Back to the Future:
Gaining New Perspective on Hurricanes through Data
Rescue and Environmental Seismology
byAndrew Keizo Ando
G5

Submitted For:
Graduate Essays in the Natural Sciences
Epitome: Applying modern analysis of hurricanes to historical seismic
data opens the door to new searches for old storms.
Word Count: Max 7,428

Contents
1 Past and Future Hurricanes

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2 More Than Just Earthquakes

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3 Can Old Data Do New Tricks?

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4 A First-Principles Microseism Model

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5 Telling the Story of a Storm

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6 Opening New Doors to the Past

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List of Figures
1

Hurricane Iselle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

2

Ship Tracks and Observational Bias . . . . . . . . . . . . . . . . . . . . . . .

4

3

Legacy Data Timeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Seismometer Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

115Characterization of Historical Instrument Responses . . . . . . . . . . . . . .

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6Digitized Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7

Microseism Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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8Hurricane Arthur (2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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9Generalized Storm Patterns from Microseism . . . . . . . . . . . . . . . . . .

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10

Hurricanes Three and Four (1938) . . . . . . . . . . . . . . . . . . . . . . . .

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Past and Future Hurricanes
In early August 2014 Hurricane Iselle, a Category Four storm, was barreling west acrossthe Pacific and making a beeline for the Big Island of Hawai‘i. In Hilo, news coverage
had been warning of danger from the approaching storm for days. I remember creatingcrisscross patterns of duct tape on the windows of my childhood home in Hilo while mymother checked and double-checked our two weeks of emergency supplies. However, I also
distinctly recall the sharp contrast between my family’s reaction and that of many older folks
in town. The typical unworried attitude amongst older members of the community stemmed
from experiences of other hurricanes on the Big Island and the belief that they invariably
turned away; this was just the way it was, the way it had always been.
Iselle kept on coming, but, luckily for all of us on the island, it weakened to a tropicalstorm by the time it made landfall. Nonetheless, it was an unprecedented event. It was
the strongest storm to make landfall on the island in recorded history, and it brought over
12 inches of rain to Hilo and winds that gusted to 90 miles per hour atop Mauna Kea.
Between de-roofed houses, widespread power failures, and devastation of crops, Iselle didover $125 million in damages. Experiencing the storm itself was significant, but what was
especially striking to me was the difference in reactions along generational lines, highlighting
rapid change brought by a changing climate and the increased uncertainty which accompaniesit.
Of course, because climate change is a global phenomenon, it’s not just Hawai‘i which isfacing this uncertainty, but rather every coastal city on Earth. Just in the United States,
127 million people—more than 40% of the population—live in coastal counties and cities
vulnerable to hurricanes and the havoc they can wreak (Cutter et al., 2007). The frightening
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Figure 1: (left) Satellite image of Hurricane Iselle as it moved west across the Pacific towards
Hawai‘i. Image credit: NASA Earth Observatory. (right) Locals surveying damage anddowned trees on the Big Island of Hawai‘i after Hurricane Iselle. Image credit: Honolulu
Star Advertiser.
prospect of the next Katrina or Sandy drives many efforts to increase public awareness andbuild more resilient communities, and climate change is only increasing the sense of urgency(Knutson et al., 2021). However, these preparations are made more difficult by uncertainties
over how exactly hurricane behaviors are changing (Walsh et al., 2016): Are they getting
more frequent? Longer? Stronger? Affecting different areas? Or some deadly combinationof these?
At the most fundamental level, understanding changes in hurricane behavior is approached through looking for trends, that is, reviewing historical hurricane activity andlooking for differences from decade to decade. The most definitive historical record of hurricane activity is the National Oceanic and Atmospheric Administrations Hurricane Database,or HURDAT, which covers activity in the Atlantic Ocean from 1851 to present (Landsea
et al., 2015). Naturally, due to the long time span covered, HURDAT is compiled using datafrom a variety of sources and observation methods. Generally, the observations can be split

