSI -ECV
EnKF
control analysis
EnKFanalysis k
EnKFanalysis 2
EnKFanalysis 1
member 1 forecast
member 2 forecast
member k forecast
data assimilationFirst guess forecast
control forecast
Ensemble covariance
member 1 analysis
member 2 analysis
member k analysis
Re-center EnK
Fanalysis ensem
bleto control analysis
Wang et al. 2011
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NCEP pre‐implementation test of hybrid DAhttp://www.emc.ncep.noaa.gov/gmb/wd20rt/experiments/prd12q3r/vsdb/
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• Observations (e.g., satellite) are spreading through the DA window.
• Current GSI‐EnKF hybrid is 3DVAR‐based ‐‐ temporal evolution of error covariance within DA window not considered.
• ENS4DVAR is a natural extension of the current 3D GSI‐EnKFhybrid.
• Temporal evolution of the error covariance within the assimilation window is realized through the use of ensemble perturbations (e.g., Qiu et al. 2007)
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Ensemble 4DVAR for GSI: motivation
Lei, Wang et al. 2012
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''1''1
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HxyHxyαCαxBx
αx
oToTT
oe JJJJ
R
K
k
ekk
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B 3DVAR static covariance; R observation error covariance; K ensemble size; C correlation matrix for ensemble covariance localization; e
kx kth ensemble perturbation;'1x 3DVAR increment; 'x total (hybrid) increment; 'oy innovation vector;
H linearized observation operator; 1 weighting coefficient for static covariance;
2 weighting coefficient for ensemble covariance; α extended control variable.
• Extended control variable method in 3D GSI hybrid (Wang 2010, MWR):
Extra term associated with extended control variable
Extra increment associated with ensemble
Ensemble 4DVAR for GSI: method
Add time dimension in ENS4DVAR
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One obs. example for TC
‐3h 0 3h*
ens4dvar ens3dvar
–3h increment propagated by model
integration
t=0 t=0 t=0
time
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tt‐3h t+3h
Temp.
Height
t‐3h t t+3h
Upstream impact
Downstream impact
Another example
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Why Hybrid? “Best of both worlds” VAR (3D,4D)
EnKF hybrid References (examples)
Benefit from use of flow dependent ensemble covariance instead of static B
x x Hamill and Snyder 2000; Lorenc 2003, Wang et al. 2007b,2008ab, 2009b; Zhang et al. 2009; Buehneret al. 2010ab; Wang 2011;
Robust for small ensemble x Wang et al. 2007b, 2009b; Buehner et al. 2010b
Better localization for integrated measure, e.g. satellite radiance; radar with attenuation
x Campbell et al. 2010
Easiness to add various constraints
x x
Outer loops x x
More use of various existing capability in VAR
x x
Summarized in Wang 2010, MWR
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ExperimentsTest period: Aug. 15 2010 – Sep. 20 2010
Model: GFS T190 64 levels
Observations: all operational data
Data assimilation methods: o GSI (gsi)o 3D GSI‐EnKF (hybrid1way)o ensemble 4DVAR: 2‐hourly frequency (ens4dvar1way) 1‐hourly frequency (ens4dvar1way‐hrly)o excluding the balance constraint: hybrid1way‐nb ens4dvar1way‐nb
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Hurricane track forecasts
2010 hurricanes
• ens3dvar hybrid better than GSI and further improvement by ens4dvar hybrid. • Balance constraint in GSI hurt TC forecast for both ens3dvar and ens4dvar hybrid.
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Global forecasts verified against EC analyses
Height Temperature
• ens3dvar hybrid better than GSI and further improvement by ens4dvar hybrid. • Balance constraint in GSI help both ens3dvar and ens4dvar hybrid.
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Global forecasts verified against conv. obs.
Significant improvement of ens3dvar hybrid and ens4dvar hybrid over GSIens4dvar showed further improvement over ens3dvar especially for wind
6h wind 6h temp
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Global forecasts verified against conv. obs.
