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Observations of Wind Asymmetries in Atlantic Tropical Cyclones Erin

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Observations of Wind Asymmetries in Atlantic Tropical Cyclones Erin Observations of Wind Asymmetries in Atlantic Tropical Cyclones Erin Dougherty Academic Affiliation, Fall 2014: Senior, University of Virginia SOARS® Summer 2014 Science Research Mentor: Chris Davis Writing and Communication Mentor: Kate Young Coach: Teri Eastburn Peer Mentor: Anthony Torres ABSTRACT Most major cities are located on coastlines, vulnerable to the direct impacts of tropical cyclones. Therefore, it is critical to understand and improve prediction of these storms in order to make communities more resilient. Though hurricane warning systems have improved in recent years, these warnings are insufficient, because they fail to account for an indication of tropical cyclone wind asymmetry, or the radial extent of maximum winds in different locations within the cyclone. This study explored the wind asymmetry (defined by magnitude and orientation) among 337 Atlantic tropical cyclones from 1988-2012, utilizing the National Hurricane Center’s (NHC) Extended Best Track Dataset (EBT) and Statistical Hurricane Intensity Prediction Scheme (SHIPS). Asymmetry was defined as the magnitude of the largest difference in the radius of gale- force wind across opposing quadrants, normalized by the average of the four wind radii. The asymmetry orientation pointed along the axis of maximum asymmetry toward the quadrant with the greater gale radius. Relationships between wind asymmetry and various storm characteristics such as geographical location, storm life cycle, intensity, size, storm motion, and vertical wind shear were examined. The magnitude of asymmetry increased in higher latitudes and along coastlines, particularly in smaller storms. Asymmetry was higher at the beginning of a storm’s life, possibly owing to a less well-organized structure, and higher near the end of a storm’s life, coinciding with an increase in vertical wind shear and translation speed. Results from this study may allow for improved tropical cyclone forecasts and warnings to help protect seaside communities. This work was performed under the auspices of the Significant Opportunities in Atmospheric Research and Science Program. SOARS is managed by the University Corporation for Atmospheric Research and is funded by the National Science Foundation, the National Oceanic and Atmospheric Administration, the National Center for Atmospheric Research, the University of Colorado at Boulder, Woods Hole Oceanographic Institution and by the Center for Multiscale Modeling of Atmospheric Processes. 1. Introduction Winds in tropical cyclones (TCs) are a critical factor in determining the storm’s intensity and they provide the basis for issuing appropriate warning systems. Despite recent advances in forecasts and warning systems (Lubchenco & Hayes 2012), winds in tropical cyclones continue to result in a huge amount of destruction, both to economy and human life (Blake, Landsea, & Gibney 2011). From 1988-2012, tropical cyclones resulted in approximately $3.6 trillion in damage and 20,375 fatalities in the United States alone (Weather Underground). Thus, a better understanding of these powerful storms is essential, especially regarding the structure and behavior of wind in TCs. Though warning systems focus mainly on a TC’s maximum wind speed, current studies suggest that the distribution of wind speeds within a TC provide a more comprehensive illustration of the storm’s unique behavior (Uhlhorn et al. 2014; Rogers & Uhlhorn 2008; Kimball & Mulekar 2004). This differential distribution of wind speeds, known as wind asymmetry, has been explored in order to understand how a given TC’s structure and intensity change based on environmental factors. For example, Uhlhorn et al. (2014) found that the amplitude of wavenumber-1 flight-level (700mb) wind asymmetries increased with storm translation speed and that flight- level asymmetry amplitudes were about 50% greater than those at the sea surface. Rogers & Uhlhorn (2008) similarly found a relationship between the asymmetric wavenumber-1 parameter and storm motion and shear in Hurricane Rita. The amplitude of asymmetry in Rita evolved from displaying a maximum to the right of the storm track at both flight and surface level on the first day to exhibiting a right of storm track at flight- level and left of storm track at surface level (Rogers & Uhlhorn 2008). Both Uhlhorn et al. (2014) and Rogers & Uhlhorn (2008) suggest a number of factors, such as vertical wind shear, SOARS® 2014, Erin Dougherty, 2 storm translation speed, and storm motion, contributing to observed wind asymmetries, as characterized by the wavenumber-1. While previous studies examining wind asymmetry have relied on aircraft reconnaissance data, this study will