System and method for forecasting snowfall probability distributions

1. A method of forecasting snowfall accumulation, the method comprising:

identifying a predicted location and a predicted time period of a snowstorm;

receiving a plurality of weather forecasts for the predicted time period in the predicted location;

for each of the plurality of weather forecasts, determining a snowfall accumulation forecast;

forming an ensemble histogram by identifying a series of consecutive, non-overlapping snowfall accumulation ranges and determining how many of the snowfall accumulation forecasts are in each of the snowfall accumulation ranges;

calculating a probability density function representing a relative likelihood of snowfall accumulation amounts based on the ensemble histogram;

forming a snowfall probability distribution based on the probability density function;

generating, based on the snowfall probability distribution, a snowfall probability forecast that includes a most likely snowfall accumulation range and a probability that snowfall accumulation in the predicted location over the predicted time period will be within the most likely snowfall accumulation range; and

outputting the snowfall probability forecast.

2. The method of claim 1 , further comprising:

identifying a deterministic snowfall accumulation forecast for the predicted location over the predicted time period; and

creating an adjusted probability density function by:

making a mode of the adjusted probability density function equal to the deterministic snowfall accumulation forecast;

calculating a difference between a mean of the probability density function and the deterministic snowfall accumulation forecast; and

shifting the probability density function based on the difference between the mean of the probability density function and the deterministic snowfall accumulation forecast, wherein the snowfall probability forecast is based on the adjusted probability density function.

3. The method of claim 1 , further comprising:

creating a normalized probability density function by moving data points from far tails of the probability density function toward a mean of the probability density function until the probabilities of each snowfall accumulation range decrease from the most likely snowfall accumulation range to the tails of the snowfall probability distribution,

wherein the snowfall probability forecast is based on the normalized probability density function.

4. The method of claim 1 , wherein the snowfall probability forecast further includes:

a higher snowfall accumulation range and a probability that snowfall accumulation in the predicted location over the predicted time period will be within the higher snowfall accumulation range; and

a lower snowfall accumulation range and a probability that snowfall accumulation in the predicted location over the predicted time period will be within the lower snowfall accumulation range.

5. The method of claim 4 , wherein the snowfall probability forecast further includes:

a highest snowfall accumulation range and the probability that snowfall accumulation in the predicted location over the predicted time period will be within the highest snowfall accumulation range; and

a lowest snowfall accumulation range and the probability that snowfall accumulation in the predicted location over the predicted time period will be within the lowest snowfall accumulation range.

6. The method of claim 1 , wherein the snowfall accumulation forecasts are determined based on the plurality of weather forecasts using a Cobb method.

7. The method of claim 6 , wherein:

each of the plurality of weather forecasts include a forecasted precipitation amount, a forecasted temperature, a forecasted vertical motion, and a forecasted relative humidity; and

the snowfall accumulation forecasts are determined by:

calculating a snow-to-liquid ratio based on the forecasted temperature, the forecasted vertical motion, and the forecasted relative humidity; and

multiplying the forecasted precipitation amount by the snow-to-liquid ratio.

8. The method of claim 1 , wherein the snowfall probability forecast is output to a remote device for display to a user via a graphical user interface.

9. The method of claim 1 , wherein the snowfall probability forecast is output to control a remote device.

10. The method of claim 1 , wherein the plurality of weather forecasts includes at least one of National Centers for Environmental Prediction (NCEP), Global Forecast System (GFS), one or more members of Global Ensemble Forecast System (GEFS), one or more members of NCEP Short Range Ensemble Forecast (SREF), or one or more members of European Centre for Medium-Range Weather (ECMWF) ensemble.

