Mike Evans Motivation Models are often better at forecasting large scale patterns than quantitative precipitation Forecasters often make forecasts by examining model forecasts of largescale patterns then evaluating ID: 287824
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Slide1
Use of a historical analog-based winter storm guidance package for forecasting a central New York snow event
Mike EvansSlide2
Motivation
Models are often better at forecasting large scale patterns than quantitative precipitation.
Forecasters often make forecasts by examining model forecasts of large-scale patterns, then evaluating
qpf
based on experience.
“Based on my experience, what type of precipitation pattern does this type of storm produce?”.
“Does the model QPF look reasonable?”.
This type of methodology should best be employed by forecasters with the most experience.
Slide3
Motivation
…Forecast skill is largely determined by experience. The relative advantage of highly experienced forecasters is secured by virtue of the larger set of cases from which they many draw upon….
-
Roebber
and
Bosart
, 1996Slide4
Motivation continued…
….There were major variations in the experience and perceived abilities of the Phoenix teams, generally producing the expected variations in performance. Teams composed of more senior and capable forecasters produced the greatest improvements (over guidance)…
- McCarthy, Purcell and Ball, 2007 Slide5
The problem….
“I can’t remember what happened yesterday, let alone what happened with some storm from 5 years ago”
-
An unidentified lead forecaster with many years of experienceSlide6
The Saint Louis University Historical Analog-Based Winter Weather Guidance System
Aids forecasters by providing easy access to a data set of winter storm events that are similar to upcoming, forecast events.
“Instant Experience”Slide7
The Saint Louis University Historical Analog-Based Winter Weather Guidance System
Ingests current GFS forecasts of large-scale forecast patterns
Searches the NARR data base to find the best historical analogs to the current forecast
Returns information on the analogs
Mean fields, and probabilistic information is included
Detailed information on the top 15 analogs is includedSlide8
Parameters that determination the top 15 analogs
Parameters are determined within a user-selectable domain (2 east-coast domains, 2 mid-west domains).
300 and 500 and 850
hPa
heights.
850
hPa
temperature and theta-e advection.
850 and 700
hPa
frontogenesis
.
Surface temperature and sea-level pressure.
Precipitable
water.
Storm track is included in the calculation.
For details, see:
http://intra.crh.noaa.gov/metdat/ssd/2008-12-16 13.01_SLU_Analog_Seminar.wmv.Slide9
http://www.eas.slu.edu/CIPS/ANALOG/analog.phpSlide10
Select a model run for comparisonSlide11
15 analogs are returned – with information on the analogs availableSlide12
Example – December 31, 2008Slide13
Sea-level pressure and 1000-500 mb thickness – December 31, 2008Slide14
700 hPa heights and f
rontogenesisSlide15
Vertical motion, temperature and NAM QPF – December 31, 2008Slide16
How much snow have similar systems historically produced for our area?Slide17
Mean 500 hPa heights and sea-level pressure from the 15 analogsSlide18
Probabilistic snowfall information from the 15 analogsSlide19
Best analog – February 26, 1994Slide20
Best analog – observed snowfallSlide21
2nd best analog – January 13, 2000Slide22
2nd best analog – observed snowfallSlide23
Observed snowfall – December 31, 2008Slide24
Conclusion
Probabilistic information from the analogs indicated a high probability for greater than 2 inches of snow across central NY, with lower probabilities of 4 and 6 inches.
The best analogs indicated that a west-east band of 6 to 10 inches would be possible across central New York. Slide25
In this case, the guidance system…
Provided “instant experience” to the forecaster for these types of systems.
Allowed forecasters to assess the “reasonability” of the model QPF, and corresponding snowfall amounts.
Other cases showed similar results.
Still waiting to assess the system for a major nor’easter.