manuscript submitted to Geophysical Research Letters

1

Meteotsunami observed by the deep-ocean seafloor pressure gauge network off northeastern Japan

2

Tatsuya Kubota1, Tatsuhiko Saito1, Naotaka Y. Chikasada1, and Osamu Sandanbata1

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National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Ibaraki, Japan.

45Corresponding author: Tatsuya Kubota (kubotatsu@bosai.go.jp)

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Key Points:•

tsunami-like pressure signals with amplitudes of several hPa

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•

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A numerical simulation revealed that the signals were meteotsunamis related to anortheastward-moving atmospheric low pressure

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Deep-ocean pressure gauge array observation off NE Japan detected non-seismic•

It is necessary to consider effects of the atmospheric pressure to appropriately estimate
sea-surface height from seafloor pressure gaugemanuscript submitted to Geophysical Research Letters

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Abstract

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This study reports an ocean-bottom pressure gauge (OBP) network, S-net, captured

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meteotsunamis with amplitudes as small as a few cm, and investigates its validity and limitation

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for meteotsunami researches through data analyses and numerical simulations. On 1 July, 2020,

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S-net recorded tsunami-like signals, although no earthquake was reported. These waves,21

propagating northward with a velocity of ~110 m/s, were explained by an atmospheric low

22pressure with a maximum amplitude of −0.5 ± 0.1 hPa moving northeastward at a speed of 45–

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50 m/s. The maximum amplitudes of the sea-surface height during the low pressure passing were24

up to twice larger than those directly converted from the ocean-bottom pressure which ignores25

the effect of the atmospheric pressure. Our results suggest that the S-net with numerical26

simulations can detect the generation and propagation of meteotsunamis, which could not be

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achieved in the past when dense OBP networks were not available.

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Plain Language Summary

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Recent developments in deep-ocean tsunami observation networks using ocean-bottom pressure

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gauge sensors have been remarkable, which have an advantage for continuously monitoring the32

ocean. On 1 July, 2020, a deep-ocean observation network off eastern Japan, S-net, recorded

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small tsunami-like ocean waves. Although tsunamis are often excited by earthquakes, no34earthquake was reported at that time. We investigated the observed feature of these waves, which

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suggests that the waves were meteorological tsunamis, or meteotsunamis, originating from an

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atmospheric pressure disturbance. To investigate the behavior of these waves in detail, we

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conducted numerical meteotsunami simulations. The maximum amplitudes of the sea-surface

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height during the atmospheric low pressure passing were were up to twice larger than those

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expected from the observed seafloor pressure change without considering the contribution of the40

atmospheric pressure disturbance. We demonstrated that analyzing the data from the wide and41

dense array of S-net made it possible to understand the behavior of the meteotsunamis in detail,42

which could not be achieved in the past when only a few pressure gauges were available. The S-

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net's continuous monitoring of the seafloor pressure in the deep ocean will contribute to44

deepening our understanding of oceanography and meteorology.

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manuscript submitted to Geophysical Research Letters

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1 Introduction
Recently, deep-ocean tsunami observations using ocean-bottom pressure gauges (OBPs)

48(e.g., Aoi et al., 2020; González et al., 2005; Kaneda et al., 2015; Kawaguchi et al., 2015;

49

Rabinobich & Eblé, 2015; Tsushima & Ohta, 2014) have been developed. Use of these OBPs has

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contributed to our understanding of earthquake rupture processes (e.g., Kubota et al. 2020a;

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2021a) and tsunami propagation processes (Gusman et al. 2017; Kubota et al., 2018; 2020b;

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Saito & Kubota, 2020; Saito et al., 2010; Sandanbata et al., 2018; Tang et al., 2012; Titov et al.,53

2005). In response to the 2011 Tohoku-Oki earthquake, a dense OBP network consisting of 150

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observatories, called the Seafloor Observation Network for Earthquakes and Tsunamis along the55

Japan Trench (S-net), was constructed (Figure 1a, Aoi et al. 2020). Recent studies have revealed

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that S-net is capable of observing tsunamis at much higher spatial resolutions than was

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previously possible. S-net has started to be widely utilized for monitoring ocean waves related to58

earthquakes. One of the largest tsunamis so far recorded by S-net was that associated with the59

Mw 7.0 Off-Fukushima earthquake on 21 November, 2016 (Kubota et al. 2021b; Tsushima &60

Yamamoto, 2020, Figure 1d). Furthermore, it has been reported that much smaller tsunamis with

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amplitudes less than one centimeter related to the Mw 6.0 Off-Iwate earthquake on 20 August,62

2016 were observed (Kubota et al., 2020a, Figure 1c). S-net is also expected to be utilized for the63

real-time tsunami forecasts (e.g., Inoue et al. 2019; Suzuki et al., 2020; Tanioka, 2020; Tsushima

64& Yamamoto, 2020). In addition to earthquake-induced tsunamis, or seismic tsunamis, the OBPs

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can record other oceanographic phenomena, such as infragravity waves and internal tides (e.g.,

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Fukao et al. 2019; Tonegawa et al. 2018).

