NEWxSFC - Interim Summary…as of 18 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
2,1,1	1	donsutherland1	Chief	70	69	-1.082	53%	2	65.9	11.3	-0.475	37%	6	23.34	-1.201	35%	2	1.31	-1.129	35%	2	74.5%	0.969	33%	4	donsutherland1
-,-,-	2	anthony	Rookie	67	123	-0.7193	34.1%	5	65.6	11.7	-0.835	51%	5	33.56	-0.784	23%	5	2.03	-0.353	9%	8	62.1%	0.296	17%	9	anthony
5,2,2	3	shanabe	Senior	66	87	-0.7189	33.7%	5	62.9	17.5	0.031	0%	9	26.96	-0.720	23%	5	1.58	-0.691	20%	5	71.7%	0.726	30%	5	shanabe
10,6,3	4	Donald Rosenfeld	Senior	84	92	-0.655	30%	5	73.4	14.6	-0.361	-5%	4	33.32	-0.435	10%	5	1.56	-0.680	16%	5	66.1%	0.186	6%	6	Donald Rosenfeld
-,-,-	5	Raven	Senior	79	135	-0.432	20%	6	77.2	23.5	0.218	-18%	9	39.35	-0.217	6%	7	1.96	-0.271	7%	8	64.2%	0.279	4%	7	Raven
8,-,-	6	Mitchel Volk	Senior	81	163	-0.388	15%	7	87.3	17.0	-0.289	12%	6	41.20	-0.459	11%	7	2.05	-0.409	9%	7	66.9%	0.396	16%	7	Mitchel Volk
4,4,4	7	herb @maws	Senior	71	119	-0.278	13%	8	68.4	14.1	-0.254	18%	8	33.79	-0.217	5%	8	1.88	-0.253	8%	8	58.8%	0.022	-5%	10	herb @maws
-,-,-	8	Newa	Journeyman	75	156	-0.187	8%	8	76.0	21.8	-0.005	-3%	9	41.46	-0.020	1%	8	2.21	0.141	-6%	10	66.4%	0.352	5%	6	Newa
6,5,5	9	TQ	Senior	65	127	-0.126	7%	8	66.8	15.9	0.155	-11%	9	30.08	-0.384	11%	6	1.85	-0.134	6%	7	73.0%	0.168	2%	8	TQ
12,7,8	10	NYNJPAWeather	Rookie	73	204	0.134	-3%	10	92.2	17.3	-0.097	16%	10	45.29	0.140	-1%	9	2.44	0.184	-3%	10	56.4%	-0.306	-12%	11	NYNJPAWeather
11,8,6	11	ilibov	Senior	85	183	0.239	-10%	9	66.8	23.3	0.403	-28%	10	42.75	0.435	-12%	10	2.00	0.078	0%	9	72.1%	0.081	2%	8	ilibov
13,9,7	12	Don Rooney	Senior	69	158	0.452	-22%	11	82.3	23.8	0.636	-46%	12	37.59	0.349	-10%	10	2.24	0.348	-11%	11	65.3%	0.229	14%	7	Don Rooney
15,10,9	13	jackzig	Senior	79	219	0.493	-21%	12	86.8	27.0	0.369	-15%	11	51.30	0.727	-20%	13	2.57	0.612	-15%	13	39.5%	-0.967	-28%	12	jackzig
14,11,10	14	weatherT	Rookie	70	174	0.707	-35%	13	55.6	30.4	0.568	-32%	11	43.25	0.735	-23%	12	2.44	1.197	-41%	12	60.1%	0.036	-5%	9	weatherT

There have been six (6) snowstorm forecasting Contests to date. 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.