The Similar Soundings Technique For Incorporating Pattern Recognition Into The Forecast Process at W...
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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.

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Outline

The Similar Soundings Technique For Incorporating Pattern Recognition Into The Forecast Process at WFO BGMMike EvansRon Murphy


Outline

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

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

Example: Forecasts from the Eta Model

Large single band

Associated Eta12 3 hr QPF

Smaller multi-bands

Associated Eta 12 3 hr QPF


Summary

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

Example: Explicit forecasts from high resolution models

5 km MM5 Model – 24 hr precipitation

2.5 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

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

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

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


Current applications

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

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

Why do this?

  • “I can’t remember what happened yesterday, let alone 2 years ago”


The similar soundings technique for lake effect snow

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

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

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

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

Example of using the technique to anticipate lake effect snow


Les example continued

LES Example… continued


Les example continued1

LES example… continued


A range of possibilities

A Range of Possibilities?


A range of possibilities1

A Range of Possibilities?


Other applications the future

Other Applications / The Future

  • Severe Weather Applications

  • Enhanced web pages

  • Searchable database


Can this technique also be used for anticipating severe weather structures

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

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


Example continued

Example… continued


Example continued1

Example… continued


A range of possibilities2

A Range of Possibilities?


A range of possibilities3

A Range of Possibilities?


So how did the algorithm work this summer

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

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

Questions??


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