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into two broad eras: the era of satellite observation starting in the 1960s and 1970s and thepre-satellite era during which the dominant source of data is ship logs.
Satellite data was a revelation for meteorologists and storm watchers. Prompted by
NASA’s needs for high-accuracy weather forecasts to determine safe launch windows for the
space program, the first weather satellite was launched in 1960. By the end of the decade,the Television Infrared Observation Satellite program had launched nearly a dozen satellites,
and no hurricane anywhere on Earth was going to escape their combined fields of view. This
sharply contrasted with the state of observation in the preceding decades. Without an eye inthe sky, the only way to know of the existence of a storm was if it approached shipping lanes
and caught some hapless vessel in the strong winds, driving rain, and rolling seas. Reports
from this ship would allow for diversion of other shipping to avoid the storm, improving
overall safety. However, because shipping lanes only cover a fraction of the vast ocean, it
is likely that some storms were never encountered by a ship and are thus missing from therecord (Vecchi and Knutson, 2008).
Concerns surrounding completeness of the record in the pre-satellite era have prompted
several HURDAT reanalysis efforts. These studies conducted more thorough searches ofhistorical weather data not previously incorporated into HURDAT and made adjustmentsthat accounted for the calibration of historical meteorological equipment, revealing several
previously unrecorded storms (e.g., Landsea et al., 2015). Even so, the great majority of observations are from ships and thus highly dependent on historical trade routes. For example,
more storms were observed in the Caribbean when more shipping started traveling throughthe area after the Panama Canal opened in 1914 (Figure 2). Studies analyzing the biases
based on the density of these ship tracks indicate that a few storms per year are still likely

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Figure 2: (left) The tracks of all storms in the Atlantic from the satellite era, 1966 to 2006.
(middle) Locations where observations of storms were made in the period from 1878 to
1914, prior to the opening of the Panama Canal. Areas with warmer colors (red) have more
observations and those with cooler colors (blue) have fewer observations. White areas haveno observations. (right) Same as the previous panel for the period from 1915–1945 after the
opening of the Panama Canal. Note the greater density of observations in the Caribbean.
All modified from Vecchi and Knutson (2008)
missing during the first half of the 20th century (e.g., Vecchi and Knutson, 2008).
A few missing storms per year may not seem significant, especially if they were small
or far out to sea. However, consider that, on average, about ten storms develop each year
in the Atlantic and Gulf of Mexico, and a few missing storms a year adds up to quite asignificant error of 10–20%. This level of uncertainty becomes particularly worrying in thecontext of defining a baseline behavior for oceanic storms during the early part of the 20th
century, prior to the marked acceleration of warming in the 1970s. For example, missing dataprior to the 1970s may result in falsely attributing an increase in the number of storms after
this time to a changing climate rather than to issues with catalog completeness. Therefore,among climate modelers, there is motivation to seek out novel data which can complement
historical catalogs and increase catalog completeness, thereby increasing the certainty inboth historical trends of storm activity and predictions for future storm behavior. Perhaps
surprisingly, a prime candidate for such a data source is the collection of historical archives

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of seismologic recordings, that is, ground motion data. If successful, use of these records tostudy hurricanes could represent a win-win for climate scientists and seismologists looking
to secure resources for data rescue.