11. A system, comprising:

a forecast database that stores a plurality of weather forecasts;

an analysis unit that:

identifies a predicted location and a predicted time period of a snowstorm;

for each of the plurality of weather forecasts, determines a snowfall accumulation forecast;

forms an ensemble histogram by identifying a series of consecutive, non-overlapping snowfall accumulation ranges and determining how many of the snowfall accumulation forecasts are in each of the snowfall accumulation ranges;

calculates a probability density function representing a relative likelihood of snowfall accumulation amounts based on the ensemble histogram;

forms a snowfall probability distribution based on the probability density function;

generates a snowfall probability forecast that includes a most likely snowfall accumulation range and a probability that snowfall accumulation in the predicted location over the predicted time period will be within the most likely snowfall accumulation range; and

outputs the snowfall probability forecast.

12. The system of claim 11 , wherein:

the forecast database further includes a deterministic snowfall accumulation forecast for the predicted location over the predicted time period; and

the analysis unit is further configured to:

create an adjusted probability density function by:

making a mode of the adjusted probability density function equal to the deterministic snowfall accumulation forecast;

calculating a difference between a mean of the probability density function and the deterministic snowfall accumulation forecast; and

shifting the probability density function based on the difference between the mean of the probability density function and the deterministic snowfall accumulation forecast,

wherein the snowfall probability forecast is based on the adjusted probability density function.

13. The system of claim 11 , wherein the analysis unit is further configured to:

create a normalized probability density function by moving data points from far tails of the probability density function toward a mean of the probability density function until the probabilities of each snowfall accumulation range decrease from the most likely snowfall accumulation range to the tails of the snowfall probability distribution,

wherein the snowfall probability forecast is based on the normalized probability density function.

14. The system of claim 11 , wherein the snowfall probability forecast further includes:

a higher snowfall accumulation range and a probability that snowfall accumulation in the predicted location over the predicted time period will be within the higher snowfall accumulation range; and

a lower snowfall accumulation range and a probability that snowfall accumulation in the predicted location over the predicted time period will be within the lower snowfall accumulation range.

15. The system of claim 14 , wherein the snowfall probability forecast further includes:

a highest snowfall accumulation range and a probability that snowfall accumulation in the predicted location over the predicted time period will be within the highest snowfall accumulation range; and

a lowest snowfall accumulation range and a probability that snowfall accumulation in the predicted location over the predicted time period will be within the lowest snowfall accumulation range.

16. The system of claim 11 , wherein the analysis unit is further configured to determine the snowfall accumulation forecasts based on the plurality of weather forecasts using a Cobb method.

17. The system of claim 16 , wherein:

each of the plurality of weather forecasts include a forecasted precipitation amount, a forecasted temperature, a forecasted vertical motion, and a forecasted relative humidity; and

the analysis unit is configured to determine the snowfall accumulation forecasts by:

calculating a snow-to-liquid ratio based on the forecasted temperature, the forecasted vertical motion, and the forecasted relative humidity; and

multiplying the forecasted precipitation amount by the snow-to-liquid ratio.

18. The system of claim 11 , wherein the analysis unit outputs the snowfall probability forecast to a remote device for display to a user via a graphical user interface.

19. The system of claim 11 , wherein the analysis unit outputs the snowfall probability forecast to control a remote device.

20. The system of claim 11 , wherein the plurality of weather forecasts includes at least one of National Centers for Environmental Prediction (NCEP), Global Forecast System (GFS), one or more members of Global Ensemble Forecast System (GEFS), one or more members of NCEP Short Range Ensemble Forecast (SREF), or one or more members of European Centre for Medium-Range Weather (ECMWF) ensemble.

21. A non-transitory computer readable storage medium storing instructions that, when executed by a computer processor, cause a computing system to:

identify a predicted location and a predicted time period of a snowstorm;

receive a plurality of weather forecasts for the predicted time period in the predicted location;

for each of the plurality of weather forecasts, determine a snowfall accumulation forecast;

form an ensemble histogram by identifying a series of consecutive, non-overlapping snowfall accumulation ranges and determining how many of the snowfall accumulation forecasts are in each of the snowfall accumulation ranges;

calculate a probability density function representing a relative likelihood of snowfall accumulation amounts based on the ensemble histogram;

form a snowfall probability distribution based on the probability density function;

generate a snowfall probability forecast that includes a most likely snowfall accumulation range and a probability that snowfall accumulation in the predicted location over the predicted time period will be within the most likely snowfall accumulation range; and

output the snowfall probability forecast.

