A Comparison of Multi-Radar/Multi-Sensor Quantitative Precipitation Estimates with Mid-Level Frontogenesis for Nocturnal Elevated Convection in the Corn Belt Region

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2017-12-01
Authors
Cervac, Caitlin
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Abstract

In an effort to improve warm season quantitative precipitation forecasts, this study evaluates the frontogenetic regions associated with nocturnal mesoscale convective systems. Comparisons of these regions were made to multi-radar/multi-sensor (MRMS) quantitative precipitation estimates to calculate the distance of the frontogenetic maximum to the precipitation maximum. These comparisons were made for nineteen cases during the months of June, July, and August in 2015 and 2016. Frontogenesis for each case was evaluated at every 50 mb of the atmosphere, beginning at 900 mb and ending at 600 mb, with the ultimate goal of determining which level of the atmosphere could be used to best predict the location of the precipitation maximum. An average location was also recorded as the mean of the latitudes/longitudes of the seven levels of interest. Additionally, three different times were evaluated (0300 UTC, 0600 UTC, and 0900 UTC) to determine if time of night had any influence on the results. Overall, it was concluded that the 900 mb level and the average level were the best overall predictors of maximum rainfall, with precipitation falling approximately 150 km from the frontogenetic region on the warm side. However, t-tests indicated that the difference between the distances measured at each level were not statistically significant at all levels given an alpha value of .05, and thus no level could be technically classified as the best level. Furthermore, there did not appear to be any influence of time of night on the location of the maximum. Due to the small sample size and the large amount of variability among the cases, all results should be regarded with caution. Further analysis is necessary to understand the characteristics, shape, and location of frontogenesis which ultimately resulted in this variability.

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Sun Jan 01 00:00:00 UTC 2017