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
winter weather forecasts
. These probabilistic forecasts are computed based on
the deterministic accumulation forecasts combined with ensemble information (see below).
Prior to the 2013-14 season, the probabilistic forecasts were manually edited by the WWD forecaster.
For the 2013-14 season and onward, the limited suite of probabilistic forecasts is
usually not edited.
The probabilistic forecasts found here on the WPC PWPF page are also based on
the deterministic WWD
accumulation forecasts and are generated automatically using an ensemble of
model forecasts along with the WWD forecasts. The automatic nature of this product
generation allows an extensive set of displays of probabilities for
snowfall or freezing rain exceeding a number of thresholds and accumulations of snowfall
or freezing rain for various percentile levels. The percentile amounts and probabilities for 24-hour
intervals are generated at six-hour increments through 72 hours. The six-hour
increments are made possible by disaggregation of the 24-h human deterministic forecast
based on six-hour accumulations from a blend of model guidance selected by the WWD
The automatic processing also allows the generation of probabilistic winter
precipitation forecasts for 48-h intervals based on 48-h accumulations obtained
by adding two 24-h accumulations together. The same method used to compute the 24-h
probabilistic products is applied to the 48-h intervals ending at 48 through 72 hours
after the initial time. As with the 24-hour forecasts, the 48-h forecasts are
produced at six-hour intervals. Finally, a single set of probabilistic forecasts
are created for the entire 72-hour period.
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)
5 NCEP GEFS members, randomly selected
32 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 (Toth and Szentimrey 1990) probability distribution or 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 WPC
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 formats:
Probabilities of exceeding a threshold show filled contour levels
of probability that the 24-hour, 48-hour, or 72-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.
Percentile accumulations for 24-, 48-, or 72-hour intervals show filled
contours of snowfall or freezing
rain amounts for which the probability of observing that amount or less is
given by the percentile level. For example, if the 75th percentile map
shows six inches of snow at a location, then the probability of getting up to
six inches of snow is 75% at that point. Conversely, there is only a 25%
probability of snowfall exceeding six inches at the location in this example.
Percentile accumulations increase as the percentile level increases.
To illustrate this point, take the previous example, but instead of
the 75th precentile map consider the 10th percentile map showing
two inches of snow at the location. In this case, the probability of getting
up to but no more than two inches of snow is just 10%. The probability of
getting more than two inches is 90%; so, a significant accumulation of
snow is likely.
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
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,
Toth, Z., and T. Szentimrey, 1990: The binormal distribution: A distribution for representing
asymmetrical but normal-like weather elements. J. Climate, 3,