Seeing Weather Through Chaos: A Case Study of Disembedding Skills in Undergraduate Meteorology Students

Abstract

Disembedding, or recognizing patterns in a distracting background, is a spatial thinking skill that is particularly relevant to the interpretation of meteorological surface and upper-air maps. Difficulty “seeing” patterns such as cyclonic flow, thermal ridges, or pressure gradients can make weather analysis challenging for students. In this qualitative case study, we characterize how three undergraduate meteorology students with varying disembedding skill complete a series of meteorological tasks. Videos and transcribed verbal data collected during the task, as well as participant products, were analyzed for instances of disembedding and rule-based reasoning. Results demonstrate that the student with greater disembedding skill relied on observing patterns embedded in meteorological maps in conjunction with rule-based reasoning, whereas the two students with lower disembedding skill preferred generalized application of rules. These results can aid meteorology instructors in recognizing students who struggle with disembedding data and patterns and inform the development of instructional interventions in undergraduate meteorology classrooms.

A Preliminary Report on the Measurement of a “Global” Weather Curiosity: The Brief Epistemic Weather Curiosity Questionnaire (EWCQ-B) and Implications for the Measurement of Weather Curiosity

“…The participants in question, described fully by , were primarily White women. In these studies, the variables of interest were weather salience; systemizing, which represents an analytical cognitive style linked previously to meteorological cognition (Bolton, Blumberg et al, 2020;McNeal et al, 2018;McNeal, Petcovic, Bals-Elsholz et al, 2019;McNeal, Petcovic, LaDue et al, 2019); awareness for weather-warning products (Allan et al, 2017); storm-preparation self-efficacy beliefs (Stewart, 2015); storm-safety protective actions (Bolton, Haynie et al, 2022;Krause et al, 2017); interestand deprivation-associated curiosity (Litman & Jimerson, 2004;Lowenstein, 1994;Lowenstein et al, 1992); trait openness (McCrae & Costa, 2008); and interest in learning broad science (Weible & Zimmerman, 2016). For our reanalysis, we selected the following items: "I am curious about why the weather changes; "I feel drawn to learning about weather;" "Weather easily captures my attention;" "I like to search for answers to questions about weather.…”

An image encryption algorithm based on a 3D chaotic Hopfield neural network and random row–column permutation

“…Chaos is a non-linear dynamical phenomenon that exists in a wide variety of natural fields [3][4][5][6], such as biology, meteorology, and economics. Interestingly, chaos is not a pure disorder but rather an ordered state that does not possess periodic changes and other notable symmetrical features.…”