Significant improvement of ens3dvar hybrid and ens4dvar hybrid over GSIens4dvar showed further improvement over ens3dvar especially when “nb” balance constraint seems helpful at early lead time, but hurt at later lead time for hybrid
96h wind 96h temp
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Summary
Ensemble‐4DVAR capability was developed for GSI and tested for GFS. Ensemble‐4DVAR further improved upon the ENS3DVAR hybrid for TC track forecasts, global forecasts verified against EC and obs.
Depending on the specific forecasts, the balance constraint in the GSI can benefit or hurt the forecasts . Improvements on balance constraint needed.
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Part 2: Observation impact study with the hybrid DA system
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Introduction
Modern data assimilation systems assimilate millions ofobservations each day to produce initial conditions fornumerical weather forecasts.
What are the impacts of the obs. from different platforms? Are impacts dependent on DA methods and how? Are impacts dependent on how impacts are assessed and
how?
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Increments with static and flow dependent B
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Increments by GSI and hybrid: AMSU
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Introduction (cont’d)
Various methods have been used to assess the impacts ofobservations
Observing System Experiments (OSE; e.g., Zapotocny et al.,2007, etc.)
Adjoint method (e.g., Baker and Daley, 2000; Gelaro et al.,2008, etc.)
Ensemble based method (e.g., Liu and Kalnay, 2008; Kalnay2011; Kunii et al. 2012, etc.)
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Experiment design
Test period : Dec. 15 ‐ Jan.31 2010Model: Global Forecast System Model (GFS) T190 64 levels Observations denied: AMSU, Rawinsonde Data assimilation methods: GSI Hybrid GSI‐EnKF (one way coupled) 6 experiments for OSE GSI assimilating all obs. (gsi) GSI denied rawinsonde (gsi noraob) GSI denied AMSU (gsi noamsu) Hybrid assimilating all obs. (hybrid) Hybrid denied rawinsonde (hybrid noraob) Hybrid denied AMSU (hybrid noamsu)
22Kutty, Wang et al. 2012
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Experiment design (cont’d)
Ensemble based obs. impact estimate (Kalnay 2011)
Goal evaluate the quality of the estimate for the hybrid system research to further improve the estimate compare with other obs. impact metric, e.g., OSE
Initial experiments Estimate of rawinsonde temp. impact for temp. and wind forecasts. Verified w.r.t actual impact. Same for satellite data (e.g., AMSU)
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RMSE of global forecasts w.r.t EC analysis
24h wind 24h temperature 24h spec. humidity
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RMSE of global forecasts w.r.t obs.
24h wind 24h temperature
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Zonally averaged impact (wind RMSE difference)
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Zonally averaged impact (temp. RMSE difference)
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Zonally averaged impact (humid. RMSE difference)
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Relative impact (500mb Height)
(b) gsi noraob(a) gsi noamsu
(c) hybrid noamsu (d) hybrid noraob
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1000hPa
925hPa
850hPa
700hPa
500hPa
300hPa
250hPa
200hPa
global
-0.0035 -0.0030 -0.0025 -0.0020 -0.0015 -0.0010 -0.0005 0.0000
estimate actual
error reduction / gridpoint (K2)
Ensemble based obs. impact estimate for the hybrid system
1000hPa
925hPa
850hPa
700hPa
500hPa
300hPa
250hPa
200hPa
global
-0.008 -0.006 -0.004 -0.002 0.000
estimate actual
error reduction /grid point (ms-1)2
Rawinsonde temp. impact for 24h temp. fcst
Rawinsonde temp. impact for 24h wind. fcst
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OSE Forecasts by hybrid DA was better than GSI in both control and data denial
experiments. In some verifications, the hybrid assimilating less data was even better than the GSI assimilating all obs.
Magnitude and distribution of obs. impact depend on the obs. and the DA methods. The relative impact between rawinsonde and AMSU depends on DA methods and verifications.
