an index for anticipating excessive precipitation with elevated thunderstorms alzina foscato and...
DESCRIPTION
PRECIP Project Use McCoy’s forecast method to predict where heavy-rain-producing elevated thunderstorms will occur Deploy teams to collect observational data from storm environment or https://www.facebook.com/PRECIPresearchprogramTRANSCRIPT
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AN INDEX FOR ANTICIPATING EXCESSIVE PRECIPITATION WITH ELEVATED THUNDERSTORMSAlzina Foscato and Patrick MarketDept. of Soil, Environmental & Atmospheric SciencesUniversity of Missouri
Presented to the National Weather Association Annual Meeting21 October 2015, Oklahoma City, OK
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Acknowledgments • Funded by NSF
• Collaborators• University of Missouri
• Joshua Kastman, PhD Canidate• Ryan Difani, Master’s Canidate
• SUNY-College at Brockport• Dr. Scott Rochette
• NOAA/NWS/ Weather Prediction Center
• Mike Bodner
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PRECIP Project
Use McCoy’s forecast method to predict where heavy-rain-producing elevated thunderstorms will occur
Deploy teams to collect observational data from storm environment
http://weather.missouri.edu/PRECIP or
https://www.facebook.com/PRECIPresearchprogram
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McCoy 2014
Moisture – PWATs > 1.6” (~40 mm)Lifting – 250-mb DIV > 3 x 10-5 s-1
Instability – K Index > 32
250-mb JetCore > 70 kt
• Created a method to forecast heavy-rainfall producing elevated thunderstorms in this preferred region of elevated convection in US
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McCoy’s Conclusions…• Unique patterns to look for when forecasting heavy-
rainfall-producing elevated thunderstorms:• Strong signal; strong variability
• Upper-level jet streak to the northeast of the region• Divergence > 3 x 10-5 s-1 (lift)
• Event located within or just south of 850-mb θe advection maximum (convergence max)• Signals LLJ from the SSW (moisture; lift; instability)
• Strong signal; small variability• >30 K-index values (instability)• Precipitable water values > 1.6” (moisture)• 2-m θe pattern (confirms elevated convection)
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…led us here• Program for Research on Elevated Convection with Intense
Precipitation (PRECIP) highlighted certain composite meteorological fields, which featured• significant mean values• minimal spread
• (as quantified by low interquartile ranges)
• Excessive Precipitation with Elevated Convection (EPEC) Index• Currently being used for decision support for equipment deployment
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What is the EPEC index?• EPEC = KINX + PWAT + (Div250 x 100,000) mm s-1
• KINX= K-index• PWAT= Precipitable water• Div= Divergence
• These variables display strong signal and low variability
• 50th percentile minimum values• K-index 35 • PWATs 37 mm• Divergence 5 x 100,000 s-1
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What is the EPEC index?• Values from McCoy’s (2014) thesis
• Uses 25, 50 and 75 percentile numbers • 74 is 25th percentile • 86 is 50th percentile • 98 is 75th percentile
• Scaled divergence term, as it is many orders of magnitude smaller than KINX or PWAT
• Employed only on the “cold” side of a θe boundary
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Objectives• EPEC is designed to help aide in identifying where heavy
rainfall with elevated (or surface-based) convection may occur• Discriminate surface-based from elevated using 2-m or 950-
mb θe field
But…
• Does EPEC demonstrate skill in forecasting where heavy precipitation should occur?
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Rankings• Ranked as 0,1,2,3
• 0 being a complete miss• 1 as being the poorest• 2 being a decent event but could be better• 3 is “epic”, highlights the correct region
• Ranking these events is the initial approach to this research.
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Generate Plots• Used NAM and GFS to plot total precipitation and plot
EPEC
• Plots were created using total storm precipitation in GARP and plotting the EPEC index over it
• The total precipitation was taken at 1200 UTC of the event date and EPEC was plotted for the 36 forecast hour
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Ranking of a 0Northwest Missouri on 22 May 2014 using NAM
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Ranking of a 1Eastern Iowa on 25 June 2015 using GFS
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Ranking of a 2Central Missouri on 03 April 2015 using NAM
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Ranking of a 3Central Oklahoma on 17 July 2014 using GFS
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Results
• 21 total events• PRECIP deployments when ranked were either a 2 or a 3
• 6 total PRECIP deployments• Used for Mann-Whitney testing
Rankings Total for GFS Total for NAMRanking of 0 2 2
Ranking of 1 6 6
Ranking of 2 9 11
Ranking of 3 4 2
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Results of Season for GFS
May June July August September0
1
2
3
4
5
6
Ranking Results for GFS in 2014-2015
Ranking of 0Ranking of 1Ranking of 2Ranking of 3
Month
Num
ber i
n th
e ra
nks
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Results of Season for NAM
May June July August September0
1
2
3
4
5
6
7
Ranking Results from NAM in 2014-2015
Ranking of 0Ranking of 1Ranking of 2Ranking of 3
Month
Num
ber o
f the
rank
s
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Results Mann-Whitney Test• Used Mann-Whitney test for statistical difference
between the NAM and the GFS
• The results showed no statistical difference between the models and their rankings
• Whenever a deployment was considered, PRECIP normally went with the better performing model that day
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WPC Work0000 UTC (24 hours out) 02 July 2015 from the NAM
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Conclusions• PRECIP deployments when looked back at to rank EPEC
were either a 2 or 3
• Help to correctly identify areas of elevated convection with heavy rainfall and flash flooding
• EPEC performs better in the warm season• McCoy’s composites: May-September
• Mann-Whitney test revealed no statistically significant difference between EPEC values from NAM and GFS
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Future Work• Statistical analysis using Python
• Mean absolute error• Root mean square error• Correlation fields
• To quantify rankings
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References• McCoy, L.P., 2014: Analysis of Heavy-Rain-Producing Elevated
Thunderstorms in the MO-KS-OK Region of the United States.
• McCoy, L., P. S. Market, and C. Gravelle, 2014: Composite Analyses of Heavy-Rain-Producing Elevated Thunderstorms in the MO-KS-OK Region of the United States. AMS 26th Conference on Weather Analysis and Forecasting, Atlanta, GA, January 2014, P0.98.