Winter '12 / '13
NEWxSFC  Interim Standings…after Storm #4 
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 
3 
1 
Brad
Yehl 
Journeyman 
68 
72 
0.983 
64% 
2 
56 
6 
0.820 
69% 
3 
24.7 
1.179 
43% 
2 
1.1 
1.327 
43% 
1 
79% 
1.178 
38% 
2 
Brad Yehl 
5 
2 
herb
@maws 
Senior 
66 
338 
0.774 
53% 
3 
126 
15 
0.385 
35% 
5 
53.3 
0.644 
25% 
4 
2.4 
0.629 
22% 
4 
81% 
0.848 
17% 
4 
herb@maws 
1 
3 
dryslot 
Intern 
65 
241 
0.753 
48% 
2 
130 
15 
0.466 
44% 
6 
42.8 
0.783 
28% 
4 
1.9 
0.719 
23% 
4 
82% 
1.119 
19% 
3 
dryslot 
2 
4 
donsutherland1 
Chief 
66 
402 
0.665 
38% 
4 
120 
26 
0.207 
16% 
8 
61.8 
0.699 
21% 
5 
2.8 
0.660 
19% 
5 
70% 
0.762 
29% 
4 
donsutherland1 
4 
5 
Donald
Rosenfeld 
Senior 
67 
96 
0.639 
41% 
4 
61 
4 
0.868 
73% 
2 
29.4 
0.763 
28% 
3 
1.3 
0.784 
27% 
3 
66% 
0.539 
12% 
6 
Donald Rosenfeld 
9 
6 
snocat918 
Rookie 
67 
462 
0.328 
19% 
7 
122 
15 
0.487 
41% 
5 
59.0 
0.363 
12% 
6 
2.7 
0.302 
9% 
6 
69% 
0.239 
13% 
6 
snocat918 
12 
7 
weatherT 
Senior 
69 
499 
0.233 
18% 
7 
145 
25 
0.366 
27% 
8 
67.4 
0.039 
3% 
9 
2.9 
0.072 
3% 
8 
61% 
0.630 
11% 
10 
weatherT 
6 
8 
iralibov 
Senior 
73 
309 
0.131 
2% 
6 
148 
20 
0.147 
5% 
6 
61.3 
0.292 
14% 
7 
2.6 
0.146 
8% 
6 
58% 
0.013 
1% 
6 
iralibov 
7 
9 
Roger
Smith 
Senior 
70 
355 
0.129 
4% 
6 
157 
24 
0.020 
6% 
7 
59.7 
0.050 
5% 
7 
2.6 
0.007 
1% 
7 
69% 
0.050 
1% 
5 
Roger Smith 
13 
10 
TQ 
Senior 
68 
878 
0.557 
35% 
10 
102 
66 
0.915 
87% 
11 
81.8 
0.173 
7% 
9 
3.5 
0.170 
7% 
9 
55% 
0.411 
2% 
9 
TQ 
14 
11 
quagmireweathercentral 
Rookie 
67 
1338 
1.612 
92% 
11 
213 
84 
1.005 
96% 
9 
116.6 
1.762 
56% 
11 
5.0 
1.954 
60% 
12 
33% 
1.665 
48% 
12 
quagmireweathercentral 
There have been four (4)
snowstorm forecasting Contests…as of 17FEB13. Under the ‘twothirds’ rule…forecasters who have entered at least
three (3) forecasts are included in this interim summary.
To qualify for ranking in
the Interim and final ‘EndofSeason’ standings…a forecaster must enter at
least twothirds of all Contests. If a forecaster has made more than
enough forecasts to qualify for ranking…only the lowest SUMSQ Zscores
necessary to qualify are used in the computing the average. IOW…if you
made nine forecasts…only your six best SUMSQ Zscores are used to evaluate your
seasontodate 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 ‘Endof Season’
ranking and 2) encourage forecasters to take on difficult and/or lateseason
storms without fear about how a bad forecast might degrade their overall
'seasontodate' 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 sumtotal snowfall for all stations and the observed
sumtotal 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 overforecast or underforecast.
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.