2. Pixels in the reflectivity composite field are clustered to find storms at different scales (20km^2, 160km^2, 480 km^2). Properties are extracted from grids on left at these scales.

3. Human-training of storm-type algorithm, classifying storms into 4 types: supercell, line, pulse, unorganized

1. Some of the spatial grids input into the storm type algorithm: these are derived from multi-radar 3D grids created in real-time for all WSR-88D in CONUS

Valliappa Lakshmanan, Travis Smith, Robert RabinUniversity of Oklahoma & National Severe Storms Laboratory, Norman OK, USA

Partial funding for this research was provided under NOAA-OU Cooperative Agreement #NA17RJ1227

1. More categories of storms2. A broader, more diverse training set3. Build climatology in collaboration with NCDC

iv. Future Plans

Automated Classification of Storms Based on Radar-Derived Storm Properties

Please do stop me if you see me in the hallway! I’d love to address any questions or comments.

Reflectivity @ 11km Reflectivity near ground Reflectivity @ -20C

VIL

Prob. Of Significant Hail

Az Shear 0-3km Step 2

4. Train decision tree on data

5. Storm type algorithm running in real-time. The results are shown visualized using Google Earth

To identify the storm type (supercell, linear, pulse storm or non-organized) in real-time. i. Goal

ii. Why? 1. Automated classification can be used to create climatology of storms across CONUS2. The climatology can be used to create guidance for probabilistic warnings.

Yes, you can! Download the software from http://www.wdssii.org/ and run w2segmotionll

v. Can I try this on my data?

iii. Technique