Winter '12 / '13 NEWxSFC - Interim Standings…after Storm #6

AVG SUMSQ

AVG STP

AVG Total Absolute

AVG Absolute

Mean RSQ

 

Previous Ranks

Rank

Forecaster

Class

Total STN 4casts

Error (")

Error Z

% MPRV over AVG

Rank

4cast (")

Error

Error Z

% MPRV over AVG

Rank

Error (")

Error Z

% MPRV over AVG

Rank

Error (")

Error Z

%MPRV over AVG

Rank

RSQ

RSQ Z

% MPRV over AVG

Rank

Forecaster

1,3,1

1

dryslot

Intern

95

301

-0.961

49%

2

127

21

-0.356

39%

6

51.5

-1.096

28%

4

2.2

-1.086

28%

4

80%

1.451

104%

2

dryslot

3,1,2

2

Brad Yehl

Journeyman

92

269

-0.873

56%

3

102

17

-0.634

53%

5

44.4

-1.012

36%

3

1.9

-1.147

37%

2

79%

1.112

31%

3

Brad Yehl

2,4,3

3

donsutherland1

Chief

93

208

-0.828

43%

3

80

14

-0.602

38%

5

48.0

-0.697

20%

4

2.0

-0.700

19%

4

63%

0.649

43%

4

donsutherland1

4,4,4

4

Donald Rosenfeld

Senior

93

219

-0.736

41%

3

85

6

-1.104

77%

2

46.4

-0.788

23%

3

2.0

-0.741

22%

3

57%

0.477

16%

5

Donald Rosenfeld

5,2,5

5

herb @maws

Senior

91

271

-0.688

50%

4

117

14

-0.390

36%

6

47.7

-0.583

22%

5

2.1

-0.586

21%

4

82%

0.694

14%

5

herb @maws

5,8,-

6

iralibov

Senior

98

245

-0.232

15%

5

135

17

-0.043

10%

6

53.1

0.074

-6%

6

2.2

-0.048

0%

6

65%

0.086

0%

6

iralibov

8,5,8

7

snocat918

Rookie

92

380

-0.210

11%

8

120

13

-0.521

45%

5

54.0

-0.209

7%

7

2.4

-0.187

5%

7

72%

0.187

10%

7

snocat918

7,9,6

8

Roger Smith

Senior

97

440

-0.193

6%

6

135

24

-0.199

14%

6

65.5

-0.214

1%

6

2.7

-0.321

4%

6

53%

-0.066

-14%

6

Roger Smith

12,7,7

9

weatherT

Senior

95

595

-0.040

10%

8

123

29

-0.254

19%

7

74.7

-0.073

3%

8

3.1

-0.134

4%

8

46%

-0.659

-33%

11

weatherT

8,-,-

10

Mitchel Volk

Senior

91

619

0.091

2%

9

149

35

0.506

-24%

9

82.8

0.350

-5%

10

3.6

0.462

-9%

11

59%

0.130

-6%

6

Mitchel Volk

10,-,-

11

snowman

Senior

96

643

0.114

9%

9

130

26

-0.069

22%

8

79.9

0.025

2%

8

3.4

0.001

3%

8

51%

-0.616

-30%

10

snowman

13,10,9

12

TQ

Senior

94

364

0.166

-11%

8

91

29

0.266

-27%

8

63.4

0.311

-7%

9

2.7

0.306

-8%

9

48%

-0.199

-11%

8

TQ

14,11,10

13

quagmireweathercentral

Rookie

92

396

0.813

-36%

11

95

22

-0.241

6%

6

65.7

1.016

-26%

11

2.8

1.167

-31%

11

35%

-0.789

-47%

8

quagmireweathercentral

 

 

There have been six (6) snowstorm forecasting Contests…as of 19-MAR-13.  Under the ‘two-thirds’ rule…forecasters who have entered at least four (4) forecasts are included in this interim summary.

 

To qualify for ranking in the Interim and final ‘End-of-Season’ standings…a forecaster must enter at least two-thirds of all Contests.  If a forecaster has made more than enough forecasts to qualify for ranking…only the lowest SUMSQ Z-scores necessary to qualify are used in the computing the average.  IOW…if you made nine forecasts…only your six best SUMSQ Z-scores are used to evaluate your season-to-date performance.  You can think of it as dropping the worse quiz score before your final grade is determined.  The reason we have this rule is to 1) make it possible to miss entering a forecast or two throughout the season and still be eligible for Interim and ‘End-of Season’ ranking and 2) encourage forecasters to take on difficult and/or late-season storms without fear about how a bad forecast might degrade their overall 'season-to-date' performance score(s).

 

The mean normalized ‘SUMSQ error’ is the Contest/s primary measure of forecaster performance.  This metric measures how well the forecaster/s expected snowfall 'distribution and magnitude' for _all_ forecast stations captured the 'distribution and magnitude' of _all_ observed snowfall amounts.  A forecaster with a lower average SUMSQ Z Score has made more skillful forecasts than a forecaster with higher average SUMSQ Z Score.

 

The 'Storm Total Precipitation error’ statistic is the absolute arithmetic difference between a forecaster/s sum-total snowfall for all stations and the observed sum-total snowfall.  This metric…by itself…is not a meaningful measure of skill…but can provide additional insight of forecaster bias.

 

The 'Total Absolute error' statistic is the average of your forecast errors regardless of whether you over-forecast or under-forecast.  This metric measures the magnitude of your errors.

 

The 'Average Absolute Error' is the forecaster/s ‘Total Absolute Error’ divided by the number of stations where snow was forecast or observed.

 

The ‘RSQ error’ statistic is a measure of the how well the forecast captured the variability of the observed snowfall.  Combined with the SUMSQ error statistic…RSQ provides added information about how strong the forecaster/s ‘model’ performed.