More Than Just Earthquakes
When people hear that someone is a seismologist, their minds tend to jump to earthquakes.
This is not unwarranted; the detection and characterization of earthquakes is indeed thebread and butter of seismologists. However, because one can never know when an earthquake
may strike, ground-motion data is continuously recorded all over the world and has been since
the late 1800s (Richards and Hellweg, 2020).
As a result of constantly monitoring the way the ground moves, seismologists record
not only earthquakes, but many other signals related to both natural and anthropogenic
phenomena. Any process that takes place on Earth wherein force is applied to the groundcan be recorded on a seismometer. This broader view of seismic data as a “pulse of the
Earth” means that all sorts of processes may be studied by looking at seismic records. On
the anthropogenic side, football fans celebrating a touchdown can be distinguished from
dancing concertgoers (Malone et al., 2015), reductions in road and foot traffic can be used
to assess COVID lockdown compliance (Lecocq et al., 2020), or the size of nuclear explosions
can be calculated (Gutenberg, 1946). On the natural side, vibrations from volcanic eruptions
can be used for monitoring (Shelly et al., 2007), wind causing trees to sway and water flowing
through rivers can be quantified (Smith and Tape, 2019), and glacier calving can be detected(Amundson et al., 2008). Among all of these, one of the earliest and most prevalent non-5earthquake signals to catch the eye of seismologists was a nearly constant background motion
with fairly coherent and consistent shape, dubbed “microseism” (Gutenberg, 1931).
It was not long before seismologists linked microseism to low-pressure weather systems
over the ocean, i.e., oceanic storms (Lee, 1935). However, there was debate over how exactly
a storm over the ocean was generating signals recorded on land, sometimes thousands of miles
away. Perhaps the most commonsense explanation was the initial one: ocean waves stirred
up by storms were crashing against steep coasts and causing the Earth to shake (Gutenberg,1931)—after all, anyone standing near a steep cliff with the roaring sea below can certainly
attest to the slightly disconcerting but regular shaking caused by the breaking waves. While
this explanation had its proponents, observations of microseism in places where there were no
cliffs and waves tended to gently lap up on beaches with gradual slopes necessitated a revision
or expansion of this theory. These changes came about when it was recognized that ocean
waves transferred their energy to the solid Earth in any sufficiently shallow water, rather
than only at steep cliff faces (Longuet-Higgins, 1950). In this new theory, transfer of energy
from ocean to Earth is achieved through pressure variations at the seafloor. While seemingly
less intuitive than waves crashing on the shore, this mechanism is also quite elegant. Imagine
the increase in pressure you feel when diving into a swimming pool, even under just a few feetof water. Similar to this phenomena, the change in the amount of water from wave trough tocrest above a point on the seafloor is enough to cause the Earth to “feel” the pressure pulse,a signal that travels through the ground and is subsequently recorded on land as microseism.
With this understanding of how ocean waves transfer energy into the Earth, all sorts ofparameters could be investigated. Changes in microseism reflect changes in the shape ofocean waves, that is, their height and their frequency—how many oscillations occur in one

6second. Additionally, networks of seismometers spread around the world allow for assessmentof the global distribution of these changes. Thus, seismic data can be used to paint a picture
not only of the global ocean wave climate, but how it has changed over decades (Aster et al.,2008). Other processes which change ocean wave behavior such as ice coverage (Grob et al.,2011), and hurricanes (Ebeling and Stein, 2011) have also been effectively investigated using
microseism.
Studies on the sensitivity of microseism to oceanic storms have had a history of boom,
bust, and boom. Spurred on by military interest in more accurate wave and weather forecasts to facilitate amphibious landing operations during the Second World War, identifying
distant storms using microseism was a topic of great interest during the 1940s and 1950s(Leet, 1949). At this time, microseism were seen as having great potential as a more costefficient and longer-ranged alternative to expensive and line-of-sight-limited aerial weather
reconnaissance conducted by long-range patrol planes leftover from the war. However, thesemicroseism analyses were somewhat limited by the methods of the day for extracting frequency1 information. Because the frequency of the microseism—i.e., how much of a wave
passes (or how many waves pass) by a fixed point in the time span of one second—is related
to the shape of the related ocean waves, it is a crucial parameter for characterizing microseism and hence storm behavior. Efficient ways of extracting frequency information were
not developed until the late 1960s (Cooley and Tukey, 1965), and even then, widespread andsystematic use would have to wait for computational developments to catch up. By thistime, satellite observation was widespread and the identification of storms via microseism
1Here, and throughout the rest of this essay, we shall understand frequency to be the characteristic ofa wave (whether seismic or oceanic) measured in Hertz, number of oscillations per second, rather than the
more general meaning of how often an event occurs.

