

About WPC's Probabilistic Winter Precipitation Forecast (PWPF) Products
The operational WPC Winter Weather Desk (WWD) creates 24h forecasts of snowfall and
freezing rain accumulations for each of three consecutive 24h 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 24h snowfall and freezing rain
accumulation forecasts are finalized, the WWD issues its public products: a
limited suite of
probabilistic
winter weather forecasts. These probabilistic forecasts are computed based on
the deterministic accumulation forecasts combined with ensemble information (see below).
Prior to the 201314 season, the probabilistic forecasts were manually edited by the WWD forecaster.
For the 201314 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 24hour
intervals are generated at sixhour increments through 72 hours. The sixhour
increments are made possible by disaggregation of the 24h human deterministic forecast
based on sixhour accumulations from a blend of model guidance selected by the WWD
forecaster.
The automatic processing also allows the generation of probabilistic winter
precipitation forecasts for 48h intervals based on 48h accumulations obtained
by adding two 24h accumulations together. The same method used to compute the 24h
probabilistic products is applied to the 48h intervals ending at 48 through 72 hours
after the initial time. As with the 24hour forecasts, the 48h forecasts are
produced at sixhour intervals. Finally, a single set of probabilistic forecasts
are created for the entire 72hour period.
A multimodel 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:
10 NCEP GEFS ensemble members, randomly selected
10 ECMWF ensemble members, randomly selected
9 NCEP ShortRange Ensemble Forecast (SREF) NMMB members
6 NCEP ShortRange Ensemble Forecast (SREF) ARW members
1 NCEP North American Mesoscale 4km CONUS Nest 12Z (day) or 00Z (night) operational run Day 12. 12km NAM Day 3.
4 NCEP Hires WRF ARW (00Z/12Z) runs for Day 1. 06Z GFS/SREF NMMB members for Day 23 (day shift), 18Z GFS/SREF NMMB members for Day 23 (night shift)
2 NCEP Hires WRF NMMB (00Z/12Z) runs for Day 1. 00Z GFS/SREF NMMB member for Day 23 (day shift), 18Z GFS/SREF NMMB member for Day 23 (night shift)
1 NCEP Global Forecast System (GFS) 12Z (day) or 00Z (night) operational run
1 European Center for MediumRange Weather Forecasts (ECMWF) latest operational run
1 NCEP Global Ensemble Forecast System (GEFS) latest ensemble mean (6h SLRs)
1 WPC Forecast
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46 Total members
SLR refers to the snowtoliquid ratio, which is a multiplicative factor applied
to precipitation accumulated as type snow to compute the snowfall. The 6h
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 24h mean SLR applied to the GEFS is the average of four 6h
SLRs covering the 24h period. For all other members listed above, the 24h accumulations
are sums of 6h accumulations, using the 6h 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 nonNCEP models
is determined by applying a
simple decision tree algorithm using surface temperature, and temperatures on the 925hPa, 850hPa, and
700hPa 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 63rd 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 24hour, 48hour, or 72hour accumulation of winter precipitation will
equal or exceed the given threshold. As an example, consider the
6inch 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 72hour 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.
For more information on creation of the PWPFs and how to navigate the web page,
please see this informational video.
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: Realtime forecasting of snowfall
using a neural network. Wea. Forecasting, 22, 676684.
Toth, Z., and T. Szentimrey, 1990: The binormal distribution: A distribution for representing
asymmetrical but normallike weather elements. J. Climate, 3, 128136.
