FINAL standings after FOUR contest-worthy snow storms
Under the ‘two-thirds’ rule … forecasters who have entered at least THREE forecasts are included in these FINAL standings.
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).
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.
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 a forecast’s 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’ (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.
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.