1. Integration of Storm Scale Ensembles, Hail Observations, and Machine Learning for Severe Hail Prediction David John Gagne II Center for Analysis and Prediction of Storms (CAPS)/ School of Meteorology, University of Oklahoma RAL, NCAR, Boulder, CO Jerry Brotzge CAPS, University of Oklahoma Amy McGovern School of Computer Science, University of Oklahoma Ming Xue CAPS/ School of Meteorology, University of Oklahoma David John Gagne II Center for Analysis and Prediction of Storms (CAPS)/ School of Meteorology, University of Oklahoma RAL, NCAR, Boulder, CO Jerry Brotzge CAPS, University of Oklahoma Amy McGovern School of Computer Science, University of Oklahoma Ming Xue CAPS/ School of Meteorology, University of Oklahoma

2. Hail: The Frozen Menace Hail is large, spherical ice precipitation that originates in a convective cloud. Hail has caused billions of dollars in damage worldwide this year. It primarily damages crops, vehicles, and buildings and can injure or kill people and animals. @KD0STS @marketjournal @KNEBStormCenter @justinejeanne

3. Hail Forecasting Challenges 1.Conditions favorable for hail occur over much larger areas than actual hail does 2.Numerical weather models can generate simulated storms but have errors in intensity, location, and timing 3.Ensembles of numerical weather models capture some but not full range of uncertainty 4.Numerical models do not predict the size of hail directly Project Goals 1.Produced 18-30 hour forecasts of hail size from an ensemble of storm-scale numerical weather prediction models using machine learning methods 2.Produced consensus probabilistic forecasts of severe hail (at least 1 inch diameter) 3.Compared machine learning methods with an existing physics-based method 4.Implemented hail size forecasts in an operational environment Project Goals 1.Produced 18-30 hour forecasts of hail size from an ensemble of storm-scale numerical weather prediction models using machine learning methods 2.Produced consensus probabilistic forecasts of severe hail (at least 1 inch diameter) 3.Compared machine learning methods with an existing physics-based method 4.Implemented hail size forecasts in an operational environment

4. Storm-Scale Ensemble Forecast Ensemble of WRF-ARW models Perturbed initial and boundary conditions Microphysics, land surface model, and boundary layer parameterizations varied Models initialized at 00 UTC run for 60 hours Training data from 2013 Spring Experiment (30 runs) Testing data from 2014 Spring Experiment (12 runs) Forecast hours 18 to 30 evaluated Updraft speedStorm Height Downdraft SpeedTotal Graupel Mass Vapor Mixing RatioCAPE ShearCIN Storm Rel. HelicityLCL Updraft HelicityStorm Motion Radar ReflectivityPrecipitable Water

5. Hail Reports Hail size reported by citizens No automated instruments available mPING reports from crowd- sourced smartphone app SPC reports collected by NWS offices Quarter Golf Ball Baseball Softball

6. Radar-Estimated Hail Size Maximum Expected Size of Hail

7. Storm Identification Total mass of ice precipitation at each grid point. Enhanced watershed finds local maxima and grows objects to size limit Size filter removes objects with area less than 10 pixels Size filter removes objects with area less than 20 pixels Limitations 1.Object-finding based on single variable 2.Parameters subjectively determined 3.Size filter can remove young storms, slow-moving storms Enhanced watershed from Lakshmanan (2009)

8. Machine Analyzed Size of Hail Random Forest (Breiman 2001) Ensemble of randomized decision trees with resampled training data and random subset selection of variables. Random Forest (Breiman 2001) Ensemble of randomized decision trees with resampled training data and random subset selection of variables. Gradient Boosting Regression Trees (Friedman 2002) Additive ensemble of decision trees weighted by residuals of each tree’s predictions. Uses random subsampling of training data to increase accuracy. Gradient Boosting Regression Trees (Friedman 2002) Additive ensemble of decision trees weighted by residuals of each tree’s predictions. Uses random subsampling of training data to increase accuracy. Ridge/Logistic Regression Ridge regression fits a multivariate linear model that reduces the weight of each term added to the regression. Logistic regression performs a transform to limit output to between 0 and 1. Ridge/Logistic Regression Ridge regression fits a multivariate linear model that reduces the weight of each term added to the regression. Logistic regression performs a transform to limit output to between 0 and 1. HAILCAST (Brimelow et al. 2002, Jewell and Brimelow 2009) Physical 1-dimensional hail growth model. Initializes set of hail embryos and grows them based on conditions in model updraft. HAILCAST (Brimelow et al. 2002, Jewell and Brimelow 2009) Physical 1-dimensional hail growth model. Initializes set of hail embryos and grows them based on conditions in model updraft. MASH Model components: Hail Classification Model and Hail Size Regression

9. Experimental Forecast Program 2014 Forecasting experiment conducted by the NOAA Hazardous Weather Testbed at the National Weather Center in Norman, OK Experiment ran from May 5 to June 6 Forecasters and researchers from around the world make forecasts with the newest available tools Products are also subjectively and objectively evaluated each day Challenges Generating forecasts in timely manner Visualizing forecasts in a useful form

10. Hail Case: June 3, 2014 Filled contours indicate probability of hail at least 1 inch in diamater

11. 2014 Spring Experiment Results

12. Summary @KD0STS Email: djgagne@ou.edudjgagne@ou.edu Twitter: @DJGagneDos Website: cs.ou.edu/~djgagne Email: djgagne@ou.edudjgagne@ou.edu Twitter: @DJGagneDos Website: cs.ou.edu/~djgagne Hail can cause significant damage. Forecast and observing systems for hail both have systemic biases. Machine learning can decrease forecast error and account for uncertainties. Machine learning methods are less biased than uncalibrated physics- based approaches.

13. Acknowledgements NOAA partners: Michael Coniglio, James Correia, Adam Clark, and Kiel Ortega Doctoral committee members: Michael Richman, Andrew Fagg, and Jeffrey Basara SSEF: Fanyou Kong, Kevin Thomas, Yunheng Wang, and Keith Brewster SHARP: Nate Snook, Yougsun Jung, and Jon Labriola HAILCAST: Rebecca Adams-Selin The SSEF was run on the Darter supercomputer by NICS at the University of Tennessee This research was funded by NSF Grant AGS-0802888 and NSF Graduate Research Fellowship 2011099434