Since I was young, I have always been interested in hurricanes. I became increasingly interested after witnessing hurricanes taking place, specifically Hurricane Irene and Hurricane Sandy in New York, when I was in elementary school. Instead of being scared of these natural disasters, I wanted to learn more about them. I followed the news of every major hurricane that impacted the United States, eager to know the most that I could about each hurricane. When I was presented with the opportunity to conduct my own research through my school’s Advanced Science Research class, I immediately knew that I would be researching something related to hurricanes. As I prepared for this class by completing the summer project, which was to read ten research articles of our choice on any topic, I noticed a trend in the articles concerning hurricanes. Most of them were about the effect of climate change on hurricanes, specifically how climate change was making hurricanes stronger and more dangerous. After reading these articles, I settled on the idea of researching the impact of climate change on hurricanes. My teacher then suggested that I look into climate modeling, as this was a realistic way to research hurricanes … After securing mentors to help me with my project, we decided to use the Columbia Hazard (CHAZ) Model to simulate hurricanes under conditions representative of projected future temperatures …

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…

Climate change impacts all people living on the Earth. The El Ni�o Southern Oscillation (ENSO) is a system which influences the climate around the globe. For this reason, it would be helpful to create a procedure for predicting ENSO each year, allowing the population to understand and prepare for a potential climate in their area, months in advance. This study developed a procedure to create predictions of ENSO every year. This procedure is simple, using basic statistics and computer science to create forecasts more accurate than those currently existing. Additionally, the study helped specify the relationship between the pressure systems surrounding the Pacific and ENSO, assisting in creating stronger predictions and allowing us to better understand the phenomenon.

Climate change involves complex interactions and changing likelihoods of diverse impacts (IPCC, 2014). In recent decades, changes and variations in climate impacting global agriculture sustainability and food security has been an important concern for our Earth family. Based on my summer internship in the Global Environment and Natural Resources Institute (GENRI), George Mason University, I was luck having the opportunity for working on analysis of climate change and assessment of climate impacts on agriculture under the supervision by my research mentor.

On October 29, 2012, Hurricane Sandy made landfall and caused widespread damage along the eastern seaboard. Although it had a weak maximum wind speed of 75 mph, Hurricane Sandy led to a total loss of approximately 51.2 billion dollars. Upon watching the disturbing images of wreckage on television, I was overwhelmed with sadness and curiosity. When I heard news reporters claiming that Sandy was as extremely large in size but its wind speed was not very high, I was surprised that a seemingly weak hurricane could be so destructive. I then began to wonder the significance of hurricane size in determining the huge economic loss. After talking to my mother, an atmospheric scientist, and Dr. Lixin Zeng, an expert on hazard insurance, I learned that many empirical hurricane loss models solely rely on wind speed to determine the overall loss and ignore the role of size. I realized that I had the opportunity to discover something brand new. Inspired, I rifled through internet databases and statistical models to develop an economic loss model that uses a variety of predictors for hurricane loss.