Highlight of the CWB WRF development
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Transcript of Highlight of the CWB WRF development
Highlight of the CWB WRFdevelopment
OUTLINE Re-center the EAKF using the blending scheme Hybrid variational/ensemble data assimilation
Blending Scheme
RF
anal
RF
analanal MeanGFSMean ...
RF
fcsthr
RF
analfcsthr MeanGFSMean 6.6Guess
Blending
Analysis Blending
Fields to be Blended (13) - U, V, T, QVAPOR, PH, P, MU,- U10, V10, T2, Q2, PSFC, TH2
CLS=1200 km
EAKF
Xa1
Xa2
Xa32
Xb1
Xb2
Xb32
guess
analy_mean
analysis
guess_meanBlend
analysis
Ensemble Forecast
6hr
Guess Blending
guess_meanBlend
RECENTER
Xb1
Xb2
Xb32
WRF 72hr
EAKF
Xa1
Xa2
Xa32
Xb1
Xb2
Xb32
guess
Blend
Ensemble Forecast
6hr
Analysis Blending
Blend
analysis
Xa1
Xa2
Xa32
RECENTER
analy_meanBlend
EAKF
WRF 72hr
analy_mean
Experimental Design
Model Version: CWB OP24 Run: Full Cycling Domain: CWB WRF Domain 1 (45KM) Period: 2008/09/04 00UTC ~ 2008/09/23 00 UTC T-PARC Experiments :
1) Without Blending (woBL, CTL)2) Guess Blending (guessBL)3) Analysis Blending (analyBL)4) analyBL + Ensemble Mean Analysis with Bleinding (analyBL_mBL)
Ensemble Performance
T U V
guessBL
analyBL
woBL
over
small
SCORE of Ensemble Mean Analysis Forecast 72hr (against NCEP)
H T U V24hr
woBL :代表無 Blending guessBL : Guess BlnedinganalyBL : Analysis Blending analyBL_m : blending again on analyBL ensemble mean
H T U V48hr
SCORE of Ensemble Mean Analysis Forecast 72hr (against NCEP)
woBL :代表無 Blending guessBL : Guess BlnedinganalyBL : Analysis Blending analyBL_m : blending again on analyBL ensemble mean
H T U V
72hr
SCORE of Ensemble Mean Analysis Forecast 72hr (against NCEP)
woBL :代表無 Blending guessBL : Guess BlnedinganalyBL : Analysis Blending analyBL_m : blending again on analyBL ensemble mean
TC Track Forecasts - SINLAKU (1)
woBL analyBLguessBL analyBL_m
from 2008/09/09 00UTC to 2008/09/13 00UTC
woBL :代表無 Blending guessBL : Guess BlnedinganalyBL : Analysis Blending analyBL_m : blending again on analyBL ensemble mean
TC Track Forecasts - SINLAKU (2)
from 2008/09/09 00UTC to 2008/09/13 00UTC
woBL :代表無 Blending guessBL : Guess BlnedinganalyBL : Analysis Blending analyBL_m : blending again on analyBL ensemble mean
TC Track Forecasts - HAGUPIT (1)
woBL
analyBL
guessBL
analyBL_m
from 2008/09/20 00UTC to 2008/09/23 00UTC
woBL :代表無 Blending guessBL : Guess BlnedinganalyBL : Analysis Blending analyBL_m : blending again on analyBL ensemble mean
TC Track Forecasts - HAGUPIT (2)
from 2008/09/20 00UTC to 2008/09/23 00UTC
woBL :代表無 Blending guessBL : Guess BlnedinganalyBL : Analysis Blending analyBL_m : blending again on analyBL ensemble mean
Summary Guess-blending outperforms the other
experiments, especially for the typhoon track More detail analysis is undergoing to answer why
guess blending is the best The effect of the blending scheme is bounded
by the GFS performance, the blending strategy should be re-evaluated as the GFS was improved, e.g. 2012 NCEP GFS
Performance of the hybrid system
Single Observation Tests in 3DVAR-Hybrid - Tests of Tuning Factors in Hybrid
Localization Scale Ensemble Covariance Weighting Vertical localization
The use of the ensemble perturbation
Single Observation Tests in 3DVAR-Hybrid
Observation Setting
Tuning Factors in Hybrid Localization Scale Ensemble Covariance Weighting
• Temperature• innov = 1 K , obs_err = 1 K• Lon ~ 137.143 ( x = 150 )• Lat ~ 28.2 ( y = 69 )• Lev ~ 860 mb ( z = 11 )
Localization Scale 200 km
375 km
750 km
Ensemble System : EAKFShade : IncrementGreen Line : Geopotential Height of Ensemble Forecast 6hr Mean.
VAR
50%
75%
Full
Ensemble Covariance Weighting Factor
Ensemble System : EAKFShade : IncrementGreen Line : Geopotential Height of Ensemble Forecast 6hr Mean.
