1 / 1

Automated Classification of Storms Based on Radar-Derived Storm Properties

Partial funding for this research was provided under NOAA-OU Cooperative Agreement #NA17RJ1227. Automated Classification of Storms Based on Radar-Derived Storm Properties. Valliappa Lakshmanan, Travis Smith, Robert Rabin University of Oklahoma & National Severe Storms Laboratory, Norman OK, USA.

duff
Download Presentation

Automated Classification of Storms Based on Radar-Derived Storm Properties

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Partial funding for this research was provided under NOAA-OU Cooperative Agreement #NA17RJ1227 Automated Classification of Storms Based on Radar-Derived Storm Properties Valliappa Lakshmanan, Travis Smith, Robert Rabin University of Oklahoma & National Severe Storms Laboratory, Norman OK, USA i. Goal To identify the storm type (supercell, linear, pulse storm or non-organized) in real-time. ii. Why? 1. Automated classification can be used to create climatology of storms across CONUS 2. The climatology can be used to create guidance for probabilistic warnings. iii. Technique 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 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. Reflectivity near ground Reflectivity @ 11km Reflectivity @ -20C Prob. Of Significant Hail Step 2 Az Shear 0-3km VIL 5. Storm type algorithm running in real-time. The results are shown visualized using Google Earth 4. Train decision tree on data 3. Human-training of storm-type algorithm, classifying storms into 4 types: supercell, line, pulse, unorganized iv. Future Plans 1. More categories of storms 2. A broader, more diverse training set 3. Build climatology in collaboration with NCDC v. Can I try this on my data? Yes, you can! Download the software from http://www.wdssii.org/ and run w2segmotionll Please do stop me if you see me in the hallway! I’d love to address any questions or comments.

More Related