NEWxSFC - Interim Summary…as of 07 JAN 2009					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
2	1	donsutherland1	Chief	49	60	-1.082	53%	2	61.6	8.1	-0.546	44%	5	19.67	-1.187	35%	2	1.21	-1.095	35%	2	79.0%	0.815	16%	4	donsutherland1
5	2	shanabe	Senior	46	78	-0.659	30%	5	55.8	14.7	0.010	2%	8	23.85	-0.591	20%	5	1.51	-0.598	18%	5	74.5%	0.456	10%	6	shanabe
3	3	dmcguriman	Senior	53	92	-0.492	24%	6	73.6	14.9	-0.006	3%	9	28.30	-0.291	7%	7	1.57	-0.514	17%	6	77.2%	0.633	13%	5	dmcguriman
4	4	herb @maws	Senior	50	96	-0.319	14%	7	66.4	13.3	-0.145	13%	8	27.38	-0.299	7%	7	1.66	-0.363	12%	7	71.2%	0.226	5%	8	herb @maws
6	5	TQ	Senior	51	135	-0.266	15%	7	74.3	16.3	0.256	-19%	9	33.40	-0.328	9%	6	1.99	0.024	2%	8	76.4%	0.271	5%	7	TQ
10	6	Donald Rosenfeld	Senior	60	138	-0.132	7%	8	96.6	15.2	0.346	-28%	5	39.33	0.264	-8%	9	1.91	-0.080	3%	7	70.4%	-0.179	-4%	8	Donald Rosenfeld
12	7	NYNJPAWeather	Rookie	52	174	-0.118	10%	8	89.0	8.5	-0.582	48%	7	38.18	-0.133	8%	7	2.20	-0.100	5%	8	69.0%	-0.159	-2%	9	NYNJPAWeather
11	8	ilibov	Senior	63	181	0.288	-12%	9	72.5	19.8	0.441	-34%	10	42.19	0.562	-16%	10	2.00	0.031	1%	9	73.3%	0.019	0%	8	ilibov
13	9	Don Rooney	Senior	48	165	0.826	-41%	13	85.5	26.9	1.082	-74%	14	36.05	0.701	-20%	12	2.32	0.691	-21%	12	71.4%	0.071	5%	8	Don Rooney
15	10	jackzig	Senior	56	270	1.203	-60%	12	76.3	11.3	-0.405	30%	7	56.58	1.489	-45%	14	2.95	1.270	-36%	13	35.3%	-2.248	-49%	14	jackzig
14	11	weatherT	Rookie	42	211	1.239	-55%	14	49.7	34.5	0.941	-103%	11	42.52	0.981	-30%	13	2.97	1.792	-62%	13	66.8%	-0.287	-2%	10	weatherT

There have been four (4) snowstorm forecasting Contests to date. Under the ‘two-thirds’ 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 ‘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.