I was just 10 years old when Hurricane Irene hit New York and 11 years old when Hurricane Sandy hit. I witnessed these storms ravage my community first-hand, and I remember hearing of my cousin’s house flooding in nearby Long Beach, New York. She was forced to uproot and relocate elsewhere temporarily. These storms wreaked havoc; I was out of school for more than a week, and many of my neighbors went without electricity for many days. Even though I was a child at the time, I can still remember the chaos that ensued in the aftermath of these storms. When it came time to find a mentor outside of my high school with whom I could complete climate research, I knew I wanted research hurricanes, weather and climate. Having had personal experience with hurricanes continued to motivate me throughout the research process … Where will these major hurricanes make landfall in the future? Who in particular will be affected? When will these storms cause the most damage to Long Island and the New York metropolitan area? These questions were the foundation of the project I submitted to the Regeneron Science Talent Search. Dr. Murakami and I conceived of the project that I would complete during the summer of 2018 on the chalk board in his office. I sought to create a novel risk index that could pinpoint regions of the country expected to experience the most monetary and life losses due to hurricane activity. I desired to continue to use GIS software to visually represent the data, so laypeople could simply look at a map and understand whether or not their homes were predicted to be in danger … Heightened tropical cyclone (TC) risk is becoming a pressing issue as anthropogenic forcing and U.S. coastal population density increases (Ashley et al. 2014). Several studies quantified local U.S. TC risk by utilizing varied physical storm attributes and characteristics of the region affected. Huang et al. (2000) used measures like radius of maximum wind speed, central pressure difference, landfall location, storm track, and decay rate to identify TC risk on a zip-code scale over a 50-year interval. Similarly identifying TC risk on a zip-code scale, the risk index developed by Vigh et al. (2018) uses wind speed and storm surge height during specific storm events to provide warnings to individuals. To build upon these risk indexes, I developed a wind-based and rain-based TC risk index using a Geographic Information System (GIS)-based approach, which can calculate TC risk for a multitude of timeframes and regions of interest. Additionally, previous studies propose an increase in anthropogenic climate change and radiative forcing is associated with an increase in sea surface temperature (SST) in the North Atlantic (NA) main development region (MDR) and an increase in the proportion of the NA basin’s intense TC activity (Daloz et al. 2015). With a weakening of subtropical easterlies and an eastward shift in TC genesis location, storm tracks are predicted to curve eastward more frequently, increasing the TC density in the central and northern NA and increasing risk in the northeastern U.S. (Colbert et al. 2013). Additionally, Kossin (2018) found that TCs have slowed down in translational speed by 10% in the last half century and continue to do so, which will correspond with an increase in related TC accumulated rainfall affecting U.S. coastal communities. I utilized the devised risk index to assess the effects of anthropogenic climate change on hurricane risk in U.S. counties…

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• Meteorology