The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. The aforementioned probabilistic forecasts are based on the deterministic accumulation forecasts and are manually edited by the WWD forecaster. The experimental probabilistic forecasts found here on the WPC PWPF page are also based on the deterministic WWD accumulation forecasts, but are generated automatically using an ensemble of model forecasts along with the WWD forecasts. The automatic nature of this product generation allows a much more extensive set of displays of probabilities for snowfall or freezing rain exceeding a number of thresholds.

A multi-model ensemble is utilized to create a distribution of values around the WPC accumulation at each grid point. The typical constituency of this ensemble is as follows:

21 NCEP Short-Range Ensemble Forecast (SREF) members

1 NCEP North American Mesoscale (NAM) 12Z (day) or 00Z (night) operational run
1 NCEP Global Forecast System (GFS) 12Z (day) or 00Z (night) operational run
1 European Center for Medium-Range Weather Forecasts (ECMWF) latest operational run
1 Canadian Model (CMC) latest operational run
1 ECMWF latest ensemble mean
1 NCEP Global Ensemble Forecast System (GEFS) latest ensemble mean (6-h SLRs)
1 GEFS latest ensemble mean (24-h mean SLR)

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28 Total members

SLR refers to the snow-to-liquid ratio, which is a multiplicative factor applied to precipitation accumulated as type snow to compute the snowfall. The 6-h SLR at each grid point is an average of the value obtained using the Roebber et al (2007) neural network algorithm (Rnna) applied to the NAM forecast, the value from the Rnna applied to the GFS forecast, a seasonal climatological value, and 11. The 24-h mean SLR applied to the GEFS is the average of four 6-h SLRs covering the 24-h period. For all other members listed above, the 24-h accumulations are sums of 6-h accumulations, using the 6-h SLR values in the case of snowfall. The precipitation type determination for the NCEP models is the dominant type algorithm (Manikin 2005). Precipitation type for non-NCEP models is determined by applying a simple decision tree algorithm using surface temperature, and temperatures on the 925-hPa, 850-hPa, and 700-hPa mandatory isobaric levels.

A binormal probability distribution (density) function (PDF), which allows skewness, is utilized for the PWPF. The fitting of the binormal distribution is a method of moments approach. The WPC forecast is the mode of the distribution. The placement of the HPC forecast in the ensemble order statistics determines the skewness of the distribution. The variance of the distribution is matched to the variance of the ensemble. The WPC deterministic forecast is included as a 29th member of the ensemble for the computation of the variance. This fit is done at each grid point; so, the probability density function (PDF) varies from grid point to grid point.

The PWPF forecasts provide information in the following format:

Probabilities of exceeding a threshold show filled contour levels of probability that the 24-hour accumulation of winter precipitation will equal or exceed the given threshold. As an example, consider the 6-inch threshold for snowfall. If a point of interest falls within the 40% contour on the probability map, then the chance of snowfall exceeding 6 inches is 40% or greater. As the threshold values increase, the probabilities of exceeding them decrease.

References

Manikin, G. S., 2005: An overview of precipitation type forecasting using NAM and SREF data. Preprints, 21st Conf. on Wea. Analysis & Forecasting / 17th Conf. on Numerical Weather Prediction, Washington, DC, Amer. Meteor. Soc., 8A.6.

Roebber, P. J., M. R. Butt, S. J. Reinke, T. J. Grafenauer, 2007: Real-time forecasting of snowfall using a neural network. Wea. Forecasting, 22, 676-684.