Ensemble based obs. impact estimate for the hybrid Initial results showed the estimate was promising. The quality of the estimate can be dependent on obs. and forecasts of interest, forecast lead time, etc.
Summary
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Part 3: Improving high resolution TC prediction using hybrid DA
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•WRF: Δx=5km
•Observations: radial velocity from two WSR88D radars (KHGX, KLCH)
Radar hybrid DA for Ike 2008
Li, Wang, Xue 2012, MWR
IKE 2008
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Temperature increment
3DVARb Hybrid1 Hybrid.5
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No Radar DA 3DVARb
Hybrid1 Hybrid.5
Cold core
Warm core
Wind and pot. temperature analyses
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Wind and Precipitation forecasts
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Part 4: LETKF related work
Effects of sequential or simultaneous assimilation and localization methods on the performance of ensemble Kalmanfilter
Doppler Radar data assimilation using the LETKF
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Motivation
Popular EnKF schemes contain differences(1) Stochastic versus deterministic
EnKF vs. LETKF, EnSRF, ETKF, etc.(2) Simultaneous vs. Sequential
LETKF vs. EnSRF, EnKF(3) If and how localization is applied
B vs. R localization Previous studies (e.g. Kepert 2009; Greybush et al. 2011; Janjic 2011)
related to (2) & (3) did not reach consensus Rigorous study on whether the choice of (2) and (3) has an effect on
EnKF accuracyo Optimally tunedo Study the effect of (2) and (3) in isolation and their interactiono Comparison in variety of contexts
Holland and Wang, 2012, QJRMS
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Experimental Design
• 2‐layer dry global primitive equation model (Zou et al. 1993)
• Model error: T31 model truncation, T127 Truth run• Observations = truth run + added error• Comparison in various contexts
Baseline experiments: 50 member, Surface π &Interface height obs. Other experiments: with digital filter initialization,
adding more observation types, reducing observation number, increasing ensemble size, decreasing Pb/R ratio.
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Scheme Breakdown
SchemeSerial/
simultaneous Localization Algorithm
Bserial Serial B EnSRF
Rserial Serial R EnSRF
Bsimult Simult B EnSRF
Rsimult Simult R EnSRF
LETKF Simult R LETKF
LETKF: Hunt et al. 2007
EnSRF: Whitaker and Hamill 2002
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Baseline experiments: Analysis Error
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Baseline experiments: imbalance
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Explore imbalance difference using non‐cycling experiments
Surface π tendency increment difference (Bsimult‐Rsimult)
Wind and height gradient increment difference (Bsimult‐Rsimult)
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Conclusion
Baseline experiment:• Effects of sequential vs. simultaneous assimilation depend on
whether B‐ or R‐localization was being used (Bsimult worse than Bserial; Rsimult better than Rserial)
• Effects of B‐ or R‐localization depend on simultaneous or serial assimilation was being used (Bserial ~ Rserial; Bsimult worse than Rsimult)
• Schemes that produced less accurate analyses tended to be least balanced.
Sensitivity experiments:• Difference among the schemes reduced when digital filter
initialization was used, both wind and mass observations were assimilated, the number of observations was decreased, the ensemble size was increased, and the ratio of forecast error to observation error in the system was decreased.
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Doppler Radar Data Assimilation Using LETKF
Apply LETKF to storm‐scale radar data assimilation
Study the effect of simultaneous/serial assimilation and localization methods for storm scale by comparing with serial EnSRF
Study the scalability of parallel LETKF.
Thompson, Wicker and Wang, 2012
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Vertical Velocity
Refle
ctivity
Truth LETKF EnSRF
Preliminary OSSE results after 1 hour of DA
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Ongoing and future work
Working with collaborators, further develop, test and research on ens4dvar and TL/ADJ 4DVAR hybrid for Global DA.
Working with collaborators, further test and research the hybrid (ens3dvar, ens4dvar) for regional NWP.
