Verification of a Blowing Snow Model and Applications for Blizzard Forecasting Jeff Makowski, Thomas Grafenauer, Dave Kellenbenz, Greg Gust National Weather Service – Grand Forks

Outline  Marginal vs. Real Blizzards  Canadian Blowing Snow Model  What is it?  Is it useful?

Marginal vs. Real Blizzards  Real blizzard = Widespread zero visibility for a long enough duration  Usually with several inches of falling snow  Shuts down most if not all activities/commerce/transportation  Marginal blizzard = Areas of zero visibility for a long enough duration  Usually with very little falling snow  Rural vs urban areas - cities may not be affected  Challenges:  Easier when heavy snow is predicted, but:  Events with little to no falling snow, difficult to forecast the differences between marginal and real blizzards  Events with little to no falling snow, difficult to forecast the differences between marginal blizzards and winter weather advisories for blowing snow  Is it possible to forecast these differences?  Is there accurate guidance available that would assist in the forecast process, and could help collaboration?

Help from Environment Canada  Baggaley, D. G., and J. M. Hanesiak, 2005: An Empirical Blowing Snow Forecast Technique for the Canadian Arctic and Prairie Provinces. Wea. Forecasting, 20,

What is the Canadian (Baggaley) Blowing Snow Model?  Based on a robust set of observations from Canadian Prairie stations  Simplifies the complexities related to forecasting blowing snow  Inputs: SnowRate, Temperature, WindSpeed, Snow Age  Outputs: Probability, Low End Wind Threshold (Patchy), High End Wind Threshold (Definite)  Probability = Probability that the visibility due to blowing snow will be 1/2sm or less  Needs a snow density model (How much snow is available to blow around?) – FUTURE WORK

Very Brief Literature Review  Created a series of charts that summarize the proportion of times where the combinations of wind speed, temperature, and snow age gave blowing snow visibility reductions of a given threshold.  This method will not always give a deterministic answer, but rather a statistical likelihood.

SnowAge 1-2 Hours

SnowAge 3-5 Hours

SnowAge 6-11 Hours

SnowAge Hours

SnowAge Hours

SnowAge 48+ Hours

Tips – From Dave Baggaley  We generally want to see some big numbers, for several hours.  Probabilities around 50% = “Blowing snow at times" or perhaps just limited to vulnerable areas.  Probabilities 80+% = Straight blowing snow forecast with the understanding that there will be variability through the period.  Probabilities 100% = Unbroken <1/4 mile visibilities.

Can this Model provide useful Guidance?  If yes…forecasting the differences between ‘real’ and marginal blizzards may be possible (or the difference between marginal blizzards and advisories).

Research Results (so far…)  Looked at each of the 10 verified winter season blizzards within FGF CWA  For each blizzard:  Selected the most severe hour  Determined the Blowing Snow Model output for selected sites (KDVL, KJMS, KGFK, KFAR, KHCO, KBJI, KPKD)  Observed data  Model data (NAM, GFS, ECMWF, MOSGuide, SREF)  Attempted to define a marginal blizzard  Compare Blowing Snow Model results  Computed MOS wind speed biases at each forecast hour

Defining a Marginal Blizzard  The difference between a marginal blizzard and a ‘real’ blizzard depends on two factors:  Coverage of low visibility  Duration of that low visibility  Downloaded ASOS/AWOS observations from each blizzard event

Defining a Marginal Event  Developed Python scripts to read the observations, and calculate coverage and duration values at different visibility thresholds (2sm, 1sm, 3/4sm, 1/2sm, 1/4sm)

Snow Small Area Snow Small Area

Classify Blizzards by Coverage and Duration – Related to Impacts  Real blizzard with snow – March 31 st  Real blizzard no snow – Jan. 26 th  Real/marginal blizzard – Dec. 28 th and Jan. 16 th  Marginal blizzard – Jan. 22 nd and Feb. 13 th  Marginal/no blizzard – Jan. 3 rd, Feb. 26 th, and March 5th  Not used  March 21 (Very small area)

Some Preliminary Results Blowing Snow Model probabilities (based on observed data)  Jan 26 th and March 31 st  Probability = 92%  Dec. 28 th and Jan. 16 th  Probability = 69%  Jan 22 nd and Feb 13 th  Probability = 55%  Jan 3 rd, Feb 26 th and March 5 th  Probability = 29% Note: If 6-hr snowfall was less than 1 inch, used the NoSnow probability

Observed Coverage vs. BLSN Model Probabilities

Model Biases Blowing Snow Model - Model biases  Inputted model T, Wind, SnowAmt into the Blowing Snow Model, and then compared that value to the observed Blowing Snow Model value (with falling snow).  Used a recent model run  NAM12  GFS40  ECMWF  MOSGuide  SREF

MOS Guidance Biases – Winter 2013/14 Blizzard Events

Takeaways - Conclusion  Canadian Blowing Snow Model shows usefulness:  Coverage indicator of low visibilities.  Output could potentially provide better shift to shift, and office to office consistency:  <50% Probability = Lower impact marginal blizzard or advisory  50% to 70% Probability = Lower impact marginal blizzard  70% to 90% Probability = High impact marginal blizzard  >90% Probability = “Real” blizzard  All information could be used in some sort of a program to give a probability based on known biases (especially wind).  Potential for a MOS Guidance Bias Smarttool (used during winter cold air advection events)?  Need to look at more cases…