utilize the National Hurricane Center’s (NHC) Extended Best Track (EBT) dataset and the Statistical Hurricane Intensity Prediction Scheme (SHIPS). The EBT is advantageous in providing consistent 6-hr observations of TC structure and intensity over the Atlantic from 1988-2012, yet the main limitation present is the considerable deal of uncertainty in the many of the parameters. The main contribution to uncertainty is the collection of data from numerous sources, such as satellite, aircraft reconnaissance, and surface measurements (among land-falling TCs). The uncertainty is especially notable when it comes to the gale-force (34-kt) wind radii measurements, used to define TC wind asymmetry, with uncertainty measures from 25-40nm (Figure 1). In using the gale-force wind radii, this study is also limited to observing those wind radii in four-quadrants only (Landsea & Franklin 2013). Figure 1. Figure from Landsea & Franklin (2013) of the extended best-track gale-maximum wind radii uncertainty, stratified by measurement type and tropical cyclone intensity. SOARS® 2014, Erin Dougherty, 3 Despite the drawbacks in using the EBT dataset, the consistent and detailed observations were useful for exploring Atlantic TC wind asymmetries from 1988-2012, which was the goal of this study. Wind asymmetry was defined in various ways, such as normalized magnitude, directional orientation, and storm relative orientation, all based off of the radial extend of gale- force winds among four quadrants. Relationships were then examined between asymmetry metrics with other TC characteristics, including geographic location, storm life cycle, vertical wind shear, and storm translation speed. In section 2, TC data and methodology for defining asymmetry metrics will be discussed. Section 3 will include results of asymmetry metrics, while section 4 will present a discussion of the results, and section 5 will provide conclusions and directions for future work. 2. Methods Tropical Cyclone Data Atlantic tropical cyclone (TC) data from 1988-2012 were obtained via the Tropical Cyclone Extended Best Track Dataset (EBT) version 2.01 (last updated February 22, 2013). The data are based on the National Hurricane Center’s (NHC) Atlantic TC dataset, extending back to 1851, known as HURDAT. The EBT includes the 6-hourly observational data from HURDAT, and is derived from a number of sources, such as ships and surface stations, satellites, and aircraft, all of which are associated with some degree of error and uncertainty (DeMaria et al. 2013, Landsea and Franklin 2013). The EBT also contains six additional parameters determined by the NHC: eye diameter, radius of maximum wind (RMW), radii of the 34, 50, and 64-kt winds in four quadrants (northeast, southeast, southwest, northwest), and the pressure and radius of the outer closed isobar (DeMaria et al. 2013). SOARS® 2014, Erin Dougherty, 4 To ensure the data adhered to logical physical properties of TCs, a quality check was performed. Cases in which the RMW exceeded the radii of 34-kt winds in all quadrants, or minimum pressure was greater than the pressure of the outer isobar were excluded from analysis (Kimball & Mulekar 2004). Other data excluded from analysis include observations with missing wind radii data, TC landfall cases, and cases in which dates and times for a specific storm in the EBT dataset and the 850-200mb vertical shear data did not match. After quality check and exclusion of unwanted data, cases remained out of the initial 10860. The vertical wind shear data comes from NHC’s Statistical Hurricane Intensity Prediction Scheme (SHIPS), which is a statistical-dynamical forecast model, used to predict TC intensity (NHC 2009). For the purposes of this project, however, SHIPS data was used for retrospective analysis of TC asymmetry rather than for forecasting purposes (RAMMB 2014). Metrics of Asymmetry Before the 34-kt wind radii (Ri) were utilized as TC asymmetry measurements, the wind radii in each quadrant were normalized in the following manner: = ( , , , ) Eqn. (1) 푅1 푅1푛 푚푒푎푛 푅1 푅2 푅3 푅4 Where R1n is the normalized 34-kt wind radii in the northeast quadrant and R1, R2, R3, and R4 are the raw 34-kt winds in the northeast, southeast, southwest, and northwest quadrants, respectively. Normalization of the wind radii was performed to remove the bias of larger TCs displaying larger asymmetries. Following normalization, wind asymmetry (A) was calculated by finding the maximum difference between opposing quadrants, given by the following equation: SOARS® 2014, Erin Dougherty, 5 = max (| |, | |) Eqn. (2) 1푛 3푛 2푛 4푛 Where A is asymmetry퐴 (unitless).푅 This− 푅yielded푅 a range− 푅 of asymmetry values from 0 to 4, with 0 indicating a symmetric TC in which the normalized 34-kt wind radii in 4- quadrants were all equal, and 4 indicating an asymmetric TC in which the 34-kt wind radius is present in
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