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Published byPearl Alyson Leonard Modified over 9 years ago
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Cloud to Ground Lightning Climatology and Hail Prediction in the Mid-South Matthew Reagan Mississippi State University
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Objective Create model to predict severe hail events using strictly cloud-to-ground lightning
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Hypothesis Cloud to ground lightning activity will change as a hail core descends to the ground
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Charge Structure Precipitation Theory –Krehbiel 1986 –Lang and Rutledge 2002 Dipole Structure –Krehbiel 1986 –Dotzek et al. 2001 –Rakov and Uman 2003 Krehbiel 1986
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Cloud-to-Ground Lightning Percentage of Positive and Negative Strikes –Reap and MacGorman 1989 Severe Weather and Positive CG –Reap and MacGorman 1989 –MacGorman and Burgess 1994 –Liu et al. 2006
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Return Strokes Krehbiel 1986 Reap and MacGorman 1989
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Scope
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Lightning Climatology MonthPositive StrikesTotal StrikesPercent Positive January13,29999,63213.34 February27,246241,93111.26 March44,114445,5839.90 April65,672898,2607.31 May98,9381,907,5915.19 June53,9991,425,0013.79 July84,8802,252,0113.77 August66,0821,840,0113.59 September18,677381,2234.90 October19,779293,9096.73 November27,104276,2749.81 December22,360144,54415.47 Total542,15010,205,9705.31 2,795 strikes per day
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Lightning Climatology
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Percent Positive
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Return Strokes Reap and MacGorman 1989 –Positive: 78% –Negative: 31% Reap & MacGorman –NSSL Network Mid-South Study –NLDN PositiveNegative January75.547.3 February75.151.4 March76.850 April74.452.4 May74.353.4 June72.551.3 July73.149.1 August72.749.6 September72.451.1 October76.251.6 November77.951.9 December76.350.5 Total:74.350.9
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Diurnal Climatology
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Pulse Thunderstorms
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Storm Collection Hail Storms –Storm reports Non hail producing storms –VILD between 2.5 and 3.5 kg/m 3
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Extraction
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Storm of Interest
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Model Creation Lightning in 5 minute increments –Total CG –Total Positive Strikes –Return Strokes Per Strike 355 storms –300 for training –55 for testing Jack knifing –300 times in training –55 times in testing Test finalists on independent data set
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Models Used Logistic Regression Artificial Intelligence –Polynomial –Linear –Radial –Sigmoid
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What is Artificial Intelligence?
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y z x o x x o x o o x y
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Percent Correct
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Probability of Detection
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False Alarm Ratio
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Probability of False Detection
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Bias
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Heidke Skill Score
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Eliminated –D2 & D3 Polynomials –Sigmoid –Radial E001 C100 Remaining –D1 Polynomials –Linear –Radial –Logistic
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Model Families Radial E01 C10 Radial E001 C1
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PCBiasFARPODPOFDHSS Polynomial D1 C100.691.060.290.760.400.36 Linear C1000.691.060.280.760.38 Radial E001 C100.671.120.320.770.440.34 Logistic Regression0.6910.280.740.330.38 PCBiasFARPODPOFDHSS Polynomial D1 C100.0040.0290.0090.0040.0140.017 Linear C1000.0010.0250.004 0.007 Radial E001 C100.0040.0180.0040.0060.0120.016 Logistic Regression0.0020.0360.0060.0080.0120.010 Median Statistics Variance
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Independent Data Set 2002-2008 storms used for training –355 events 2009-2010 storms used for testing –205 events Linear C100 Logistic Regression PC0.530.63 Bias0.860.82 FAR0.400.29 POD0.510.58 POFD0.440.30 HSS0.070.28
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January 20, 2010
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January 20, 2010 Event b
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January 20, 2010 Event c
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EHI and Average Strikes ML CAPE0-3 SRHEHIAverage Strikes Nov. 15, 200521213544.6964.9 Nov. 29, 201024914787.44205.3 May 07, 201022512203.10138.6 May 10, 200617971421.59147.3 March 29, 200225753245.2174.3
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Center 7.5 Radius
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Limitations Hail Report Bias ¼”Pea ½”Marble ¾”Penny 7/8”Nickel 1"Quarter 1 ¼”Half Dollar 1 ½”Walnut/Ping Pong Ball 1 ¾”Golf Ball 2"Hen Egg 2 ½”Tennis Ball 2 ¾”Baseball 3"Teacup 4"Grapefruit 4 ½”Softball Hail SizeFrequency 1”161 1 ¼”18 1 ½”13 1 ¾”101 2”4 2 ½”2 2 ¾”10 4”3 4.50”1
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Limitations Hail Report Bias Non Severe VILD Threshold
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Limitations Hail Report Bias Non Severe VILD Threshold Cloud to ground lightning only
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Summary CG lightning peaks in summer months Positive CG accounts for 5.31% of CG lightning with a peak in winter months Script has proven flexible with storm mode Logistic regression model out performed artificial intelligence Storm environment factors need to be studied
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Questions?
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