The project aims to fill in the historical catalog of hurricanes, before satellites were used, through seismograms. Tropical storms and local weather generate microseisms: tropical storms by gravity waves and local weather by wind, rain, etc. My goal is to distinguish the two of them based on their relationship between frequency and power.
After seven weeks of doing research, my project focus is getting narrower as I develop my own intuitions and hypothesis to chase after. Now my focus is on local low-pressure systems and how we can use seismograms to track them. Currently, I am using barometric data from over 100 stations to get a 6 hour stepped weather map, since none is available online at the resolution that I want.
Map of Normalized Pressure Over Northeast America on June 26th, 2014 at 8:30 pm (GMT). Red color depicts high pressure and blue color depicts low pressure.
The figure above shows a pressure map of a portion of Northeast America interpolated using over 100 stations. The low pressure zone is on the east coast and covers a large portion of the states.
Currently, one of the challenges I am facing is the absence of my graduate student mentor for the next three weeks. While I have colleagues in the lab with whom I can collaborate, I am learning to actively seek their input and engage in idea-sharing. Nevertheless, I am enthusiastic about this opportunity to foster greater independence and continue utilizing the workstation I have set-up.
Figure 1: Hurricane Bertha's Track, the color and size of the dots signify the windspeed of the eye of the hurricane. Seismic station HRV indicated with a black triangle.
The map displayed above depicts a hurricane I am studying: Hurricane Bertha in 2014. I made the plot using pyGMT (python Generic Mapping Tool). The color and size of the markers on the map indicate the speed of the hurricane in knots, while the markers themselves represent the hurricane's eye every six hours. Like numerous other hurricanes in the northern hemisphere, Hurricane Bertha originated at tropical latitudes and followed a west-northwestward trajectory.
An important aspect to consider is the location of the Harvard seismograph station (HRV), from which I am obtaining seismograms. As the hurricane traverses along the coastline, the strongest seismic signal is dependent on the hurricane’s relative location to the station and its intensity.
This week, I am working with seismograms from the Harvard Seismograph Station (HRV) located in Harvard, MA. The specific data type I used was BHZ (Broadband High-gain seismometer Vertical component): data collected from a broad range of frequencies in the vertical component (up/down). Before I am able to identify storms and low-pressure systems on historical records, I am locating hurricanes recorded by the HURDAT (HURricane DATabase) produced by NOAA (National Oceanic & Atmospheric Administration) on HRV seismographs in 2014, which is known to be a quiet year. Beyond the seismograms, I am using wind speed and hurricane distance to HRV from HURDAT and barometer readings from a deployed transportable array to get an idea of how hurricane signals change. All the data I am using is publicly available.
Since I am using many different sources of information to characterize a hurricane, it is important for me to remain critical of each result. It is natural to look for patterns and find connections. As a result, I will have to be cautious when making claims and ensure they are backed by evidence and logic.
This summer I am working with Professor Miaki Ishii and PhD student Thomas Lee to distinguish tropical storms from local weather using seismograms. Not only earthquakes can cause ground motion! Rain, wind, and snow can be detected, as well as ocean swells created by hurricanes. After a year of working with earthquake-caused seismology at UCLA, my world has been flipped since I am now filtering out high-frequency earthquakes from long-period hurricane signals. While I just started on the project this past Tuesday, my goal for the first half of the summer is to gain an intuition for filtering tropical storms/local weather in order to achieve my goal for the second half of the summer, which is to personalize the research project by adding my own ideas.