The Similar Soundings Technique For Incorporating Pattern Recognition Into The Forecast Process at WFO BGM Mike Evans Ron Murphy

Outline  Forecasting issues with lake effect snow  What is the “similar soundings” technique?  Application of the technique for lake effect snow.  Other Applications / The future.

Lake effect snow forecasting  Currently, we have two ways to use models to forecast lake effect snow.  Examine data from “low-resolution” models. Use pattern recognition.  Examine explicit forecasts from high resolution models.

Example: Forecasts from the Eta Model Large single band Associated Eta12 3 hr QPF Smaller multi-bandsAssociated Eta 12 3 hr QPF

Summary  Explicit forecasts from the Eta can be used to imply location of larger bands. (Not so good with intensity).  Forecasts from the Eta can still be used to forecast smaller bands – forecast larger- scale pattern, then use pattern recognition / rules of thumb.

Example: Explicit forecasts from high resolution models 5 km MM5 Model – 24 hr precipitation2.5 km MM5 Model – 950 mb omega Intense single band east of Lake Ontario Smaller scale Finger Lakes bands

Some arguments for using low-resolution models and pattern recognition  We have years of experience and “rules of thumb” associated with forecasting lake effect snow this way.  High-detail, high resolution forecasts may look realistic, but can be completely wrong.  Small-scale, multi-band lake effect is difficult to model – even at high resolutions - output looks noisy.  Problems continue with getting full-resolution Eta data into AWIPS.  Pattern recognition can be used with low resolution ensemble forecasts (SREFs).

The best method may be a combination of the old and new ways  Always start with observations!  Examine output from lower resolution models.  Use pattern recognition to make a “first guess” on location and intensity of bands.  Examine output of high resolution, explicit forecasts of lake effect snow.  Combine information from both sources to make a forecast.

The Similar Sounding Technique Aids Forecasters With Pattern Recognition (step one).

Current Applications  This technique was originally designed to help forecast lake effect snow.  The technique was also examined for severe weather forecasting this past summer.

The Concept  Pattern Recognition is a critical skill for forecasters  The best forecasters have a wealth of experience and can recognize patterns associated with significant weather events.  Example: Lake Effect snow occurs with favorable combinations of temperature, moisture, stability and wind direction.  This technique is designed to assist forecasters with recalling details from previous events, in order to recognize the potential for future significant weather events.

Why do this?  “I can’t remember what happened yesterday, let alone 2 years ago”

The “Similar Soundings” Technique For Lake Effect Snow  A 2 year database of lake effect snow events and parameters has been developed (includes “null cases”).  An application has been developed that ingests current forecast data from BUFKIT soundings, and compares several parameters to the data in the historical data base.  Forecasters can modify the data before comparisons are made.  An algorithm is run that determines the 3 most similar historical soundings.  Forecasters can examine the soundings and the observed weather that occurred with these 3 most similar days.

How are the 3 most similar sounding days determined?  Each historical sounding is compared to current forecast data, and assigned points based on similarity.  Parameters that are compared for similarity are related to: Wind direction and speed, stability, moisture and microphysics. Time of year and time of day is also considered.  Points are added, 3 highest totals are returned to the user as “most similar”.

Where does the data come from?  Eta 6 hour forecasts, displayed on BUFKIT.  12 hour forecasts were used when 6 hour forecasts were not available.  Data taken at SYR, ITH and BGM.  Note: With the exception of the largest, strongest bands, the 12 km Eta is not really explicitly forecasting individual lake effect snow bands.

So, the similar soundings application should…  Help forecasters remember what happened two years ago.  Helps forecasters use pattern recognition based on data from a model which is not explicitly forecasting (most) snow bands, but is forecasting the larger-scale environment.

Example of using the technique to anticipate lake effect snow

LES Example… continued

LES example… continued

A Range of Possibilities?

Other Applications / The Future  Severe Weather Applications  Enhanced web pages  Searchable database

Can this technique also be used for anticipating severe weather structures?  A 3 year database of severe weather events and associated parameters has been developed.  Forecasters enter data into an online checklist.  Parameters entered into the checklist are compared to data from the data base.  An algorithm is run that compares current data to historical data. The 3 most “similar” historical dates are returned.  Forecasters can examine the soundings associated with the 3 similar events, and are given a summary of what occurred on those days.

Keep in mind…  For severe weather, this technique is not really designed to determine whether or not severe weather will occur.  It’s better at determining, if severe weather occurs, what form will it take (null events are not included in the historical data base).

Example

Example… continued

A Range of Possibilities?

So, how did the algorithm work this summer?  The technique shows promise in differentiating between organized severe weather structures and pulse storms.  Sometimes, a larger range of outcomes is indicated than what the forecaster may have been anticipating.  Should work better as more events are added to the database (about 50 so far).

The future…  Web pages will be enhanced with radar loops and weather maps.  Eta model will be replaced by either the RUC or WRF next year.  Eventually, the severe and LES databases will be used for searches.  For example: show me all of the cases where the CAPE was greater than 3000 J/kg. Show me all of the tornado cases. Show me all of the cases where the inversion height was below 800 mb, the mean wind was from 300° and the dendritic snow growth zone was greater than 100 mb deep, etc.

Questions??