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TRANSCRIPT
William Lamberson The Pennsylvania State University National Climatic Data Center
Mentor: Mike Squires
8/2/2011 1
Background: What is RSI? Regional Snowfall Index
Evolved from NESIS (NorthEast Snowfall Impact Scale).
Region specific scale that puts snowstorm and their societal impacts into a historical perspective.
For each region impacted by a storm, an RSI number is calculated based on the amount of snow that falls and the amount of people that experiences the snow.
RSI scores are broken down into six categories.
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Background: The RSI Scale
Category RSI Values Approximate Percentage of
Storms
Description
0 0 – 1 54 % -‐
1 1 – 3 25 % “Notable”
2 3 – 6 13 % “Significant”
3 6 – 10 5 % “Major”
4 10 – 18 2 % “Crippling”
5 > 18 1 % “Extreme”
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Background: What are the Regions?
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Background: How is it Calculated?
€
RSI =AT
AT
+PT
PT
⎛
⎝
⎜ ⎜ ⎜
⎞
⎠
⎟ ⎟ ⎟
⎡
⎣
⎢ ⎢ ⎢
⎤
⎦
⎥ ⎥ ⎥ T=T1
T 4
∑ = AT1
A T1+
PT1
P T1
⎛
⎝ ⎜
⎞
⎠ ⎟ + +
AT4
A T4
+PT4
P T4
⎛
⎝ ⎜
⎞
⎠ ⎟
T = regional specific snowfall thresholdsAT = area affected by snowfall greater than threshold TAT = mean area affected by snowfall greater than threshold TPT = population affected by snowfall greater than threshold TPT = mean population affected by snowfall greater than threshold T
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Region Specific Snowfall Thresholds
Region T1 T2 T3 T4
Central 3 6 12 18
East North Central 3 7 14 21
Northeast 4 10 20 30
South 2 5 10 15
Southeast 2 5 10 15
West North Central 3 7 14 21
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Sample RSI Calcula@on: March 1993
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Mo@va@on Would help convey the impacts of an impending snowstorm to
transportation officials, emergency managers , the media, and the general public.
People ask for it.
“Given the current difficulties in forecasting precipitation type and snowfall amounts and areal distribution associated with these events, we do not yet recommend the use of NESIS in a predictive manner” – Kocin and Uccellini, 2004
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Method Use archived model forecasts to create a storm total forecast
map for snowstorms already documented in NCDC’s RSI catalog.
Create snowfall forecasts 48 and 24-‐hours out.
Calculate RSI values for regions impacted by given storm.
Compare predicted RSI values to observed RSI values.
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The Details Used archived NAM forecasts.
Forecasts included 3-‐hour forecasts of surface precipitation amount and type.
Also included vertical profiles of temperature, relative humidity, and vertical velocity, and geopotential height.
Used those four variables and a method developed by Cobb and Waldstreicher to estimate snow to liquid ratio at each grid point.
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The Details Multiplied snow to liquid ratio at a grid point by precipitation
amount to get a 3-‐hour snowfall forecast.
Went through maps by hand to crop out unrelated snowfall.
Summed up 3-‐hour forecasts to get a storm total forecast.
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Storm Selec@on Archived model data only goes back to 2005.
NAM model only forecasts 84 hours out.
Limited to storms in RSI catalog that occurred after 2004 and did not spend more than 36-‐hours in a single region.
Total Storms: 30 (76 total RSI forecasts for each time frame)
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Example Storm
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Region Predicted RSI Observed RSI
WestNorthCentral 20.54 (category 5) 4.73 (category 2)
EastNorthCentral 7.39 (category 3) 1.19 (category 1)
Results: 48 – Hours Out
0 1 2 3 4 5 Tot.
0 12 7 0 0 0 0 19
1 7 11 1 0 0 0 19
2 0 8 6 2 0 0 16
3 0 1 2 2 0 0 5
4 0 3 4 3 0 0 10
5 0 1 2 1 3 0 7
Tot. 19 31 15 8 3 0 76
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• Percent Correct = 0.408
• Heidke Skill Score = 0.211
Observed Category
Fore
cast Categ
ory
Results: 24 – Hours out
0 1 2 3 4 5 Tot.
0 11 3 0 0 0 0 14
1 8 13 0 0 0 0 21
2 0 7 3 0 0 0 10
3 0 5 4 1 0 0 10
4 0 1 6 2 0 0 9
5 0 2 2 5 3 0 12
Tot. 19 31 15 8 3 0 76
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Observed Category
• Percent Correct = 0.368
• Heidke Skill Score = 0.207
Fore
cast Categ
ory
Conclusion Forecasting RSI indexes with NAM output and the Cobb
Waldstreicher method of estimating snow to liquid ratio does not work.
This does not mean the project is over. This method produced a strong bias.
Possible that forecasting RSI with a different method could work.
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Sample Storm Using 10 to 1 Ra@o
Region Predicted RSI Observed RSI
WestNorthCentral 8.97 (category 3) 4.73 (category 2)
EastNorthCentral 2.22 (category 1) 1.19 (category 1)
Future Work Figure out what is causing the high bias (NAM or SLR
estimation).
Retry with new methods, NAM estimates snowfall rate.
Possible partnership with Dr. Miller of UNC-‐Asheville.
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Acknowledgements Mike Squires – Mentor who gave constant guidance and
assistance.
Scott Applequist – Helped with data format and coding issues.
Robert David – Gave valuable input and vastly expanded the number of storms in the RSI catalog so that my project could be possible.
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References Kocin, P. J. and L. W. Uccellini, 2004: A Snowfall Impact Scale
Derived From Northeast Storm Snowfall Distributions. Bull. Amer. Meteor. Soc., 85, 177-‐194
Squires, M. F. and J. H. Lawrimore, 2006: Development of an Operational Snowfall Impact Scale. 22nd IIPS, Atlanta, GA.
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