7was seen as both archaic and unnecessary.
Today, microseism studies are seeing something of a renaissance. Modern signal processing techniques and computer analysis have allowed new levels of details to be extracted
from the pulse of the ocean represented by microseism. Not only can microseism observestorms and sea ice when cloud cover or darkness stymies satellite observation, but the factthat the microseism is physically coupled to the ocean allows greater detail resolution to
be achieved on both global and local scales (Gerstoft and Tanimoto, 2007). For example,
while satellites can determine the visible extent of a storm, microseism allows for assessmentof the strength of the winds and waves within. Additionally, with climate change and thenature of its effects an ever-present and often-inscrutable question in science, many haverecently seen a golden opportunity to examine physical processes related to climate changeusing long-running analysis of microseism (Figure 3). In particular, changes in ocean waves
over time have been investigated at global scale with digital seismic data which dates backto the 1980s (Aster et al., 2008), as well as on local scale using select rescued paper datafrom a station in California dating back to the 1930s (Bromirski , 2023).
This resurgence in microseism research has increased both the level of detail with which
processes are viewed and the temporal scope over which they are investigated. However, theparticular gap of applying modern processing to oceanic storm microseism from historical
recordings has yet to be bridged. This is likely the result of two salient challenges, both ofwhich arise out of the nature of the data. First, the level of detail that older instruments
were capable of recording must be determined in order to determine what type of modern
computational analyses can be supported. Second, while analyses will take advantage of
decades of advances in computers and signal processing, they should be possible to perform8

Figure 3: Timeline showing the eras of historical and digital seismic recording as well as
the global average surface temperature anomaly (red) and the estimated number of storms
missing from historical catalogs (blue) estimated by Vecchi and Knutson (2008). Note theacceleration of warming seen after 1970, and the coverage of times where storms may be
missing by historical seismic data.
with just a single station and be based on first principles because old data may be sparseand its fidelity unclear.

Can Old Data Do New Tricks?
To understand how the detail recorded in seismograms has changed over the years, one mustfirst understand how the data was recorded and how methods for doing so have evolved. In
most basic form, a seismometer is a large mass suspended in a frame. When the groundmoves, momentum causes the mass to stay in place while the frame moves with the Earth.
Measuring how the position of the mass changes with respect to the frame is analogous toa measurement of how the Earth is moving (Figure 4). Isolating a mass from the Earthitself is a theoretically beautiful and elegant concept. In a perfect instrument, the mass

9

would magically float in the exact same position, completely independent of its surroundings,
remaining unaffected as the Earth and frame moved around it. However, in the real world,the mass must be suspended via a physical connection, most typically a spring or systemof springs. Imagine holding a spring with a bowling ball hanging from the end of it; if you
rapidly move your hand up and down, the ball will remain stationary, but if you move slowly
enough or far enough the ball will move with you. A seismogram is imperfect in exactly thesame way. Much work has been undertaken in the century since the beginning of instrumental
seismic recording to reduce this imperfection as much as possible. In particular, leaps andbounds of progress have been made in developing more effective ways of both isolating themass from the frame and measuring its motion (Dewey and Byerly, 1969). The main resultof the decades of progress in seismic instrumentation is what is referred to as a broadband
response (Steim, 1986). This means that a seismometer will faithfully record motion at avery wide range of frequencies, i.e., rapid (high frequency) oscillations will not be any moreor less amplified in the recording than slower (low frequency) oscillations.
To characterize storms using microseism, it is crucial to accurately measure the frequencyof the microseism oscillations; the microseism frequency is related to the frequency of theocean waves and to the speed of wind generated by a storm. Therefore, before undertaking
such characterizations with historical data, it is of paramount importance to understandhow the non-broadband response exhibited by historical instruments may affect the data.
In short, one must understand how ground motion at different frequencies is recorded on
historical instruments in comparison to modern instruments. To determine this, one ideallywould have both a modern and historical instrument recording in the exact same place atthe exact same time. In this situation, determining the frequencies for which historical10Figure 4: (a) Diagram of a simple seismogram with a mass suspended from a frame by aspring. (b) Upwards ground motion moves the frame up (gray arrow), and the spring extends
as the mass stays in place (white arrow). (c) Same as (b) for downwards ground motion.
Note the increase in the distance from frame to mass from (a) to (b), and the decrease from(b) to (c).
instruments are robust would simply be a matter of comparing the recordings from the two
instruments side by side.
While having operational historical instruments, let alone operating them alongside modern instruments, sounds far-fetched, such a setup is not nonexistent. One of the rare placesin the world where this occurs is in the small one-room museum of the Albuquerque Seismological Laboratory (ASL) where a full set of operational half-century-old instruments sit.
After finding a few staff scientists at the ASL open to the challenge of figuring out how to
get a computer to read output from an instrument made in the 1950s2 , I made a trip to2

I extend my thanks Dr. Adam Ringler and Dr. Robert Anthony for their enthusiasm and help.

