A 10-year study of elevated severe thunderstorms was performed using The National Centers for Environmental Information Storm Events Database. A total of 80 elevated thunderstorm cases were identified, verified, and divided into "Prolific" and "Marginal" classes. These severe cases occurred at least 80 km away from, and on the cold side of, a surface boundary. The downdraft convective available potential energy (DCAPE), downdraft convective inhibition (DCIN), and their ratio are tools to help estimate the potential for a downdraft to penetrate through the depth of a stable surface layer. The hypothesis is that as the DCIN/DCAPE ratio decreases, there exists enhanced possibility of severe surface winds. Using the initial fields from the Rapid Refresh numerical weather prediction model, datasets of DCIN, DCAPE, and their ratio were created. Mann-Whitney U tests on the Prolific versus Marginal case sets were undertaken to determine if the DCAPE and DCIN values come from different populations for the two different case sets. Results show that the Prolific cases have values of DCIN closer to zero, suggesting the downdraft is able to penetrate to the surface causing severe winds. Thus, comparing DCIN and DCAPE is a viable tool in determining if downdrafts will reach the surface from elevated thunderstorms.

Precipitation data are important for hydrometeorological analyses, yet there are many ways to measure precipitation. The impact of station density analysed by the current study by comparing measurements from the Missouri Mesonet available via the Missouri Climate Center and Community Collaborative Rain, Hail, and Snow (CoCoRaHS) measurements archived at the program website. The CoCoRaHS data utilize citizen scientists to report precipitation data providing for much denser data resolution than available through the Mesonet. Although previous research has shown the reliability of CoCoRaHS data, the results here demonstrate important differences in details of the spatial and temporal distribution of annual precipitation across the state of Missouri using the two data sets. Furthermore, differences in the warm and cold season distributions are presented, some of which may be related to interannual variability such as that associated with the El Niño and Southern Oscillation. The contradictory results from two widely‐used datasets display the importance in properly choosing precipitation data that have vastly differing temporal and spatial resolutions. With significantly different yearly aggregated precipitation values, the authors stress caution in selecting 1 particular rainfall dataset as conclusions drawn could be unrepresentative of the actual values. This issue may be remediated by increased spatiotemporal coverage of precipitation data.