22. A method of forecasting weather accumulation, the method comprising:

identifying a predicted location and a predicted time period of a storm;

receiving a plurality of weather forecasts for the predicted time period in the predicted location;

for each of the plurality of weather forecasts, determining a weather accumulation forecast;

forming an ensemble histogram by identifying a series of consecutive, non-overlapping weather accumulation ranges and determining how many of the weather accumulation forecasts are in each of the weather accumulation ranges;

calculating a probability density function representing a relative likelihood of weather accumulation amounts based on the ensemble histogram;

creating a normalized probability density function by moving data points from far tails of the probability density function toward a mean of the probability density function until the probabilities of each weather accumulation range decrease from the most likely weather accumulation range to the tails of the weather probability distribution;

forming a weather probability distribution based on the normalized probability density function;

generating, based on the weather probability distribution, a weather probability forecast that includes a most likely weather accumulation range and a probability that weather accumulation in the predicted location over the predicted time period will be within the most likely weather accumulation range; and

outputting the weather probability forecast.

23. The method of claim 22 , further comprising:

identifying a deterministic weather accumulation forecast for the predicted location over the predicted time period; and

creating an adjusted probability density function by:

making a mode of the adjusted probability density function equal to the deterministic weather accumulation forecast;

calculating a difference between a mean of the probability density function and the deterministic weather accumulation forecast; and

shifting the probability density function based on the difference between the mean of the probability density function and the deterministic weather accumulation forecast, wherein the weather probability forecast is based on the adjusted probability density function.

24. The method of claim 22 , wherein the weather probability forecast further includes:

a higher weather accumulation range and a probability that weather accumulation in the predicted location over the predicted time period will be within the higher weather accumulation range; and

a lower weather accumulation range and a probability that weather accumulation in the predicted location over the predicted time period will be within the lower weather accumulation range.

25. The method of claim 24 , wherein the weather probability forecast further includes:

a highest weather accumulation range and the probability that weather accumulation in the predicted location over the predicted time period will be within the highest weather accumulation range; and

a lowest weather accumulation range and the probability that weather accumulation in the predicted location over the predicted time period will be within the lowest weather accumulation range.

26. The method of claim 22 , wherein the weather accumulation forecasts are determined based on the plurality of weather forecasts using a Cobb method.

27. The method of claim 26 , wherein:

each of the plurality of weather forecasts include a forecasted precipitation amount, a forecasted temperature, a forecasted vertical motion, and a forecasted relative humidity; and

the weather accumulation forecasts are determined by:

calculating a precipitation-to-liquid ratio based on the forecasted temperature, the forecasted vertical motion, and the forecasted relative humidity; and

multiplying the forecasted precipitation amount by the precipitation-to-liquid ratio.

28. The method of claim 22 , wherein the weather probability forecast is output to a remote device for display to a user via a graphical user interface.

29. The method of claim 22 , wherein the weather probability forecast is output to control a remote device.

30. The method of claim 22 , wherein the plurality of weather forecasts includes at least one of National Centers for Environmental Prediction (NCEP), Global Forecast System (GFS), one or more members of Global Ensemble Forecast System (GEFS), one or more members of NCEP Short Range Ensemble Forecast (SREF), or one or more members of European Centre for Medium-Range Weather (ECMWF) ensemble.

