Meteorology Commons^™

A Crowdsourced Hail Dataset: Potential, Biases, And Inaccuracies, Joseph Robert Pehoski

Theses and Dissertations

Hail is a substantial severe weather hazard in the USA, with significant damage to property and

crops occurring annually. Traditional methods of forecasting hail size have limited accuracy, and despite

improvements in remote sensing of precipitation, the fall characteristics of hail make quantification of

hail imprecise. Research into hail is ongoing, but traditional hail datasets have known biases and low

spatiotemporal resolution. The increased usage of smartphones creates the opportunity to use a

crowdsourced dataset provided by the Precipitation Identification Near the Ground (PING) program, a

program developed by the National Severe Storms Laboratory. PING data is compared to approximate …

Assessing The Predictability Of Convection Initiation Using An Object-Based Approach, Brock James Burghardt

Theses and Dissertations

Improvements in numerical forecasts of deep, moist convection have been notable in recent years and are in large part due to increased computational power allowing for the explicit numerical representation of convection. Accurately forecasting the timing and location of convection initiation (CI), however, remains a substantial forecast challenge. This is attributed to the inherently limited intrinsic predictability of CI due to its dependence on highly non-linear moist physics and fine-scale atmospheric processes that are poorly represented in observations. Because CI is the starting point of deep, moist convection that grows upscale, even small errors in initial convective development can rapidly …