Evaluation of Lightning Jumps as a Predictor of Severe Weather in the Northeastern United States Pamela Eck, Brian Tang, and Lance Bosart University at Albany, SUNY Northeast Regional Operational Workshop 17 Wednesday, 3 November 2016

Motivation Terrain can play an important role in the evolution of severe convection in the northeastern United States Great Barrington, MA (1995) F4 tornado 3 killed, 24 injured 11.5 mile track Springfield, MA (2011) EF3 tornado 3 killed, 200 injured Mechanicville, NY (1998) F3 tornado 30.5 mile track Duanesburg, NY (2014) 10-cm-diameter hail Springfield, Massachusetts on 1 June 2011 Elevation (m)

Background Upslope flow + convection = Markowski and Dotzek 2011 Upslope flow + convection = locally enhanced updraft and increased probability of severe weather (a) qr = model rain fall at 1km (shading), w5km = vertical velocity at 5km (closed contours) A B u (c) (b) windward leeward NOTE: -1K potential temperature perturbation contours indicate approximate location of the gust front A B

Background Lack of surface observations Inability of radar beams to sample behind mountains Alternative method = sudden increase in total lightning (“lightning jump”) A lightning jump is indicative of a strengthening updraft and an increase in the probability of severe weather Valatie supercell (19 July 2015) Flash rate increased by 2 standard deviations (σ) in 10 minutes Time series of flash rates MENTION VALATIE SUPERCELL Minimum threshold of 10 flashes/min

Background Schultz et al. 2011 Severe wind producing thunderstorm on 20 June 2000 in western Kansas Purple = total lightning Red = cloud-to-ground lightning wind reports lightning jumps

Methodology New England (CT, MA, RI, VT, ME, NH), New York, Pennsylvania 8-km resolution grid spacing July & August 2014–2015 (1,791 storm tracks) Lightning data from the National Lightning Detection Network (NLDN) Total lightning = intracloud (IC) and cloud-to-ground (CG) Severe weather reports from the Storm Prediction Center (SPC) Wind, hail, and tornado Severe weather day = 12Z–12Z 2σ lightning jump algorithm Minimum threshold of 5 flashes/min NLDN has a detection efficiency of 40–60% for intracloud lightning Mention that we chose an 8-km resolution grid spacing to mimic that of the GOES-R LMA... The image in the bottom right corner of instantaneous flash density over the past 2 minutes depicts a pseudo-GLM Valatie supercell

Severe Weather Report) NO Severe Weather Report) Verification Lightning jumps were verified against severe weather reports taken from the SPC If a lightning jump occurred within 45-minutes prior to a severe storm report, it verified as a hit Calculated false alarm rate (FAR) and probability of detection (POD) Lightning Jump YES NO A. Hit (Lightning Jump + Severe Weather Report) B. False Alarm NO Severe Weather Report) C. Miss (NO Lightning Jump D. Correct Null FAR = B / ( A + B ) POD = A / ( A + C ) YES NO Severe Weather Report

Results: Part I Schultz et al. 2011 (Sigma Level = 2.0, Flash Rate = 10 flashes/min) POD = 79%; FAR = 36% Our results (Sigma Level = 2.0, Flash Rate = 5 flashes/min) POD = 83%; FAR = 84% FAR is too high… Why?

Upslope Case studies indicated… Lightning jumps are occurring in sub-severe storms Markowski and Dotzek 2011 Upslope flow + convection = locally enhanced updraft  increased probability of severe weather Hypothesis: Conditioning on an upslope variable will reduce the FAR! Calculating upslope: Upslope (Λ) = v ∙ ∇zs > 0 v = u & v component of the 80-m wind ∇zs = gradient of terrain height High Resolution Rapid Refresh (HRRR) model data 2015–2016 convective seasons July 2015 (1, 9, 14, 18, 19, 24, 26, 28) Chose 80-m wind because it has a stronger signal

Upslope Two criteria must be determined… What is the minimum upslope flow threshold? Top 5% Specify that we calculated upslope along storm tracks Lightning was used to calculate each storm track

Upslope Two criteria must be determined… What is the minimum upslope flow threshold? How many points along a storm track must meet the minimum upslope threshold? Start: 1936Z End: 2228Z ~ 50 miles Top 5% Specify that we calculated upslope along storm tracks Lightning was used to calculate each storm track = 1 = 74 19 July 2015 Λ >= 0.10 ms-1 Λ < 0.10 ms-1