31. A system, comprising:

a forecast database that stores a plurality of weather forecasts;

an analysis unit that:

identifies a predicted location and a predicted time period of a storm;

for each of the plurality of weather forecasts, determines a weather accumulation forecast;

forms an ensemble histogram by identifying a series of consecutive, non-overlapping weather accumulation ranges and determining how many of the weather accumulation forecasts are in each of the weather accumulation ranges;

calculates a probability density function representing a relative likelihood of weather accumulation amounts based on the ensemble histogram;

creates a normalized probability density function by moving data points from far tails of the probability density function toward a mean of the probability density function until the probabilities of each weather accumulation range decrease from the most likely weather accumulation range to the tails of the weather probability distribution;

forms a weather probability distribution based on the normalized probability density function;

generates a weather probability forecast that includes a most likely weather accumulation range and a probability that weather accumulation in the predicted location over the predicted time period will be within the most likely weather accumulation range; and

outputs the weather probability forecast.

32. The system of claim 31 , wherein:

the forecast database further includes a deterministic weather accumulation forecast for the predicted location over the predicted time period; and

the analysis unit is further configured to:

create an adjusted probability density function by:

making a mode of the adjusted probability density function equal to the deterministic weather accumulation forecast;

calculating a difference between a mean of the probability density function and the deterministic weather accumulation forecast; and

shifting the probability density function based on the difference between the mean of the probability density function and the deterministic weather accumulation forecast,

wherein the weather probability forecast is based on the adjusted probability density function.

33. The system of claim 31 , wherein the weather probability forecast further includes:

a higher weather accumulation range and a probability that weather accumulation in the predicted location over the predicted time period will be within the higher weather accumulation range; and

a lower weather accumulation range and a probability that weather accumulation in the predicted location over the predicted time period will be within the lower weather accumulation range.

34. The system of claim 33 , wherein the weather probability forecast further includes:

a highest weather accumulation range and a probability that weather accumulation in the predicted location over the predicted time period will be within the highest weather accumulation range; and

a lowest weather accumulation range and a probability that weather accumulation in the predicted location over the predicted time period will be within the lowest weather accumulation range.

35. The system of claim 31 , wherein the analysis unit is further configured to determine the weather accumulation forecasts based on the plurality of weather forecasts using a Cobb method.

36. The system of claim 35 , wherein:

each of the plurality of weather forecasts include a forecasted precipitation amount, a forecasted temperature, a forecasted vertical motion, and a forecasted relative humidity; and

the analysis unit is configured to determine the weather accumulation forecasts by:

calculating a precipitation-to-liquid ratio based on the forecasted temperature, the forecasted vertical motion, and the forecasted relative humidity; and

multiplying the forecasted precipitation amount by the precipitation-to-liquid ratio.

37. The system of claim 31 , wherein the analysis unit outputs the weather probability forecast to a remote device for display to a user via a graphical user interface.

38. The system of claim 31 , wherein the analysis unit outputs the weather probability forecast to control a remote device.

39. The system of claim 31 , wherein the plurality of weather forecasts includes at least one of National Centers for Environmental Prediction (NCEP), Global Forecast System (GFS), one or more members of Global Ensemble Forecast System (GEFS), one or more members of NCEP Short Range Ensemble Forecast (SREF), or one or more members of European Centre for Medium-Range Weather (ECMWF) ensemble.

40. A non-transitory computer readable storage medium storing instructions that, when executed by a computer processor, cause a computing system to:

identify a predicted location and a predicted time period of a storm;

receive a plurality of weather forecasts for the predicted time period in the predicted location;

for each of the plurality of weather forecasts, determine a weather accumulation forecast;

form an ensemble histogram by identifying a series of consecutive, non-overlapping weather accumulation ranges and determining how many of the weather accumulation forecasts are in each of the weather accumulation ranges;

calculate a probability density function representing a relative likelihood of weather accumulation amounts based on the ensemble histogram;

form a weather probability distribution based on the probability density function;

generate a weather probability forecast that includes a most likely weather accumulation range and a probability that weather accumulation in the predicted location over the predicted time period will be within the most likely weather accumulation range; and

output the weather probability forecast.