Publication Date


Document Type


First Advisor

Ashley, Walker S.

Degree Name

Ph.D. (Doctor of Philosophy)

Legacy Department

Department of Geographic and Atmospheric Sciences




This research initially evaluates the ability of image processing and select machine learning algorithms to identify midlatitude mesoscale convective systems (MCSs) in radar reflectivity images for the conterminous United States. Results using a testing dataset suggest that the algorithms can distinguish between MCS and non-MCS samples with high probability of detection and low probability of false detection. Next, sensitivity tests are performed to assess MCS tracking performance. Frequency maps and time series generated from detected MCS tracks suggest that the spatiotemporal occurrence is reasonable, and machine learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains. This approach is them applied to composite radar reflectivity mosaic images that cover the contiguous United States (CONUS) and span an unprecedented study period of 22 years (1996-2017). The results illustrate two preferred regions for MCS activity in the CONUS: 1) the Mid South and Gulf Coast, and 2) the Central Plains and Midwest. MCS occurrence and MCS rainfall displays a marked seasonal cycle, with most areas experiencing these events during the warm season (May-August). Additionally, MCS rainfall was responsible for over 50\% of annual and seasonal rainfall for many locations in the CONUS. These results confirm that MCSs are a significant aspect of the CONUS hydroclimate, and understanding how these events may change between now and the late 21st century should be a research priority. Finally, this approach is used to detect potential changes in linear and nonlinear mesoscale convective systems (MCS) occurrence in the Midwest United States between the early and late 21st century using convection-permitting climate simulation output. A comparison between observed and the control run MCS statistics is performed, which finds a negative bias that agrees with previous work. Using a convolutional neural network to perform probabilistic predictions, the MCS dataset is further stratified into highly organized, quasi-linear convective systems (QLCSs)---which can include bow echoes, squall lines, and line echo wave patterns---and generally less-organized, Non-QLCS events. The morphologically stratified data reveal that the negative MCS bias in this region is largely driven by too few QLCSs. Although comparisons between the control run and a pseudo-global warming run suggest that all MCS events are less common in the future (including QLCS and Non-QLCS events), these changes are not spatially significant, whereas the biases between the control run and observations are spatially significant.


Advisors: Walker S. Ashley.||Committee members: David Changnon; Thomas J. Pingel; Jie Zhou.||Includes illustrations and maps.||Includes bibliographical references.


155 pages




Northern Illinois University

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