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Albuquerque to put a modern instrument alongside the historical seismometers and record
several weeks of data.

Figure 5: Seismic power at different frequencies for both a modern instrument (black) and ahistorical instrument (gray) recording at the same time. The area in which the two instruments agree, i.e., record ground motion in the same way, is bounded by the dashed lines.
Analyzing the data that came from this experiment allowed me to directly address therobustness of the historical instruments. Both instruments were subjected to exactly thesame ground motion. Therefore, for frequencies where the historical instrument is robust, thetwo recordings should look identical. Examining the power levels at different frequencies of
motion in the two recordings revealed that these instruments agree well between frequencies
of 0.03 Hz and 0.3 Hz (Figure 5). Crucially, ocean swell and the microseism that arises
from it have frequencies of between 0.05 Hz and 0.2 Hz, within the range where data from
historical instruments can be trusted.
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Figure 6: Seismic power extracted from digitized historical records from Harvard, MA, from1938 to 1940. Note the increase in power observed during the winter months caused byhigher microseism levels that result from the stormy North Atlantic.
In addition to challenges related to instrument behavior, working from legacy seismic
data involves complications arising from the transfer of data from a physical media, i.e.,large-format photographic paper, to a digital format readable by computers (Richards andHellweg, 2020; Ishii and Ishii , 2022). While the field of legacy seismic data rescue faces
several challenges including funding, standardization, and distribution (Hwang et al., 2020),
several collections of scanned—and in rare cases digitized3 —seismograms do exist.
The collection of seismograms that covers the 1933 to 1954 operations of the Harvard
Seismographic station in Harvard4 , MA, is among those scanned archives with significant
portions digitized5 (Ishii et al., 2015). Because this data is already in a format readily readable by computers, an initial analysis to verify sensitivity to the microseism can be made.
Being located in the Northeast, the station at Harvard should be sensitive to the seasonal3

In legacy seismic data rescue, scanning refers to obtaining a digital image of a paper seismogram, whereas
digitizing refers to tracing lines from the images to get digital data representing ground motion as a functionof time.
4
Perhaps confusingly, a small town located about an hour west of Cambridge, MA.
5
I would like to thank Hiromi Ishii whose tireless efforts were indispensable in scanning and digitizing
data from Harvard.

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patterns of waves in the North Atlantic Ocean. Namely, the stormier winters should resultin higher microseism levels than in the summers. I assessed the seasonal microseism levelby calculating the seismic power for each day of digitized data for 1938 through 1940. This
analysis revealed a strong seasonal trend (Figure 6), verifying the sensitivity of the legacy
data to microseism and the ocean. In other words, the signals are there, and the remaining
piece is the development of analytical tools to parse them.

A First-Principles Microseism Model
As opposed to the multinational global standardized seismic networks of today, historical
data were generally recorded at independent seismological observatories. As a result, thesedata typically remain archived with these independent operators, meaning it is relatively
easy to obtain large chunks of data for a single station, e.g., data from Harvard. However,significant complexity is added to data rescue efforts when other stations and archives are
considered. Thus, a first-principles analysis, which can be considered at a single station, is
crucial in efforts to analyze historical microseism. In this way, early efforts can be undertakenwith data from a single station. Later, when momentum for data rescue has been built up andresources to involve more archives have been obtained, greater resolution will be gained frommultiple-station analysis. Additionally, by illustrating the minimum of complexity needed
to characterize microseism storm responses, this first-principles analysis will highlight themost important factors at play in a complex system of atmosphere-ocean and ocean-Earthinteractions.
In simplest form, modeling microseism involves representing the transfer of energy from14

