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

1. An updated version of a lake effect snow forecasting application based on historical analogs Mike Evans and Ron Murphy NWS Binghamton, NY

2. 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.

3. WFO BGM County Warning Area

4. Single Bands

5. Multi-bands

6. Multi-bands

7. 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).

8. 00z February 6th, 2007 – NAM 925 mb divergence and observed reflectivity

9. Smaller multi-bands – from a 2.5 km MM5 (Watson et. al. 1998)

10. 12 km NAM vs. 6 km WRF

11. 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.

12. 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.

13. 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.

14. Initial version: (http://www.lightecho.net/les/) Forecasters enter data based on a model sounding

15. Example – forecast data / similar historical days

16. Example – historically similar radar reflectivity

17. 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

18. 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

19. Quick example – January 8, 2010

20. Some model forecast soundings

21. Observed radar images

22. Accumulations

23. 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.

24. 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/