Conduct OSE for hybrid DA for other data and other forecasts of interest. Conduct OSE using ens4dvar. Research and improve the ensemble based obs. impact estimation for different
data types and different forecasts for the hybrid DA system. Research on obs. impacts inferred by e.g., OSE, ensemble based method and
adjoint method. Continue efforts on LETKF for storm scale radar assimilation.
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ReferencesCampbell, W. F., C. H. Bishop, D. Hodyss, 2010: Vertical Covariance Localization for Satellite Radiances in Ensemble KalmanFilters. Mon. Wea. Rev., 282‐290.Lorenc, A. C. 2003: The potential of the ensemble Kalman filter for NWP – a comparison with 4D‐VAR. Quart. J. Roy. Meteor. Soc., 129, 3183‐3203.Buehner, M., 2005: Ensemble‐derived stationary and flow‐dependent background‐error covariances: evaluation in a quasi‐operational NWP setting. Quart. J. Roy. Meteor. Soc., 131, 1013‐1043.Hamill, T. and C. Snyder, 2000: A Hybrid Ensemble Kalman Filter–3D Variational Analysis Scheme. Mon. Wea. Rev., 128, 2905‐2915. Wang, X., C. Snyder, and T. M. Hamill, 2007a: On the theoretical equivalence of differently proposed ensemble/3D‐Var hybrid analysis schemes. Mon. Wea. Rev., 135, 222‐227. Wang, X., T. M. Hamill, J. S. Whitaker and C. H. Bishop, 2007b: A comparison of hybrid ensemble transform Kalman filter‐OI and ensemble square‐root filter analysis schemes. Mon. Wea. Rev., 135, 1055‐1076. Wang, X., D. Barker, C. Snyder, T. M. Hamill, 2008a: A hybrid ETKF‐3DVar data assimilation scheme for the WRF model. Part I: observing system simulation experiment. Mon. Wea. Rev., 136, 5116‐5131. Wang, X., D. Barker, C. Snyder, T. M. Hamill, 2008b: A hybrid ETKF‐3DVar data assimilation scheme for the WRF model. Part II: real observation experiments. Mon. Wea. Rev., 136, 5132‐5147. Wang, X., T. M. Hamill, J. S. Whitaker, C. H. Bishop, 2009: A comparison of the hybrid and EnSRF analysis schemes in the presence of model error due to unresolved scales. Mon. Wea. Rev., 137, 3219‐3232.Wang, X., 2010: Incorporating ensemble covariance in the Gridpoint Statistical Interpolation (GSI) variational minimization: a mathematical framework. Mon. Wea. Rev., 138,2990‐2995. Wang, X. 2011: Application of the WRF hybrid ETKF‐3DVAR data assimilation system for hurricane track forecasts. Wea. Forecasting, in press. Li, Y, X. Wang and M. Xue, 2011: Radar data assimilation using a hybrid ensemble‐variational analysis method for the prediction of hurricane IKE 2008. Mon. Wea. Rev., submitted.Buehner, M, P. L. Houtekamer, C. Charette, H. L. Mitchell, B. He, 2010: Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single‐Observation Experiments. Mon. Wea. Rev., 138,1550‐1566. Buehner, M, P. L. Houtekamer, C. Charette, H. L. Mitchell, B. He, 2010: Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part II: One‐Month Experiments with Real Observations. Mon. Wea. Rev., 138,1550‐1566. Holland, B. and X. Wang, 2011: Effects of sequential or simultaneous assimilation of observations and localization methods on the performance of the ensemble Kalman filter . Q. J. R. Meteo. Soc., submitted
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Flow‐dependent covariance
K
kk
GSI (static covariance) Hybrid (ensemble covariance)
Wang et al. 2011
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Track and intensity forecasts
No radar
No radar
Li et al. 2012
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3DVAR with un‐tuned static covariance (3DVARa)
3DVAR with tuned static covariance (3DVARb)
Hybrid with full ensemble covariance (hybrid1)
Hybrid with half ensemble covariance and half static covariance (hybrid.5)
Wind increment
Li et al. 2012