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FINAL standings after THREE contest-worthy snow storms
Under the ‘two-thirds’ rule … forecasters who entered at least
TWO forecasts are included in these FINAL standings.
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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 test score before your final grade is
determined.
The reasons we have this rule:
1) makes 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).
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The average 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.
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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.
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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 a forecast’s errors.
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The 'Average Absolute Error' is the forecaster/s ‘Total
Absolute Error’ divided by the number of stations where snow was forecast
or observed.
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The ‘RSQ error’ (R-squared – coefficient of
determination) 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.
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The ‘Skill score’ measures forecaster performance by comparing various
Z-Scores against a standard (NWS ER WFOs). Positive (negative) values
indicate better (worse) performance compared to the standard performance. 0% for NWS does not indicate no skill. GREEN highlights the best score in a
category.