Sensitivity of A Squall Line Simulation to Model and Initial Errors in South China Zhiyong Meng Duochang Wu Department of Atmospheric and Oceanic.
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Transcript Sensitivity of A Squall Line Simulation to Model and Initial Errors in South China Zhiyong Meng Duochang Wu Department of Atmospheric and Oceanic.
Sensitivity of A Squall Line Simulation to
Model and Initial Errors in South China
Zhiyong Meng Duochang Wu
Department of Atmospheric and Oceanic Sciences
School of Physics, Peking University
Nov.1, 2012, Kunming
Background: Conceptual Model of Squall Line
Features:
Quick development, fast movement,
large affecting area, severe
disastrous weathers
( adapted from Markowski and Richardson 2010 )
Background: Squall line cases
Guangzhou 7 May 2010
Beijing 23 June 2011
Maximum hourly rainfall 128mm
Background: Mesoscale Predictability
Impact of model resolution and physical parameterization schemes (Kuo et al. 1995)
TC
Sensitivity of forecast to initial field (Kuo et al. 1995)
mesoscale cyclone
Error growth due to moist processes (Ehrendorfer et al. 1999)
Winter cyclone , trough
Upscale growth of smaller scale error (Zhang et al. 2002, 2003)
Winter cyclone
Studies on the predictability of squall line has been very few
(Melhauser & Zhang, 2012)
4
Questions
For squall line in China:
• How sensitive is the simulation to model error?
• How sensitive is the simulation to initial error?
• How to improve the predictability of the squall line?
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The Squall Line over South China on 23 April 2007
GZ Radar Reflectivity at 0.5 Elevation
Features: fast (17m/s)
long lasting (about 11 h),
and widespread impact.
- trailing stratiform
- transition zone
- V shape stratiform
- bow echo
- backbuilding
- line behind the stratiform
Impact: Widespread heavy rain
(186.3mm,), strong wind
(30m/s at Huadu) and
thunderstorm (hail at
Qujiang)
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Synoptic overview: Guangdong squall line on 23 April 2007
23/00Z e/Wind/V 850hPa
23/06Z e/Wind/V 850hPa
23/12Z e/Wind/V 850hPa
23/18Z e/Wind/V 850hPa
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Model and Data
Model: WRFV3
Vertical: 35 levels
Physics: Grell, YSU, WSM-6
Initialized at 23/1200UTC for a
24-h run
D1 40.5km
D2 13.5km
D3 4.5km
Initial and boundary conditions:
NCEP/FNL (1 1 )
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Control experiment
Isochrones
(Meng et al. 2012 JAS)
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Sensitivity to model error:physical parameterization scheme
CNTL
KFcum
THmps
CNTL: Grell,
YSU,
WSM-6
NTM
MRFpbl
EHSlws
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Sensitivity to model error: grid size & cumulus scheme
R20_None
R13.5_None
R10_None
R5_None
R20_Grell
R13.5_Grell
R10_Grell
R5_Grell
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Sensitivity to initial error
Setup of ensemble forecast
Initial ensemble :WRF-3DVar
Ensemble size:40
STD: 1K for T, 2m/s for u and v, 0.5g/kg for qv
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The CNTL ensemble, reflectivity valid at 24/00z
60% 15%
Grouping into
A: the center of the 40 dBZ
band is 200 km within the
observed position
25%
B: Got a squall line with a
larger location error
C: No squall line formed
during the whole forecast
period.
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Evolution of ensemble spread in horizontal
Vertically mean STD of T(contour,the shaded: dBZ)
1400UTC_23
1700UTC_23
2100UTC_23
2300UTC_23
0000UTC_24
0400UTC_24
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Evolution of ensemble spread in vertical
U
T
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How can the improvement in the
initial error affect the simulation?
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Splitting experiments
∆={initialgood – initialbad}/10
All_1= initialbad+ ∆*1
All_2= initialbad+ ∆*2
..
..
..
..
..
..
..
..
..
All_9= initialbad+ ∆*9
(Melhauser & Zhang 2012)
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Sensitivity to the initial error: all variables
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Sensitivity to the initial error: all variables
2003.6.9 event in the U.S.
(Melhauser et al. 2012)
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Sensitivity to the initial error: different variables
Only UV
Only T
(g)
(h)
(i)
Only Qv
(j)
(k)
(l)
All but Qv
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Sensitivity to the initial error: different pairs
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How to improve the predictability?
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The performance of the Ensemble forecast
Single scheme
Multi-scheme
A: the exact squall line
15%
15%
B: squall line with large location
60%
70%
C: no squall line
25 %
15%
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Summary
Model error apparently affect the predictability of the squall line
• Physical parameterization
• Grid size
• Cumulus parameterization
Initial error apparently affect the predictability of the squall line
• Linear impact
• The moisture condition and moist processes played an
important role
Adding physical perturbation helped to improve the predictability
of the squall line
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Error in the formation and lasting time
A
All
B
A
All
All
B
What may have caused the differences?
Precipitable water at 23/21z (Shaded), Terrain is contoured
Good member
Bad member
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Outline
Background
Model and data
Sensitivity to model error
Sensitivity to initial error
Summary
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Ensemble spread of Multi vs. Single
U(m/s), 24/00z
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