Figure 7: Cartoon showing the possible paths of energy from an oceanic storm (gray cloud)to a seismic station on land (black triangle). Energy can travel as oceanic swell (blue arrow),
as vertical pressure pulses connecting the sea surface to the solid Earth (purple arrow), andas seismic waves (red arrow). Points where energy from the ocean is transferred to the solid
Earth are marked as a Microseism Excitation Region (MER).
wind in a storm to swell in the ocean, and then again to seismic waves in the Earth. There
are two general pathways which this energy can take (Figure 7), and both are consideredhere. In both these pathways, winds from the storm whip up ocean waves, and energy fromthese ocean waves causes pressure variations at the seafloor which transfer the energy into
the Earth. The two pathways differ in where the transfer of energy into the Earth occurs. In
the first pathway, referred to here as the far Microseism Excitation Region (MER), energy
travels straight down to the seafloor below the location of the storm and then travels as
seismic waves. In the second pathway, referred to here as the near MER, the energy travels
as oceanic swell along the sea surface for some time before transferring to the solid Earthat some location between the storm and the coast, e.g., the continental shelf or in shallow
water near the shore. Characterizing how energy propagating via these two paths manifests
itself at a seismic station is crucial to understanding the effects of these different paths.
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My first-principles microseism model uses fundamental relationships that govern the
propagation of ocean waves and seismic waves to predict what the microseism signal observed at a seismic station will be, based on the maximum windspeed and storm track for astorm. In particular, based on the maximal windspeed of a storm at a given time, the model
calculates the speed of the ocean waves. To do this, the speed ratio between wind and rain
is required. For example, a speed ratio of 0.3 would mean that the waves travel at 30% the
speed of the wind. Combining this information with the known location of the storm andthe seismic station along with seismic wave speed allows for the calculation of the total time
it takes for the energy to go from storm to station for both pathways. For the far MER,
this simply involves calculating the distance from the storm to the station and dividing thatby the speed of seismic waves. For the near MER, it involves computing the total length oftime it takes to travel two legs: first, the ocean waves to travel towards the seismic station
until they reach the point where the depth of the ocean is 50 meters; second, they travel theremaining distance to the station as seismic energy.
Because the speed and frequency of oceanic waves are fundamentally related (Ardhuin
and Orfila, 2018), the frequency can also be calculated from the speed of the ocean waves.
Combining this information with the time of arrival at the station (calculated earlier) meansthat a prediction can be made for when microseism of a given frequency will be observed.
Additionally, by working under the assumption that waves with greater height are generated
by stronger wind (Fry, 1967), a prediction of what the microseismic power is can be madeby scaling the magnitude of the predicted observations with the strength of the wind fromwhich they originated.

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Telling the Story of a Storm
As with any model, it is prudent to make sure the results are reasonable before using it tomake any conclusions about the real world. Therefore, before the first-principles microseism
model is applied to historical data, I tested it to make sure that its predictions capture
the main features of microseism observed in the real world. For this study, doing so means
using modern microseismic and hurricane data where there are relatively few uncertainties
in either the behavior of the instruments or the storms themselves. In particular, I obtained
storm data for the Atlantic from HURDAT (Landsea et al., 2015) and seismic data fromthe modern seismic station in Harvard, MA, for the time span from 2010 to 2022. There
are almost 250 storms in this record, and using the first-principles model, I calculated themicroseismic prediction for each of these and compared them to the observations at Harvard.
Crucially, this work also allows us to map what the wind and ocean wave speed ratios areat different locations.
To understand the overall results of applying the model to modern data, it is helpful toconsider a single storm. Hurricane Arthur of 2014 is an ideal candidate for this demonstration because of its proximity to the U.S. East Coast, relatively high strength, and temporalseparation from other storms (Figure 8a); if the model cannot predict the microseism response from Arthur successfully, there is little point in going further. The observations of
microseism from Hurricane Arthur should be examined before comparisons are made to predictions from the model. I extracted both the frequency and the power of the microseism as
a function of time from the seismic data recorded at Harvard (Figure 10b,c). The frequency
slowly decreases and the power slowly increases starting about July 2nd, which continues
for several days. This is followed by a sharp increase in power and decrease in frequency
17

