Participants will know the following vocabulary: funnel cloud, tornado, straight line winds, downburst, microburst, hail, graupel, mesocyclone, Doppler radar, reflectivity, attenuation, velocity folding, Doppler dilemma, divergence, vorticity, range folding, anomalous propagation, squall line, VIL, Z/R Relationship, storm relative, convergence, supercell, mesoscale. Participants will know what weather products are used to determine whether a circulation may be forming. Participants will understand how a radar completes a scan, and understand the relationship between the range from the radar and the height of the radar beam. Participants will understand the difference in resolution of the radar at near and far ranges (i.e. the aspect-ratio problem) and the significance of the problem in detection of small-scale phenomena at far ranges. Participants will understand the limitations of Doppler radar and what phenomena can and cannot be measured by it. Participants will understand the weakness of Doppler radar to see low-level phenomena at far- ranges (e.g. gust fronts, weak mesocyclones, low-topped storms). Participants will know what velocity folding is, how to recognize it on the radar when the velocity dealiasing algorithm fails. Participants will know what range folding is, its causes, and how to recognize it on the WSR- 88D. Participants will know what attenuation is and how to respond to the possibility of attenuation of a rain region on the radar. Participants will recognize the following signatures in NIDS reflectivity data: hook echo hail core thin line squall line Participants will be able to recognize the following signatures in NIDS velocity/reflectivity data: mesocyclone convergent signature of gust front divergent signature of downburst rear-flank downdraft Participants will be able to distinguish between convective and stratiform precipitation as depicted by NIDS images. Participants will be able to distinguish between convective precipitation and non- precipitating echoes (including anomalous propagation) as depicted by NIDS images. Participants will be able to recognize warm air and moisture intrusions using Mesonet data. Participants will be able to correlate a thin line on NIDS reflectivity with Mesonet observations of temperature, humidity (dew point), and winds.  Participants will be able to recognize the wind shift and associated temperature and humidity patterns in Mesonet data produced by dry lines, cold fronts, warm fronts, and outflow boundaries and know the significance of the patterns to severe weather evolution. Participants will know the limitations of a Mesonet rain gage at high rain rates. Participants will be able to discern mesoscale (as opposed to stormscale) wind flow from NIDS velocity images. Participants will be able to interpret VIL images and correlate to the VIL-of-the-day and possible hail formation. Participants will be able to obtain pertinent NWS watches, warnings, and statements from OK-FIRST WWW server. Participants will be able to determine intensity of storms by comparing higher-angle base reflectivity, composite reflectivity, and layer composite reflectivity. Participants will know the type of damage that is likely to be produced by different wind speeds (e.g., 20 mph vs. 50 mph vs. 70 mph). Participants will produce a prioritized to-do list based on the type of event and the estimated lead time. Participants will be able to calculate the lead time to a wind shift, potential tornado, or hail.