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

20 Nov 2010 case: - arctic front & surface low

20 Nov 2010 case: - the instability

CB top 7 km

Echo top 6 km

KUIL X

20 Nov 2010 case: - GST convergence

Shawnigan Lake

29 cm

2 cm

5 cm

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.

Thank you!

Questions?