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A Preliminary Evaluation of Heavy Snow Conceptual Models for East Vancouver Island (EVI)
Rodger WU and Brad SnyderPacific Storm Prediction Centre, EC, Vancouver, BC, Canada
Ruping Mo and Paul JoeNational Lab for Coastal and Mountain Meteorology, EC,
Vancouver, BC, Canada
Vancouver
British Columbia
Strait of Georgia
Pacific Ocean
East Vancouver Island (EVI)
X
X
Fraser Valley
1. Large N-S region
2. Most complex terrain
3. Subject to outflow
4. Dense population
Vancouver
Coast Mountains
Back ground picture was taken Dec 15, 2008 at Nanaimo where 38cm snow was reported (courtesy of randsco.com)
81
7047
44
44
43
29
38
33
35
81 snow events (>=5 cm/24 hrs) in the past 10 year
15 extreme snow events (>=30 cm/24 hrs) in the same period
EVI is prone to heavy snowfalls (Jan 2000-Dec 2009)
22
Forecast challengesTypes of warning events over East Vancouver Island
for 2000-2009 (event, percentage)
49, 55%27, 31%
6, 7%6, 7%
Hit
Miss
FALSE
Unverif iable
Average snow amount underforecast over East Vancouver Island for 2000-2009
7.9
25.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
All event Extreme event
Snow event
Under
fore
cast
am
ount (C
M) Warning
criteria
Four principal patterns indentified (total 81 events from Jan 2000-Dec 2009)
EVI snow events per weather pattern
17
14 14
2
5
1
26
7
0
5
10
15
20
25
30
Surface Low Warm Front Trowal Unstable Airmass
Werather pattern
Sn
ow
eve
nt
Regular (>5cm) Extreme (>30cm)
1. Surface low2. Warm front3. Trowal4. Unstable airmass
Average snow amountl underforecast per weather pattern
9
6
1
22
0
5
10
15
20
25
Surface Low Warm Front Trow al Unstable Airmass
Weather Pattern
Sn
ow
Am
ou
nt
(CM
)
The Surface Low: Extreme snow producer &forecasting trouble maker
10 Key ingredients investigated and four conceptual models developed
1. Sources of moisture
2. Arctic front
3. Surface low track
4. Georgia Strait convergence zone
5. Low-level flows
6. Vertical wind profiles
7. Stability
8. Conveyor belts
9. Banded structures
10. Upper level features
• Combined with pattern analyses, four conceptual models were developed to help forecasters understand physical processes and make better warning decisions:
1. The surface low
2. The warm front
3. The trowal
4. The unstable airmass
L
Arctic air
500hpa
Surface700hpa
Outflows
500hpa troughArctic front
250h
pa je
tSurface Low
GST C. Z.
Enhanced vertical motion
Moisture transfer
1008
528
Unstable Open cells
Moi
stur
e pi
ck-u
p
Destabi-
lization
Heavy snow
A B
A cross section along line AB
Pac
ific
Van
couv
er
Isla
nd R
ange
s
Str
ait o
f G
eorg
ia
Fra
ser
Val
ley
Enhanced vertical motion
Moist, unstable flows
Dry, cold outflows
Preliminary evaluation of conceptual models using 10 snow events from winter 2010-11
All event Extreme All event Extreme
Surface low 4 2 14 19Unstable airmass 3 - 14 -Warm front 2 - 3 -Trowal 1 1 18 18Total 10 3 10 19
Snow event Under-forecast (CM)Pattern
For each event
• Identify pattern and determine relevant conceptual model
• Identify key ingredients depicted in the model
• Determine if these ingredients play roles on heavy snow over EVI
Four key ingredients
Key ingredient ConceptualModel Says
1. Arctic front Moderate
2. Low centre West of VI - intensity below 1000
hPa
3. GSTCZ* Srn Sxns - intensity 35-45 knot
4. Instability - Inversion top 4KM or higher
Case 1
20-Nov-10(29 cm)
Moderate
West of VI998 hPa
Srn Sxns35 knots
7 km
Case 2
9-Jan-11(18 cm)
Weak
West of VI1018 hPa
Srn Sxns20 knots
5 km
Case 3
22-Nov-10(10 cm)
Weak
West of VI1007 hPa
Srn Sxns25 knots
2 km
* Georgia strait convergence zone
Summary
• Complex terrain enhanced heavy snowfalls over EVI pose great forecasting challenges to meteorologists.
• Based on 81 snow events over EVI during the years of 2000 – 2009, four principal weather patterns were identified and 10 key ingredients were investigated.
• Four conceptual models were developed aiming at assisting meteorologists to do better warning decisions.
• A preliminary evaluation of these conceptual models was performed by using 10 snow events that occurred during the winter of 2010 – 2011. The results indicate that these conceptual models have the capacity to provide a reliable forecasting guidance to the meteorologists.