mpo 674 lecture 22 4/2/15. single observation example for 4d variants d. kleist et al. 4dvar...
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![Page 1: MPO 674 Lecture 22 4/2/15. Single Observation Example for 4D Variants D. Kleist et al. 4DVAR H-4DVAR_AD f -1 =0.25 H-4DENVAR f -1 =0.25 4DENVARTLMADJ](https://reader035.vdocuments.us/reader035/viewer/2022070400/56649eff5503460f94c145dd/html5/thumbnails/1.jpg)
MPO 674 Lecture 22
4/2/15
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Single Observation Examplefor 4D Variants
D. Kleist et al.
4DVAR
H-4DVAR_ADbf-1=0.25
H-4DENVARbf-1=0.25
4DENVARTLMADJ
TLMADJ
ENSONLY
ENSONLY
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Lorenz, E.N., and K.A. Emanuel, 1998: Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model. J. Atmos. Sci., 55, 399–414.
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Lorenz, E.N., and K.A. Emanuel, 1998: Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model. J. Atmos. Sci., 55, 399–414.
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UYonsei MM5SV NRL SV JMA SV
ECMWF SV UMiami-NCEP ETKF UKMO ETKF
Targeted observing guidance (Typhoon Sinlaku, 2008)
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Sensitivity Methods
• Observation sensitivity– ETKF– Adjoint Observation Sensitivity / Impact
• Analysis sensitivity– Singular Vectors (done)– Adjoint Sensitivity (done)– Ensemble Sensitivity
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Signals and Signal VarianceSquared NCEP MRF signal
1/2 (u’2+v’2) + (cp/Tr) T’2
valid at analysis time ta
Predicted ETKF signal variance Sq, using ensembles initiated 36h prior
to analysis time ta
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tata
ETKF Summary map of Signal
Variance Sq, for many different q.
Summary bar chart
tv tv
GoodPoor
Aim: to improve a
24-hr forecast on the West Coast
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Evolution of operational
signal over 84h
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Evolution of predicted ETKF signal variance
over 84h
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Signal realization versus forecast
error reduction, at verification time tv
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0ld method: ETKF-based Pr
Heavy emphasis on TC (obvious target)
Secondary targets in areas of high ensemble variance over ocean, downstream of TC
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New method: Ensemble transform based on operational analysis error variance
Less emphasis on TC
Secondary targets: often upstream, in subtropical jet and mid-latitude troughs
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Suppose we wish to sample through 4 days of Typhoon Ewiniar (2006) as it recurves.
Can one identify spatio-temporal continuity of ETKF target regions?
Extension into medium-range (forecasts beyond 2 days)
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-4 days
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-3.5 days
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-3 days
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-2.5 days
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-2 days
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-1.5 days
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-1 day
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-0.5 days
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0 days
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Serial adaptive sampling
• Many combinations and permutations of adaptive observations are available.
• Suppose that two sets of observations can be deployed simultaneously.
• First, find the optimal first deployment. Next, calculate the best second deployment given that the first set of observations are to be assimilated by the ETKF at the same time.
• Reduces observational redundancy.
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Flight track number Flight track number
Serial adaptive sampling during
Winter Storm Reconnaissance
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Shortcomings of ETKF targeting strategy
• Inconsistency between imperfect error covariance in ETKF and operational data assimilation scheme
• Limited # ensemble members gives a rank-deficient P : leads to spurious correlations
• Ensemble mean and variance predictions must be reasonably accurate
• Theory is (quasi) linear
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Dependence of SVs on the analysis-time norm: Hurricane Charley (2004)
Using NAVDAS analysis error variance as constraint pushes primary target northward into Canada. 2-day growth diminished from 54.4 to 36.4.
NOGAPS Total-Energy SV NOGAPS Variance SV
NAVDAS Analysis Error Variance
Reynolds
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Ensemble Sensitivity (from Ryan
Torn)
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Overview
• Want to understand how initial condition errors associated with vortex and environment regulate the predictability of TC genesis
• Focus on two forecasts initialized roughly 48 h prior to genesis, one for Karl and another for Danielle
R. Torn
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Karl Forecast
R. Torn
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Methods
• Use ensemble-based sensitivity analysis to compute the sensitivity of 48 h 850 hPa circulation associated with the pre-genesis system to the initial conditions
R. Torn
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Sensitivity
R. Torn
Sensitivity of 48-h 850 hPa circ to 0-h 850 hPa circ
Sensitivity of 48-h 850 hPa circ to 0-h 400 hPa theta-e
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Vortex Sensitivity
• Most of the sensitivity appears to be associated with the pre-genesis system itself
• Instead compute sensitivity of forecast to vortex-average quantities at each vertical level for different lead times.
R. Torn
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Vortex Sensitivity
R. Torn
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Upwind Moisture Sensitivity
• Interesting to see the sensitivity of upwind moisture to the initial moisture field
• Compute sensitivity as before, except metric is now 0-48 h upwind moisture in 400-600 hPa layer
R. Torn
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Upwind Moisture Sensitivity
R. Torn
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Danielle Forecast
R. Torn
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Danielle Sensitivity
R. Torn
Sensitivity of 48-h 850 hPa circ to 0-h 850 hPa circ
Sensitivity of 48-h 850 hPa circ to 0-h 400 hPa theta-e
Increase theta-e to the south stronger circulation
Decrease theta-e to the north stronger circulation
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Danielle Sensitivity
R. Torn
Forecast of pre-Danielle has less “memory” of the initial vortex than Karl. Lower-level sensitivity of q and theta-e.