Figure 8: (left) Map showing the track of Hurricane Arthur (red circles) and the locationof Harvard, MA (black triangle), over topography and bathymetry, i.e., ocean depth, ofthe U.S. East Coast. The strength of the hurricanes is indicated by the size of each red
circle. (top right) Observed power of microseism at Harvard from Hurricane Arthur (black),
the prediction of microseism generated with the far MER (blue markers and line) and near
MER (red diamonds and line), and the combination of the two predictions (purple line).
(bottom right) Same as the previous panel for the frequency of the microseism. Note thatthe predictions are not combined for the frequency, because while power is summative,
frequency is not.
taking place on July 5th. Consequently, the power decreases and the frequency increases
back towards baseline with a rate of change similar to the initial changes.
Running the microseism model yields a wind-to-wave-speed ratio of about 0.3. Overlaying
the resultant microseism frequency and power predictions onto the observations reveal a close
match (Figure 8b,c) with the near and far MER explaining different parts of the observations.
The far MER explains the gradual decrease in frequency and increase in power initially seen;
the near MER prediction has a lot of energy arriving at once and causing the sudden decreasein frequency and increase in power seen on July 5th. The difference between the two MERs
is largely due to differences in speed of ocean waves and seismic waves. Ocean waves aremuch slower, and therefore the energy reaching the station via the near MER is delayed
18and overlaps itself as a result of movement of the storm and differences in speed arisingfrom the increasing windspeed. Additionally, the relative contributions from the two MERs
that yield the closest match to the observed power when added together are calculated. For
Arthur, the contribution from the near MER to the observations is 70% and that from thefar MER is 30%. Altogether, this result illustrates not only that microseism response canbe reasonably modeled for a single station, but also that the model can be used to gain real
insight into physical processes, for example the efficiency of generating waves from wind, andthe importance of different microseism pathways.

Figure 9: Map showing the variations in the wind-to-wave-speed ratio as a function ofgeographic location found from analysis of all storms from 2010 to 2022. Note the higher
ratio indicating more efficient transfer of energy from atmosphere to ocean in the middle of
the Atlantic.
When considering the model fits for all of the storms from 2010 to 2022, two broader
patterns in storm behavior that agree well with first-order interpretation of the physical world
19

emerge. First, mapping the wind-to-wave-speed ratio across the Atlantic reveals higher
values in the middle of the ocean and lower values close to shore and in the Caribbean(Figure 9). Physically, this can be interpreted as more efficient transfer of energy fromatmosphere to ocean in large unobstructed tracts of open sea where there are no landmasses
to slow the wind or interrupt the propagation of the newly formed waves. Second, if therelative contributions for the near and far MER are considered, it is found that the mostcommon partitioning is a 75% contribution from the near MER and a 25% contribution
from the far MER. This indicates that the near MER is generally more important, andtherefore generation of microseism is more common and more efficient at shallower depths,
i.e., 50 meters, than in the deep ocean. This again makes physical sense when considering
the amount of water that pressure changes have to travel through. If the seafloor is closer
to the ocean waves, then the excitation will be more efficient. Overall, the application of
the model to modern data not only successfully demonstrates the viability of this simple
processing, but also reveals information about the physics of the natural world that can beused to improve future iterations of the analysis.

Opening New Doors to the Past
Having developed a model which is compatible with a single station, and verified that historical data from that station is sensitive to microseism, the exciting work of bringing together
these two components can be undertaken. As a first step, two hurricanes which are currently
in the storm catalog from 1938 have been analyzed using records from HRV as the first proof
of concept. In particular, I compared Hurricane Three and Hurricane Four6 , both of which
6

Naming of tropical cyclones and hurricanes did not officially begin until 1953.

20

occurred in August of 1938, against all of the available seismic data from Harvard for thatmonth (Figure 10), and observed a close match.

Figure 10: (left) Map showing the tracks of Hurricanes Three (yellow circles) and Four(red circles) of 1938, and the location of Harvard, MA (black triangle), over topography
and bathymetry, i.e., ocean depth, of the U.S. East Coast and Caribbean. The strengthof the hurricanes is indicated by the size of each red circle. (top right) Observed powerof microseism at Harvard from Hurricane Arthur (gray), and the prediction of microseism
generated with the far MER (blue markers and line) and near MER (red diamonds andline). Note the gaps in the observations where data was missing. (bottom right) Same as
the previous panel for the frequency of the microseism.
There are two notable things about this analysis. First, it is clear that there are gaps inthe seismic data. This is because data was either not kept in the first place, or deteriorated
to the point of being unrecoverable over the near century over which it was stored (Ishii
et al., 2015). Nonetheless, the storm microseism signals are still visible with a few days
worth of gaps. If signals are visible even with gaps, the prospects of searching for storms
using archives that were better kept are very promising. This is especially true in light ofthe overall goal of supplementing existing storm catalogs; so long as large gaps on the orderof weeks are not present during every window where there is a missing storm, microseism

