NEWxSFC - Interim Summary…as of 09-FEB-13					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	dryslot	Intern	46	342	-0.913	55%	1	174	17	-0.609	59%	6	55.4	-0.926	31%	4	2.4	-1.000	32%	3	79%	1.331	19%	3	dryslot
	2	donsutherland1	Chief	43	123	-0.794	44%	3	57	17	-0.231	17%	7	34.7	-0.947	28%	3	1.6	-0.907	25%	3	67%	0.806	38%	4	donsutherland1
-	3	Donald Rosenfeld	Senior	44	127	-0.687	40%	4	73	1	-1.106	95%	1	37.1	-0.732	23%	4	1.7	-0.706	21%	4	58%	0.588	12%	7	Donald Rosenfeld
	4	herb @maws	Senior	44	500	-0.681	41%	4	168	17	-0.446	43%	5	73.0	-0.548	18%	5	3.3	-0.499	16%	5	74%	0.791	12%	5	herb @maws
	5	iralibov	Senior	48	417	-0.560	32%	5	196	18	-0.265	29%	5	74.4	-0.190	7%	6	3.1	-0.340	11%	5	64%	0.661	16%	5	iralibov
-	6	Roger Smith	Senior	45	491	-0.461	27%	6	208	24	-0.556	54%	6	74.5	-0.315	10%	7	3.3	-0.321	10%	7	69%	0.181	2%	5	Roger Smith
-	7	snocat918	Rookie	45	667	-0.396	21%	7	162	16	-0.734	62%	4	79.0	-0.382	11%	7	3.6	-0.317	8%	7	56%	-0.112	6%	8	snocat918
	8	Mitchel Volk	Senior	43	715	-0.180	11%	8	186	19	-0.304	29%	5	84.5	-0.124	4%	9	3.8	0.027	-1%	10	69%	0.269	5%	6	Mitchel Volk
-	9	Shillelagh	Senior	45	680	-0.178	11%	9	144	42	-0.068	7%	10	73.6	-0.497	17%	5	3.2	-0.504	16%	6	68%	0.127	2%	7	Shillelagh
-	10	snowman	Senior	46	744	-0.140	9%	10	197	17	-0.597	58%	6	91.9	0.121	-4%	11	4.0	0.078	-2%	10	57%	-1.036	-15%	13	snowman
-	11	weatherT	Senior	45	733	-0.049	3%	10	203	38	0.076	-7%	11	91.4	0.091	-3%	10	3.9	0.110	-3%	11	60%	-0.730	-11%	11	weatherT
-	12	TQ	Senior	44	271	0.266	-19%	9	78	31	0.548	-51%	10	49.0	-0.024	-2%	7	2.2	0.005	-3%	7	52%	-0.208	3%	8	TQ
-	13	quagmireweathercentral	Rookie	48	1974	2.355	-133%	13	304	126	2.093	-189%	13	162.2	2.487	-77%	14	6.8	2.448	-71%	14	35%	-1.467	-41%	13	quagmireweathercentral

There have been three (3) snowstorm forecasting Contests…as of 09-FEB-13. 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.