ASSESSMENT OF THE NEW GREAT LAKES WATERSPOUT FORECAST SYSTEM

Post on 14-Jan-2016

60 views 3 download

Tags:

description

ASSESSMENT OF THE NEW GREAT LAKES WATERSPOUT FORECAST SYSTEM. 2013 Great Lakes Operational Meteorology Workshop Victor Chung & Wade Szilagyi Meteorological Service of Canada April 9, 2013. Introduction – The Great Lakes Waterspout Forecast System (GLWFS). - PowerPoint PPT Presentation

Transcript of ASSESSMENT OF THE NEW GREAT LAKES WATERSPOUT FORECAST SYSTEM

ASSESSMENT OF THE NEW GREAT LAKES WATERSPOUT FORECAST SYSTEM

2013 Great Lakes Operational Meteorology Workshop

Victor Chung & Wade Szilagyi

Meteorological Service of Canada

April 9, 2013

Page 2 – April 21, 2023

Introduction – The Great Lakes Waterspout Forecast System (GLWFS)

• Experimental tool → potential for waterspouts over the Great Lakes

• Output → Szilagyi Waterspout Index (SWI), 0-48 hrs

• Automation of the Waterspout Nomogram

• Used for the first time in 2012 at the OSPC

Page 3 – April 21, 2023

Verification - Methodology

Page 4 – April 21, 2023

Verification Considerations -Seasonal Period

• Peak of waterspout season (August - September) chosen

Page 5 – April 21, 2023

Verification Considerations - Diurnal Time Period

• Daytime only (12-18Z, 18-24Z)

Page 6 – April 21, 2023

Verification Considerations – Areal Coverage

• Half lake resolution (marine forecast sub-zones)

Page 7 – April 21, 2023

Verification - Database

• 13,180 entries

Page 8 – April 21, 2023

Verification - Database

• Date (Aug. – Sept.)

• Time (12-18Z, 18-24Z)

• Location (i.e. western Lake Erie)

• Forecast/Observed/Possible Waterspouts (Yes/No)

• Forecast SWI (<0, 0, 1,…,10)

• SWI Percent Coverage (0, 25, 50, 75,100%)

• Forecast/Observation Location Correlation (Yes/No)

• Lead Time (0-48 hrs)

Page 9 – April 21, 2023

Verification - Results

• N = 1,318

Page 10 – April 21, 2023

Verification - Results

Page 11 – April 21, 2023

Verification - Results

• Average Lead Time = 36 hrs!

• Forecast/Observation Location Correlation = 92%

• Forecast SWI value-waterspout events most frequently associated with forecast SWI

≥ 7

• SWI Percent Coverage-over half of the events occurred when coverage was ≥ 75% →

SWI areal coverage is a factor to consider when forecasting waterspouts

Page 12 – April 21, 2023

Case Study –Waterspout Outbreak (Aug. 9-13, 2012)

Page 13 – April 21, 2023

1500Z, Aug 09, 2012

1604z

1601z

1542zA fewwaterspouts

Page 14 – April 21, 2023

1800Z, Aug 09, 2012

1700z

1710zFunnels and 2 waterspouts

1700zA few waterspouts

1710-1735zMultiplewaterspouts 1926z

Page 15 – April 21, 2023

2100Z, Aug 09, 2012

2145z

2137zMultiplewaterspouts

Page 16 – April 21, 2023

0600Z, Aug 10, 2012

0524z

Page 17 – April 21, 2023

1200Z, Aug 10, 2012

1242-1248z2 waterspouts

Page 18 – April 21, 2023

0000Z, Aug 11, 2012

Early evening2 funnels

Page 19 – April 21, 2023

1200Z, Aug 11, 2012

1330z

Page 20 – April 21, 2023

1500Z, Aug 11, 2012

1630z

Page 21 – April 21, 2023

2100Z, Aug 11, 2012

2000z

Page 22 – April 21, 2023

0000Z, Aug 12, 2012

2325z

Page 23 – April 21, 2023

1200Z, Aug 12, 2012

1510-1525z2+ waterspouts

1506z

Page 24 – April 21, 2023

Conclusion

1.Has a very good lead time

2.Has excellent rare event skill score

3.Has good non-rare event skill scores

The Waterspout Forecast System:

Page 25 – April 21, 2023

Future Work• Include surface convergence (SWI → ESWI)

• Higher resolution model output (horizontal and vertical)

• Distinguish between “Severe Wx” vs “Fair Wx” waterspouts

• Expand to other marine areas: Atlantic/Pacific coasts, globally

• Experimental → Operational

• Investigate use as a landspout forecast tool