I n t e g r i t y - S e r v i c e - E x c e l l e n c e Air Force Weather Agency Probabilistic Lightning Forecasts Using Deterministic Data Evan Kuchera and Scott Rentschler 16 Aug 2007

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 2 Motivation Air Force operators require skillful and objective probabilistic weather information to maximize efficiency and minimize loss Typically this is accomplished with ensembles for grid scale phenomena However, sub grid scale processes are probabilistic in nature even with deterministic data We believe that ensemble forecast skill will be higher if a probabilistic approach is taken with each ensemble member for sub grid scale phenomena Addresses both sub-grid scale and flow uncertainties

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 3 Motivation Example—lightning forecast with SPC SREF method 10 ensemble members CAPE values of 130,125,120,115,110,105,103,102,101,101 With a forecast threshold of 100 J/kg, this gives a 100% chance of lightning However, with values so close to the threshold, the true probability is likely much closer to 50% than 100% This can be accounted for somewhat with real-time calibration after the ensemble is created (as SPC does with success), but this is not necessarily an option for the Air Force (resource constraints, lack of calibration data)

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 4 Background Lightning background: Need graupel and ice particle collisions to transfer negative charge to the larger particles Thunderstorm updrafts need to grow large graupel particles with enough fall speed to cause a separation of charge in the vertical The theoretical value of CAPE required to do this is only 25 J/kg

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 5 Background CAPE background: Accepted parcel theory assumption is that as the parcel rises, all condensate is immediately removed, and that there is no latent heat of freezing However, lightning is caused by frozen condensates in an updraft! We decided to test CAPE both ways—the traditional way, and with condensates/latent heat of freezing

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 6 Image from NASA-GHCC Worldwide lightning climatology

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 7 Traditional Lifted Index

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 8 TEST Lifted Index

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 9 Methodology Goal: create a probabilistic lightning algorithm using a large set of CONUS observations and physical assumptions relevant worldwide hourly 20 km RUC analyses NLDN lightning in the RUC grid box (0-3 hr after analysis) 3 hour precipitation from METARS Find which forecast parameters are the best, then curve fit the probability of lightning given a binned value of that parameter

I n t e g r i t y - S e r v i c e - E x c e l l e n c e Results

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 11 NLDN 3-hourly lightning climatology for a 16 km grid box ( )

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 12 Results GL CAPE is calculated from the LFC to -20C Set to zero if equilibrium level is warmer than -20C TEST is condensate and latent heat of freezing included

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 13 CAPE > 0, Precipitation > 0.01

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 14 CAPE=0, Precipitation > 0.01

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 15 CAPE > 0, Precipitation=0

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 16 Results Climatology=0.155

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 17 Results Perfect Reliability

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 18 Results No Skill Forecast

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 19 SPC method: forecast 100% chance of lightning if GL CAPE is greater than 100 J/kg and precipitation is greater than 0.01 inches. Forecast 0% otherwise. NULL method: Always forecast 0% chance of lightning. TEST method: Algorithm presented here. BSS: Brier skill score, compares mean squared error of forecast to mean squared error of climatology. 1 is perfect, 0 is no skill, negative is worse than climatology. ROC area: Total integrated area underneath ROC curve. 1 is perfect, 0.5 is no skill. Results

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 20 Summary Algorithm has been developed to forecast lightning probability given observed instability (RUC analysis) and precipitation (METARS) Algorithm is somewhat sharp, reliable at all forecast probabilities, and has good resolution of events and non-events Buoyancy calculations probably need to account for condensate and latent heat of freezing—but our data are not conclusive on this point

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 21 Other/Future Work Equations have been developed (not shown here) to forecast strikes per unit area for application to any model resolution After knowing strikes per unit area, can forecast probabilities for smaller areas (i.e. Air Force base warning criteria area) based on downscaling climatology—equation has been developed for this purpose as well Just beginning to look at algorithm with model data and in ensembles—issues with model precipitation forecasts Acknowledgments: ARM data archive, Dr. Tony Eckel, Stephen Augustyn, Bill Roeder, Dr. David Bright, Jeff Cunningham

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 22 Questions? GFS 66 hour grid point lightning probability forecast valid this afternoon

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 23 Backup Slides

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 24 Backup Slides Adjustments for changes in model resolution or area of interest First, re-calculate total number of strikes for the new model grid box area If model grid is finer than RUC, re-calculate probabilities using inverse of strikes equation If model grid is coarser than RUC, increase probabilities using special upscaling equation If area of interest is smaller than area of model grid, recalculate strikes and use downscaling equation to get probabilities

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 25 Backup Slides Downscaling equation details Inputs: Strikes (S) horizontal resolution of coarse area in km (C) horizontal resolution of fine area in km (F) Equation: 1-[1-(F^2/C^2)]^(S^A) Where A is a “fudge factor” depending on F A=1-0.17*LN(F-1) A equals unity when F is 2 km, and slowly decreases toward zero as F approaches ~350 km In nature, lightning tends to be randomly distributed at 2 km (storm scale) but more clustered at higher resolutions. “A” attempts to account for this Best to use this equation from 2 to 128 km grid sizes If strikes is less than one, calculate equation using 1 strike, then multiply result times number of strikes

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 26 Backup Slides Upscaling Probability added to: [1-probability]*[1-(F^2/C^2)]*downscaled probability This ensures high probabilities will only occur when the original probability was high, or the area has increased substantially with moderately high initial probabilities No testing as to whether this is calibrated

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 27 Backup Slides NWS Topeka forecast taken from the web on 15 Aug: Friday, August 17 at 7pm Temperature: 89°F Thunder: <10%

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 28 Backup Slides

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 29 Backup Slides