Integration of Storm Scale Ensembles, Hail Observations, and Machine Learning for Severe

Hail Prediction

David John Gagne IICenter for Analysis and Prediction of Storms (CAPS)/

School of Meteorology, University of OklahomaRAL, NCAR, Boulder, CO

Jerry Brotzge

CAPS, University of OklahomaAmy McGovern

School of Computer Science, University of OklahomaMing Xue

CAPS/ School of Meteorology, University of Oklahoma

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

Hail Forecasting Challenges1. 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 Goals1. Produced 18-30 hour forecasts of hail size from an ensemble of storm-scale

numerical weather prediction models using machine learning methods2. Produced consensus probabilistic forecasts of severe hail (at least 1 inch diameter)

3. Compared machine learning methods with an existing physics-based method4. Implemented hail size forecasts in an operational environment

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 speed Storm Height

Downdraft Speed Total Graupel Mass

Vapor Mixing Ratio CAPE

Shear CIN

Storm Rel. Helicity LCL

Updraft Helicity Storm Motion

Radar Reflectivity Precipitable Water

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

Radar-Estimated Hail Size

MaximumExpectedSize ofHail

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

Limitations1. Object-finding based on single variable2. Parameters subjectively determined3. Size filter can remove young storms,

slow-moving storms

Enhanced watershed from Lakshmanan (2009)

Machine Analyzed Size of Hail

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.

Ridge/Logistic RegressionRidge 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.

MASH Model components: Hail Classification Model and Hail Size Regression

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

Hail Case: June 3, 2014

Filled contours indicate probability of hail at least 1 inch in diamater

2014 Spring Experiment Results

Summary

@KD0STS

Email: djgagne@ou.eduTwitter:

@DJGagneDosWebsite:

cs.ou.edu/~djgagneHail 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.

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