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analysis will make a significant contribution. Moreover, statistical analysis of the gaps canbe performed to determine the new lower uncertainty for the resultant storm catalogs.
The second notable conclusion involves the model performance. It is important to notethat the model was run using the average wind-to-wave-speed ratio for the Caribbean fromthe 2010 to 2022 analysis. That is, no tuning or fitting of the model parameters to thehistorical observations was performed. Thus, the good match of the model predictions to theobservations is a testament to the applicability of a model tuned on modern data to historicalseismic data. Importantly, this also indicates that improvements made to the model using
high-fidelity modern data will also improve performance when applied to historical data.
While this analysis only consists of a few storms, it is a good indication that the current
model makes up a solid groundwork for future systematic searches for storm signals. By creating lists of storms from historical seismic data and contributing these to the current storm
inventories, the overall historical record can be moved towards completion. This promise
is not only good news for this particular science question, but also for the preservation of
legacy data overall.
Many collections of legacy data are not secure. These data are on large, heavy sheetsof paper that make for bulky storage headaches, especially for extensive collections where
seismograms can number in the millions. Naturally, storage costs are not insignificant. Thus,
many archives lead a tenuous existence, where the uncertainty of scientific funding means
the risk of having to throw out data is always lurking around the corner (Hwang et al., 2020).
Studies of uniquely large earthquakes (e.g., Kanamori and Cipar , 1974) and nuclear tests(e.g., Gutenberg, 1946) recorded only on historical data have long been the main use cases forthese records. However, because these are relatively short-lived discrete events, discussions

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of triage in the form of throwing out any records without nuclear or earthquake signals
have been common (Richards and Hellweg, 2020). This work provides evidence that all of
the data—given that microseism is an ever-present signal—can be used to answer scientificquestions relevant to the existential threat of climate change (Walsh et al., 2016). Not only
does this make a case for wholesale preservation of this valuable quantitative record, but also
shows its utility in making contributions to solving problems that guide funding decisions(National Academies of Sciences Engineering et al., 2020).
While demonstrating the value of legacy seismic data for improving storm catalog completeness is crucial for giving data-rescue efforts momentum, the impacts of preserving suchdata are much larger. Seismology is the pulse of the Earth and records many physical processes beyond the earthquakes which it is usually known for. In a similar vein to this study,
legacy seismic data can be used to paint a more detailed picture of Nor’easters and wave climate over the past century. It can also be used to constrain groundwater levels (e.g., Lecocq
et al., 2017), reanalyze volcanic risk (e.g., Fehler , 1983), and reconstruct ocean temperature(e.g., Wu et al., 2020) over similarly long timescales. Having quantitative information thatcovers such long timescales is crucial to understanding most Earth processes, since they
operate over similar periods of time. Certainly, data can slowly be amassed day-by-day bycontinuing our current observational campaigns into the future, but the rate at which we gain
utile data could be massively increased through leveraging the value of historical archives.
Decades of critical information is already there, but it has sat stagnant because heretofore
we have not had the techniques to extract it.
In short, my work shows the viability of historical seismic data reanalysis for studying
problems outside of the traditional scope of seismology. It not only lays a solid groundwork

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for similar and more systematic future studies, but also opens up a new field at the crossroads of historical and environmental seismology. This new field would acknowledge andtake advantage of two facts: first, seismology is a quantitative record of almost all physicalprocesses on Earth; second, it has a remarkably long recording history. If all of the available
data were rescued and leveraged against the host of problems which they have relevanceto, then seismology, natural science, and society would all benefit. In some ways, this isperhaps a call to a more efficient scientific future—one in which using modern tools to reuse
and recycle old data is more commonplace, allowing for extraction of all the knowledge wepossibly can from the significant body of work tirelessly acquired over previous generations.
And, at the end of the day, all of this means that Marty McFly and Doc Brown were right:
the best course forward is to go back to the future.

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