NEWxSFC - Interim Summary…as of 21 DEC 2008					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	emoran	Senior	40	103	-1.096	50%	2	105.4	3.4	-0.933	78%	3	28.88	-1.308	37%	2	1.46	-1.412	39%	2	84.4%	1.053	18%	2	emoran
-	2	donsutherland1	Chief	38	69	-1.069	51%	2	75.1	9.7	-0.381	31%	5	22.80	-1.135	29%	2	1.20	-1.150	37%	2	83.2%	1.092	20%	2	donsutherland1
-	3	dmcguriman	Senior	36	93	-0.714	35%	5	85.0	11.7	-0.180	17%	8	29.83	-0.446	11%	6	1.62	-0.510	17%	6	78.6%	0.720	13%	5	dmcguriman
-	4	herb @maws	Senior	35	99	-0.475	21%	7	75.1	10.1	-0.329	28%	6	27.98	-0.511	13%	6	1.62	-0.510	17%	6	72.9%	0.280	5%	8	herb @maws
-	5	shanabe	Senior	33	98	-0.389	16%	7	68.2	21.2	0.585	-42%	12	29.08	-0.241	6%	7	1.75	-0.243	5%	7	75.7%	0.517	10%	5	shanabe
-	6	TQ	Senior	34	89	-0.346	20%	6	61.8	14.2	0.271	-19%	8	26.73	-0.350	9%	5	1.62	0.031	3%	7	82.7%	0.435	8%	6	TQ
-	7	wxduff	Intern	35	153	-0.282	22%	7	77.1	3.9	-1.184	75%	4	35.65	-0.534	12%	8	1.92	-0.229	15%	8	68.8%	-0.115	-2%	9	wxduff
-	8	Mitchel Volk	Senior	38	184	-0.173	4%	7	100.5	10.4	-0.088	12%	5	40.58	-0.378	9%	7	2.14	-0.293	8%	6	74.2%	0.203	4%	8	Mitchel Volk
-	9	apisces1	Journeyman	38	186	-0.172	6%	8	92.5	16.3	0.043	-6%	10	45.48	-0.002	0%	9	2.39	0.038	-1%	8	75.3%	0.291	6%	8	apisces1
-	10	Donald Rosenfeld	Senior	40	92	-0.155	9%	7	83.8	21.6	0.996	-84%	7	32.80	0.320	-10%	8	1.56	-0.037	2%	7	75.4%	-0.106	-2%	7	Donald Rosenfeld
-	11	ilibov	Senior	43	122	0.106	-4%	8	67.0	11.5	0.037	5%	7	34.88	0.596	-17%	9	1.58	-0.020	3%	8	80.9%	0.297	5%	6	ilibov
-	12	NYNJPAWeather	Rookie	36	239	0.354	-11%	11	112.8	6.7	-0.695	57%	6	48.63	0.230	-4%	9	2.76	0.461	-12%	11	66.3%	-0.466	-9%	11	NYNJPAWeather
-	13	Don Rooney	Senior	36	190	0.981	-48%	13	99.1	25.8	1.160	-75%	14	38.88	0.757	-19%	11	2.22	0.391	-11%	11	65.0%	-0.374	-7%	11	Don Rooney
-	14	weatherT	Rookie	23	255	1.486	-65%	14	40.9	32.7	0.614	-92%	10	44.78	0.757	-20%	12	3.46	2.504	-87%	15	59.6%	-0.829	-15%	13	weatherT
-	15	jackzig	Senior	41	355	1.716	-85%	13	98.0	15.1	-0.113	6%	9	69.13	1.878	-54%	14	3.37	1.634	-46%	14	48.9%	-1.900	-30%	13	jackzig

There have been three (3) snowstorm forecasting Contests to date. Under the ‘two-thirds’ rule…forecasters who have entered at least two (2) 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.