Results: Part II Without upslope filter (Sigma Level = 2.0, Flash Rate = 5 flashes/min) POD = 83%; FAR = 84% With upslope filter (Sigma Level = 2.0, Flash Rate = 5 flashes/min) POD = 73% ; FAR = 80% FAR is still too high… This is not very promising

Random Forest Conditioning on upslope does not necessarily yield more skill What other products might be useful in differentiating between severe and non-severe? Maximum Reflectivity Vertically Integrated Liquid (VIL) Lightning Density Echo Top – 50 or 60 dBZ Let’s test them ALL using a random forest! What is a random forest? An ensemble learning method for classification that operates by constructing a multitude of decision trees Think of the trees as deterministic models and the forest as an ensemble…

Decision Tree Dataset is broken into two parts: 2/3 is for training 1/3 is for testing SPECIFY THAT THIS EXAMPLE IS FAKE

Decision Tree Training Nodes partition using best split Dataset is broken into two parts: 2/3 is for training 1/3 is for testing Upslope Maximum Reflectivity VIL >= 0.10 ms-1 < 50 dBZ >= 50 dBZ < 50 mm >= 50 mm < 0.10 ms-1 SPECIFY THAT THIS EXAMPLE IS FAKE

Decision Tree Training Nodes partition using best split Variables are weighted differently based on importance Training Nodes partition using best split Dataset is broken into two parts: 2/3 is for training 1/3 is for testing Upslope Maximum Reflectivity VIL < 50 dBZ >= 50 dBZ < 50 mm >= 50 mm < 0.10 ms-1 >= 0.10 ms-1 SPECIFY THAT THIS EXAMPLE IS FAKE

Decision Tree Training Nodes partition using best split Variables are weighted differently based on importance Testing Each tree “votes” for a class… The forest chooses the classification with the most votes Non-Severe | Severe 8 | 5 Upslope Maximum Reflectivity VIL < 50 dBZ >= 50 dBZ 3 | 1 2 | 0 1 | 4 < 50 mm >= 50 mm < 0.10 ms-1 >= 0.10 ms-1 SPECIFY THAT THIS EXAMPLE IS FAKE

Decision Tree Training Nodes partition using best split Variables are weighted differently based on importance Testing Each tree “votes” for a class… The forest chooses the classification with the most votes How well did this forest do? Calculate verification metrics! Non-Severe | Severe 8 | 5 Upslope Maximum Reflectivity VIL < 50 dBZ >= 50 dBZ 3 | 1 2 | 0 1 | 4 < 50 mm >= 50 mm < 0.10 ms-1 >= 0.10 ms-1 SPECIFY THAT THIS EXAMPLE IS FAKE

Decision Tree Severe Non-Severe Hit False Alarm 4 1 Miss Correct Null 4 1 Miss Correct Null 1+0+0 = 1 3+2+2 = 7 Upslope Maximum Reflectivity VIL < 50 dBZ >= 50 dBZ Non-Severe | Severe 8 | 5 3 | 1 2 | 0 1 | 4 < 50 mm >= 50 mm Non-Severe Severe < 0.10 ms-1 >= 0.10 ms-1 FAR = 0.2 POD = 0.8 This was a pretty good example! Now let’s try it with some real data… SPECIFY THAT THIS EXAMPLE IS FAKE

Decision Tree Severe Non-Severe Hit False Alarm ? ? Non-Severe Severe ? ? Miss Correct Null ? ? Upslope No Jump | Jump ? | ? Non-Severe Severe FAR = ??? POD = ??? SPECIFY THAT THIS EXAMPLE IS FAKE ? | ?

Decision Tree Severe Non-Severe Hit False Alarm 369 151 369 151 Miss Correct Null 71 151 No Jump | Jump Non-Severe Severe Upslope 151 | 71 When… Sigma Level = 2.0 Flash Rate = 5 flashes/min FAR = 0.29 POD = 0.84 SPECIFY THAT THIS EXAMPLE IS FAKE 151 | 369

Results: Part III Sigma Level = 2.0, Flash Rate = 5 flashes/min POD = 84% ; FAR = 29% FAR is so much better!!! Correlation Coefficient = 0.46

Conclusions Lightning jumps can be a valuable tool for diagnosing severe weather in regions of complex terrain High false alarm rates suggest that lightning jumps are occurring in sub-severe storms Random forests provide useful method for incorporating other variables such as upslope, maximum reflectivity, and VIL that may help to differentiate between severe and sub-severe events Results support the hypothesis that upslope and lightning jumps are correlated, which verifies the findings of Markowski and Dotzek 2011 Thank you! peck@albany.edu