david john gagne ii
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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 - PowerPoint PPT PresentationTRANSCRIPT
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
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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: [email protected]:
@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