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William Lamberson The Pennsylvania State University National Climatic Data Center Mentor: Mike Squires 8/2/2011 1

WilliamLamberson ThePennsylvaniaStateUniversity ...€¦ · Background:,The,RSIScale, Category. RSI.Values. Approximate. Percentage.of Storms. Description 0 0–1 & 54% & T& 1 1–3&

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Page 1: WilliamLamberson ThePennsylvaniaStateUniversity ...€¦ · Background:,The,RSIScale, Category. RSI.Values. Approximate. Percentage.of Storms. Description 0 0–1 & 54% & T& 1 1–3&

William  Lamberson  The  Pennsylvania  State  University  National  Climatic  Data  Center  

Mentor:  Mike  Squires  

8/2/2011 1

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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)  

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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  

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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  

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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)  

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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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