EnSRF HGSI
Y-Z Plane
Lev=11 (~860mb
)
HVAREAKF
Lev=21 (~520mb
)Y-Z
Plane EnSRF HGSI
HVAREAKF
Lev=30 (~250mb
)Y-Z
Plane EnSRF HGSI
HVAREAKF
𝑳𝒄=𝟏𝟎𝒏𝒛 ×𝒌𝒄
𝛒 (𝒌 ,𝒌𝒄 )=𝒆𝒙𝒑 [− (𝒌−𝒌𝒄 )𝟐 /𝑳𝒄𝟐 ]
Summary Localization scale of 200 km and 75%
ensemble BE are used in the hybrid analysis. Vertical localization in WRF VAR-Hybrid
should be tuned. The moisture field has the most dramatic
change as including the ensemble BE.
Evaluation of the 3DVAR-Hybrid
PART-1: Ensemble Members of EAKF without/with Blending PART-2: 3DVAR vs. Hybrid-EAKFPART-3: Hybrid-WEPS vs. Hybrid-EAKFPART-4:WEPS + EAKF
Evaluation of the 3DVAR-Hybrid
3DVAR vs. Hybrid-EAKFExperimental Design: Model Version: CWB OP25 Run: Partial Cycling Domain: CWB WRF Domain 1 (45KM) Period: 2008/09/09 00UTC ~ 2008/09/30 12 UTC T-PARC Ensemble members of Hybrid-EAKF from “analyBL”
case. Verified against the NCEP GFS analysis and
RAOB/dropsound
H T U V
3DVAR HEAKF
ana
H T U V
3DVAR HEAKF
12hr
H T U V
3DVAR HEAKF
24hr
H T U V
3DVAR HEAKF
72hr
Shade :Difference ( 3DVAR - HEAKF )
Green Line :Geopotential Height of HEAKF
U VOne Month Mean
100 mbana
12hr
72hr
U VOne Month Mean
300 mbana
12hr
72hr
Shade :Difference ( 3DVAR - HEAKF )
Green Line :Geopotential Height of HEAKF
One Month Mean
850 mbana
12hr
72hr
Shade :Difference ( 3DVAR - HEAKF )
Green Line :Geopotential Height of HEAKF
T Q
OBSLocation
12hr
T WIND Qs
3DVAR HEAKF
T WIND Qs
24hr 3DVAR
HEAKF
T WIND Qs
72hr 3DVAR
HEAKF
Summary
• Compare to the 3DVAR, the forecast performance is slightly better in the hybrid system.
• The major differences are high level wind, low level temperature and moisture.– The reason to cause the difference is not clear yet.
Evaluation of the 3DVAR-Hybrid
Hybrid-WEPS vs. Hybrid-EAKF
Hybrid-EAKF
Hybrid-WEPSϐe : ϐb = 0.75 : 0.25
Spin-up
HWPES HEAKF
SCORE of Ensemble Mean Analysis Forecast 72hr (against NCEP)
T ANA.
24hr
48hr
72hr
T
Qv
3DVAR HEAKF HWEP
S HEAKF850 mb
Analysis Difference in SINLAKU Period
3DVAR
SINLAKU Typhoon Track
HEAKF HWEPS
from 2008/08/09 12UTC to 2008/08/13 00UTC
3DVAR
HAGUPIT Typhoon Track
HEAKF
from 2008/08/20 00UTC to 2008/08/23 00UTC
HWEPS
3DVAR
from 2008/08/24 12UTC to 2008/08/28 00UTC
JANGMI Typhoon Track
HEAKF HWEPS
Evaluation of the 3DVAR-Hybrid
Hybrid-EAKF+WEPS vs. Hybrid-EAKF
32 members EAKF
20 members WEPS
52 membersEAKF+WEPS 3DVAR-Hybrid
Analysis Difference in SINLAKU Period 850 mb T, Qv
HEKWP - HEAKF
HWEPS - HEAKF
HEKWP
T Qv
12hr
24hr
48hr
72hr
SCORE of Ensemble Mean Analysis Forecast 72hr (against DROPSONDES_SPECIFIC_HUMIDITY)
HEKWPHEAKF
1.0𝛽e Lev=11 (~860mb)
T Q U V
HWEPS
HEAKF
HEKWP
Full ensemble mode
1.0 Lev=30 (~250mb)𝛽e
T Q U V
HEAKF
HEKWP
HWEPS
Full ensemble mode
Summary The difference between HEAKF,
HWEPS, and HEKW is limited.HWEPS has the moisture spin-up
issue.
Flow dependent BE
Initial perturbation
Model pert.
BC pert.
Deterministic prediction
Ensemble prediction system
The advanced data assimilation system
Recentered-EAKF
Ensemble data assimilation system play a key role to support the ensemble and deterministic prediction
Dual-resolution3DVAR hybrid DA
4D VAR (hybrid)
Rapid updated radar DA
blending