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An updated version of a lake effect snow forecasting application based on historical analogs. Mike Evans and Ron Murphy NWS Binghamton, NY. Outline. Lake effect snow band types in central NY. Modeling lake effect snow. Combining model output with pattern recognition.

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an updated version of a lake effect snow forecasting application based on historical analogs

An updated version of a lake effect snow forecasting application based on historical analogs

Mike Evans and Ron Murphy

NWS Binghamton, NY

outline
Outline
  • Lake effect snow band types in central NY.
  • Modeling lake effect snow.
  • Combining model output with pattern recognition.
  • A pattern recognition improvement application.
  • Example.
modeling
Modeling
  • Large single bands can be modeled realistically using models with resolutions of 5 to 12 km (Ballentine 1998, Niziol 2003).
  • Smaller multi-bands require higher resolution (Watson et. al. 1998).
modeling summary
Modeling Summary
  • “High-resolution” models can model lake effect snow bands.
  • Small multi-bands best modeled at resolutions of 5 km or less.
  • Placement / intensity errors still occur.
given that high resolution models are not perfect
Given that high resolution models are not perfect…
  • Forecasters examine low-resolution models to forecast the meso-scale environment (flow direction, moisture depth, inversion height, ect).
  • Use pattern recognition / experience to develop a set of expectations on outcomes.
  • Use high resolution models to test expectations.
  • Final forecast utilizes a combination of pattern recognition and explicit output from high resolution models.
the wfo bgm lake effect snow application was developed to help with pattern recognition
The WFO BGM lake effect snow application was developed to help with pattern recognition
  • Compares current forecast sounding data with data from a historical sounding data base.
  • Runs an algorithm to determine the “most similar” historical soundings.
  • Provides users with information on these historical events.
limitation
Limitation:
  • Forecasters need to manually enter data for each time period of interest
  • This would result in entering data for several different times when conditions are evolving
  • Not optimal for rapidly assimilating a large amount of information
slide18

New version: http://www.lightecho.net/lesV2/The application automatically grabs forecast sounding data for each hour, and finds the top 5 analogs for each hour – much less data entry required

future improvements
Future Improvements
  • Add parameters that reflect how rapidly conditions are changing (accounting for short-wave passages, cold/warm advection, ect).
  • Add the capability to determine similar events based on more than one forecast sounding location.
summary conclusion
Summary / Conclusion
  • An application has been developed to aid forecasters with application of pattern recognition and historical analogs to lake effect snow forecasting
  • The latest version of the application allows for easier and faster assimilation of historical information
  • http://www.lightecho.net/lesV2/