When Does the Saharan Air Layer Impede the Intensification of Tropical Cyclones?

We investigate the circumstances under which the Saharan air layer (SAL) has a negative impact on the intensification of tropical cyclones (TCs) over the North Atlantic Ocean. Using hurricane tracking, aerosol optical depth (AOD) data, and meteorological analyses, we analyze the interaction of the SAL with 52 named TCs that formed over the east and central Atlantic south of the Cape Verde islands between 2004 and 2017. Following the categorization of negative SAL influences on TC intensification by Dunion and Velden, only 21% of the investigated storms can be classified (28% of all storms that encountered the SAL), and 21% of the storms continue to intensify despite the presence of the SAL. We show that among TCs that encounter the SAL, there is evidence supporting a weak negative correlation between the magnitude of TC intensification and the ambient AOD. However, above-average Saharan dust abundance in the vicinity of TCs is not a good independent indicator for storm nonintensification. To better understand the specific processes involved, a composite study is carried out, contrasting storms that intensify in the presence of the SAL against those that do not. We find that sheared air masses on the north side and drier air from the northeast of the storm early on during its lifetime, in addition to higher AOD, are associated with TC nonintensification in proximity to the SAL.

Hurricane Wind and Storm Surge Effects on Coastal Bridges under a Changing Climate

Hurricanes and their cascading hazards have been responsible for widespread damage to life and property, and are the largest contributor to insured annual losses in coastal areas of the U.S.A. Such losses are expected to increase because of changing climate and growing coastal population density. An effective methodology to assess hurricane wind and surge hazard risks to coastal bridges under changing climate conditions is proposed. The influence of climate change scenarios on hurricane intensity and frequency is explored. A framework that couples the hurricane tracking model (consisting of genesis, track, and intensity) with a height-resolving analytical wind model and a newly developed machine learning-based surge model is used for risk assessment. The proposed methodology is applied to a coastal bridge to obtain its traffic closure rate resulting from the observed (historical) and future (projected) hurricane winds and storm surges, demonstrating the effects of changing climate on the civil infrastructure in a hurricane-prone region.

Vector regression introduced

This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable) is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables) and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables) also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

Teaching and Learning Mathematics through Hurricane Tracking

During hurricane season, maps that track predicted storm paths are commonly seen on television and the Internet. The Weather Channel often receives number-one viewership ratings in regions encountering a major weather event, such as a hurricane or tornado (Kloer 2001). Mathematics teachers can tap into students' curiosity and interest about hurricanes to develop their understanding of mathematical ideas within a real-life context. In this article, we discuss observations and findings after implementing mathematics tasks based on data about hurricanes. Finding patterns and relationships, creating and interpreting graphs, and examining rates of change are just a few of the topics that can be studied. We developed these tasks as part of the Students' Transition Toward Algebra project and have used them with both middle school teachers and students.