A Process-Oriented Observational Study of

Snowfall Potential in the Central United States

Chad M GravelleSaint Louis University

Charles E GravesSaint Louis University

Scott M RochetteState University of New York College at Brockport

Annual Missouri Academy of Science MeetingMissouri Western State University, 21 April 2007

Outline

• Introduction

• Description of Dataset

• Methodology/Case Selection

• Summary of Results

• Conclusions

Introduction

• Even with advances in numerical and ensemble model guidance, forecasting the axis and spatial extent of heavy snowfall still contains a large amount of uncertainty.

• Gouree and Younkin 1966

• Browne and Younkin 1970• Probabilistic forecasts are used by SPC and HPC to show the uncertainty with an event.

Dataset

• Daily 24-h snowfall amounts were obtained from the National Climate Data Center (NCDC) Cooperative Summary of the Day (COOP) collection for the period of November 1998 and March 2003.

• North American Regional Reanalysis (NARR)– 3-h dataset with 32-km resolution on 29 pressure

levels

• NCDC publications of the Daily Weather Maps.

• COOP and NARR datasets were displayed using the General Meteorological Package (GEMPAK) software.

Methodology

Methodology15 Jan 2003

16 Jan 2003

17 Jan 2003

72-h Event

Methodology

Methodology

• COOP event data for each station was objectively analyzed using a Barnes objective analysis.– 271 x 171 grid ~ 10 km grid spacing

• Parameters for the objective analysis were chosen such that stations within 35 km had the most influence on each grid point.

• Finally, a 9-pt smoother was applied to all fields before generating graphics.

Methodology

Case Categories

• Total events, n = 82

• Banded (upper left), n = 42• Widespread (upper right), n = 15• Other/Multi-banded/Undefinable

(lower left), n = 25

Banded Categories

• Total events, n = 42

• NW/SE (upper left), n = 11• W/E within 30° of major axis

(upper right), n = 17• SW/NE (lower left), n = 14

Length/Width Example

Banded Length Statistics

Days Length of > 2" Band Length of > 4" Band Length of > 6" Band Length of > 12" BandNW/ SE AVG 2.3 1389.5 987.4 834.4 none

W/ E AVG 2.4 1308.8 1139.4 892.2 471.3SW/ NE AVG 2.4 1358.4 1121.4 1179.5 820.3

NW/ SE MED 1597.0 885.5 985.0 noneW/ E MED 1353.0 1072.0 940.5 455.0

SW/ NE MED 1323.5 1232.0 1199.5 780.0

NW/ SE SD 460.7 468.7 376.2 noneW/ E SD 371.8 471.0 304.9 35.4

SW/ NE SD 312.3 406.7 271.0 373.6

NW/ SE NORM SD 138.9 165.7 168.2 noneW/ E NORM SD 90.2 114.2 81.5 20.5

SW/ NE NORM SD 83.5 112.8 85.7 152.5

Banded Width Statistics

Width of > 2" Band Width of > 4" Band Width of > 6" Band Width of > 12" BandNW/ SE AVG 220.96 149.79 93.55 none

W/ E AVG 256.43 158.56 119.82 102.09SW/ NE AVG 283.83 216.50 165.86 82.07

NW/ SE MED 206.25 121.37 79.43 noneW/ E MED 271.70 150.57 122.70 114.20

SW/ NE MED 232.94 217.43 170.94 83.04

NW/ SE SD 122.82 84.94 44.61 noneW/ E SD 81.82 70.81 55.11 41.55

SW/ NE SD 148.83 120.06 91.24 25.19

NW/ SE NORM SD 37.03 30.03 19.95 noneW/ E NORM SD 19.84 17.17 14.73 23.99

SW/ NE NORM SD 39.78 33.30 28.85 10.29

A Look Ahead...

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A Look Ahead...

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A Look Ahead...

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A Look Ahead...

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Conclusions

• Lengths at lower snowfall thresholds often exceed the domain.

• SW/NE bands are the widest, followed by E/W and NW/SE.

• Early indications suggest that the differences in width of the three orientations are not the results of sampling, but the physical processes associated with snowfall generation.

Future Research

• Using percentiles to provide forecasters with probabilistic guidance on the width of snowfall bands.

• Examine the orientation and strength of processes to snowfall potential.

• Expanding the period of record to include more years.

• Use the identified events as analogs of future events.

Find this, and other presentations online at:

www.eas.slu.edu/CIPS/

Questions or Comments?

gravelle@eas.slu.edu