Promoting Transition to Postsecondary Education: Creating Opportunities for Social Change

Multiple studies document that students with disabilities participate at significantly lower rates than their peers without disabilities in post-secondary education, post-school employment, independent living, and community participation. This article exposits a program model at Ohio University, Gateway to Success, which addresses this inequity through a combined effort of various stakeholders. Particular consideration is given to evidence based predictors related to post-school success, the need for intervention, and the social justice implications of increased participation in post-secondary education for students with disabilities

Total Lightning Characteristics with Respect to Radar-Derived Mesocyclone Strength

Recent work investigating the microphysical and kinematic relationship between a storm's updraft, its total lightning production, and manifestations of severe weather has resulted in development of tools for improved nowcasting of storm intensity. The total lightning jump algorithm, which identifies rapid increases in total lightning flash rate that often precede severe events, has shown particular potential to benefit warning operations. Maximizing this capability of total lightning and its operational implementation via the lightning jump may best be done through its fusion with radar and radarderived intensity metrics. Identification of a mesocyclone, or quasisteady rotating updraft, in Doppler velocity is the predominant radarinferred early indicator of severe potential in a convective storm. Fused lightningradar tools that capitalize on the most robust intensity indicators would allow enhanced situational awareness for increased warning confidence. A foundational step toward such tools comes from a better understanding of the updraftcentric relationship between intensification of total lightning production and mesocyclone development and strength. The work presented here utilizes a sample of supercell case studies representing a spectrum of severity. These storms are analyzed with respect to total lightning flash rate and the lightning jump alongside mesocyclone strength derived objectively from the National Severe Storms Laboratory (NSSL) Mesocyclone Detection Algorithm (MDA) and maximum azimuthal shear through a layer. Early results indicate that temporal similarities exist in the trends between total lightning flash rate and low to midlevel rotation in supercells. Other characteristics such as polarimetric signatures of rotation, flash size, and cloudtoground flash ratio are explored for added insight into the significance of these trends with respect to the updraft and related processes of severe weather production

Kinematic and Microphysical Significance of Lightning Jumps Versus Non-Jump Increases in Total Flash Rate

Thirty-nine thunderstorms are examined using multiple-Doppler, polarimetric and total lightning observations to understand the role of mixed phase kinematics and microphysics in the development of lightning jumps. This sample size is larger than those of previous studies on this topic. The principal result of this study is that lightning jumps are a result of mixed phase updraft intensification. Larger increases in intense updraft volume greater than or equal to 10 m(sup -1) and larger changes in peak updraft speed are observed prior to lightning jump occurrence when compared to other non-jump increases in total ash rate. Wilcoxon-Mann-Whitney Rank Sum testing yields p-values 0.05, indicating statistical independence between lightning jump and non-jump distributions for these two parameters. Similar changes in mixed phase graupel mass magnitude are observed prior to lightning jumps and non-jump increases in total ash rate. The p-value for graupel mass change is p=0.096, so jump and non-jump distributions for graupel mass change are not found statistically independent using the p=0.05 significance level. Timing of updraft volume, speed and graupel mass increases are found to be 4 to 13 minutes in advance of lightning jump occurrence. Also, severe storms without lightning jumps lack robust mixed phase updrafts, demonstrating that mixed phase updrafts are not always a requirement for severe weather occurrence. Therefore, the results of this study show that lightning jump occurrences are coincident with larger increases in intense mixed phase updraft volume and peak updraft speed than smaller non-jump increases in total ash rate

Trusting AI: Integrating Artificial Intelligence into the Army’s Professional Expert Knowledge

Integrating artificially intelligent technologies for military purposes poses a special challenge. In previous arms races, such as the race to atomic bomb technology during World War II, expertise resided within the Department of Defense. But in the artificial intelligence (AI) arms race, expertise dwells mostly within industry and academia. Also, unlike the development of the bomb, effective employment of AI technology cannot be relegated to a few specialists; almost everyone will have to develop some level of AI and data literacy. Complicating matters is AI-driven systems can be a “black box” in that humans may not be able to explain some output, much less be held accountable for its consequences. This inability to explain coupled with the cession to a machine of some functions normally performed by humans risks the relinquishment of some jurisdiction and, consequently, autonomy to those outside the profession. Ceding jurisdiction could impact the American people’s trust in their military and, thus, its professional standing. To avoid these outcomes, creating and maintaining trust requires integrating knowledge of AI and data science into the military’s professional expertise. This knowledge covers both AI technology and how its use impacts command responsibility; talent management; governance; and the military’s relationship with the US government, the private sector, and society.https://press.armywarcollege.edu/monographs/1955/thumbnail.jp

Understanding the Implications of Merging Existing Lightning Datasets with GLM for Severe Thunderstorm Monitoring

No abstract availabl