Tropical cyclogenesis: Controlling factors and physical mechanisms

Rajasree, V.P.M.; Cao, Xi; Ramsay, Hamish; Núñez Ocasio, Kelly M.; Kilroy, Gerard; Alvey, George R.; Chang, Minhee; Nam, Chaehyeon Chelsea; Fudeyasu, Hironori; Teng, Hsu-Feng; Yu, Hui

doi:10.1016/j.tcrr.2023.09.004

Cited by 13 publications

(2 citation statements)

References 174 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TAMS was initially developed to track and analyze tropical MCSs over Africa associated with African easterly waves (Núñez Ocasio et al, 2020a, b). These MCSs are directly associated with the formation of tropical cyclones in the Atlantic (Núñez Ocasio et al, 2020b;Rajasree et al, 2023), the intensity of the West African Monsoon (Núñez Ocasio et al, 2021), and initiation by topography (Hamilton et al, 2020(Hamilton et al, , 2017, which all together, adds layers of complexity to study them. This first version of TAMS comprised four main steps: identification, tracking, classification, and the assignment of precipitation.…”

Section: Tams V10mentioning

confidence: 99%

TAMS: A Tracking, Classifying, and Variable-Assigning Algorithm for Mesoscale Convective Systems in Simulated and Satellite-Derived Datasets

Núñez Ocasio,

Moon

2024

Preprint

View full text Add to dashboard Cite

Abstract. The Tracking Algorithm for Mesoscale Convective Systems (TAMS) is a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems (MCSs). TAMS was initially developed to analyze MCSs over Africa and their relation to African easterly waves using satellite-derived datasets. This paper describes TAMS v2.0, an open-source MCS tracking and classifying Python-based package that can be used to study both observed and simulated MCSs. Each step of the algorithm is described with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this MCS tracker is its support for unstructured grids in the MCS identification stage and grid-independent tracking of MCSs, enabling application across various native modeling grids and satellite-derived products. A description of the available settings and helper functions is also provided. Finally, we share some of the current development goals for TAMS.

show abstract

Section: Tams V10mentioning

confidence: 99%

TAMS: A Tracking, Classifying, and Variable-Assigning Algorithm for Mesoscale Convective Systems in Simulated and Satellite-Derived Datasets

Núñez Ocasio,

Moon

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…There has been a considerable amount of research on the relationship between AEWs and TCs (Carlson, 1969; Enyew & Mekonnen, 2022; W. M. Frank & Roundy, 2006; Hopsch et al., 2007; Núñez Ocasio et al., 2021; Russell et al., 2017; Thorncroft & Hodges, 2001; Wu et al., 2012). However, recent work has shown that environmental conditions may be the driving factor behind tropical cyclogenesis (Emanuel, 2022; Helms & Bosart, 2021; Hoogewind et al., 2020; Mei et al., 2019; Rajasree et al., 2023; C. Wang et al., 2019) and that AEWs are not necessary to maintain basin‐wide TC frequency (Bercos‐Hickey & Patricola, 2023; Danso et al., 2022; Patricola et al., 2018). Although the observational record clearly indicates that AEWs can indeed develop into TCs, the complex relationship between AEWs, the environment, and tropical cyclogenesis warrants further investigation.…”

Section: Introductionmentioning

confidence: 99%

African Easterly Wave Strength and Observed Atlantic Tropical Cyclone Genesis and Characteristics

Bercos‐Hickey,

Patricola

2024

JGR Atmospheres

View full text Add to dashboard Cite

African easterly waves (AEWs) are known precursors to Atlantic tropical cyclones (TCs), and are therefore often directly connected to extreme weather events that can be both deadly and destructive. It is well established that not all AEWs develop into TCs, and there has been substantial research that has addressed the different characteristics and environments of developing and non‐developing waves. In this study, however, we specifically examine 41‐years of developing AEWs to provide a better understanding of the relationship between the developing wave and the environment, and the resulting TC. To conduct this research, we identified TCs with AEW origins from the observational record between 1980 and 2020. We then used an objective tracking algorithm to identify the developing AEWs in reanalysis data. We found a statistically significant relationship between the strength of the developing AEWs, TC genesis location and landfall, and sea surface temperature (SST) during TC genesis. Weaker AEWs tend to develop into TCs closer to the Americas in a region with warmer SSTs than those of the stronger AEWs, which tend to develop into TCs closer to Africa. Consequently, the TCs that develop from weaker AEWs are more likely to make landfall due to the close proximity of their genesis locations to the Americas.

show abstract

Augmented reality about Tropical Cyclones in the Dominican Republic: evaluation of learning and cognitive load

Michel-Acosta,

Pepín-Ubrí,

Chaljub-Hasbún

2024

J. New Approaches Educ. Res.

View full text Add to dashboard Cite

Introduction The Dominican Republic, due to its nature as a Small Island Developing State (SIDS), faces several challenges in the face of extreme weather phenomena such as hurricanes. Therefore, integrating technologies such as Augmented Reality (AR) in teaching these topics in class can influence student motivation and improve learning. Aim This article has three objectives: 1) to evaluate the learning results of the participating students using the pre-test and post-test methodology; 2) to find out the cognitive load it produces in the participating students and 3) to analyze the relationships between the different types of cognitive load. Methodology It has a quantitative approach, with a quasi-experimental design using the pre-test-post-test technique. It was carried out between May and July 2024 and consists of a non-probabilistic sample (N = 45). In addition, the student’s cognitive load was measured when interacting with the AR object, in its three types: internal, external, and relevant. Results When comparing the results of the pre-test and post-test, we obtained average values of 3.84 with a high level of variability and 4.75, with less dispersion in the answers. On the other hand, the cognitive load instrument shows high levels of internal consistency with 0.93 for the total instrument. The strongest correlation, 0.93, was obtained between external cognitive load and mental effort invested. Conclusions The hypothesis has been tested: participating students have better learning outcomes about hurricanes (tropical cyclones) after interacting with the learning object in AR format.

show abstract

Tropical cyclogenesis: Controlling factors and physical mechanisms

Cited by 13 publications

References 174 publications

TAMS: A Tracking, Classifying, and Variable-Assigning Algorithm for Mesoscale Convective Systems in Simulated and Satellite-Derived Datasets

TAMS: A Tracking, Classifying, and Variable-Assigning Algorithm for Mesoscale Convective Systems in Simulated and Satellite-Derived Datasets

African Easterly Wave Strength and Observed Atlantic Tropical Cyclone Genesis and Characteristics

Augmented reality about Tropical Cyclones in the Dominican Republic: evaluation of learning and cognitive load

Contact Info

Product

Resources

About