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Here, we report new observations of tsunami-like pressure change signals recorded by S-

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net on 1 July, 2020 (Figure 1b, 17:00–19:00 UTC). One of the most interesting aspects of these69

signals is that no major earthquake, which is the most common cause of tsunamis, was reported

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at that time (https://www.fnet.bosai.go.jp/top.php?LANG=en). We investigate the source of the71

observed "non-seismic" tsunami-like signals based on data analysis and numerical simulations.

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In Section 2, we summarize characteristics of the signals and compare them to the signals excited73

by earthquakes. Section 3 discusses its plausible cause. In Section 4, we conduct numerical74

simulations to clarify the cause of these tsunami-like signals and to discuss the generation and75

propagation processes in detail. Section 5 summarizes this research and discusses the potential76

use of S-net for other oceanographic and meteorological researches.

manuscript submitted to Geophysical Research Letters

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Figure 1. (a) Station map of this study. Names of prefectures are also shown. Yellow square

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shows location of Ofunato, where the coastal microbarographs are located. (b) Pressure

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waveforms recorded on 1 July 2020. The horizontal axis is the time on 1 July (UTC) and the82

vertical axis corresponds to the station latitude. The waveforms from the OBPs marked by white83

circles in Figure 1a are shown by thick lines. Data with low quality are plotted using gray lines.

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The tsunami-like pressure changes are denoted by red arrows. Pressure waveforms for (c) the85

2016 Off-Iwate earthquake and (d) the 2016 Off-Fukushima earthquake. Epicenters for each86

earthquake are shown by white stars in Figure 1a. Horizontal scale in Figure 1c is different from87

the other panels.

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2 S-net pressure gauge data
We analyzed the S-net OBP data on 1 July, 2020 to clarify the characteristics of the91

tsunami-like waves. We applied a bandpass filter with passbands of 100–3600 s to reduce noise

92(Figure 1b). We confirmed a wave train of pressure changes propagating to the north during93

17:00–19:00 UTC (Figure 1b), which were too small to be recognized if only a few observation94

stations were available. The wave train appears in the region off northern Fukushima and95

southern Miyagi prefectures, and disappears in the region off northern Iwate and southern

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Aomori prefectures. In contrast, a wave train propagating to the south could not be confirmed

97(34°N–37°Ν). The waveforms for the different water depth bins are also shown in Figure S1. Wemanuscript submitted to Geophysical Research Letters98

were unable to recognize waves using the OBPs installed in deeper waters (> 4000 m),

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particularly for the subnetwork installed in the outer-trench region (Figure S1c).

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We plot the distributions of the peak amplitude of the seafloor pressure and its arrival

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times in Figures 2a and 2b. The peak amplitudes are mostly a few hectopascals and tend to be102

large in the OBPs at shallower depths (< ~1500 m). From the arrival times, the apparent

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propagation direction is almost northward. Using an array analysis method called the semblance

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analysis (Text S1, e.g., Asano et al., 2008; Neidell & Taner, 1979), we estimated the propagation

105

azimuth of φ = 340° (measured clockwise from the north) with the speed of 117 m/s (Figure S2,

106details are described in Text S2). For simplicity, using the peak arrival timings from the four

107

OBP stations at depths between 1000 and 1500 m (S4N14, S4N18, S4N27, and S3N11, marked

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by white circles in Figure 2b), the apparent northward propagation velocity c' is calculated as c'109= 109.2 ± 3.6 m/s (black line in Figure 2c). These velocities correspond to the tsunami

110

propagation velocity at water depths of ~1000–1200 m (e.g., Satake, 2002).

111

Based on the pressure waveforms for which these tsunami-like wave signals are evident,

112

it seems that the dominant period T' is ~1000–1200 s. Considering the dominant period and the113

apparent propagation velocity, the wavelength in the north direction was inferred to be c'T' ~100

114

km. However, based on the earthquake-fault scaling relation of Wells & Coppersmith (1994), the115earthquake magnitudes that would generate such a large horizontal tsunami source dimension are

116

expected to be M ~7 or larger, although such a large earthquake was not reported. Therefore, this117

unexpectedly large horizontal extent of the tsunamis is inconsistent with the hypothesis of an

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earthquake as the generating source.

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We also compare the observed feature of these tsunami-like waves with those of the120

seismic tsunamis observed by S-net. The dominant period of the waves is almost comparable to121

the tsunamis associated with the Mw 7.0 Off-Fukushima earthquake (Figure 1d, Kubota et al.,1222021b; Tsushima & Yamamoto, 2020), although the maximum amplitudes of a few hectopascals

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are almost five times smaller than those for this earthquake (~10 hPa = 10 cmH2O, supposing

124

that a pressure change of 1 hPa is equivalent to a sea height change of 1 cm) and tsunami wave

125

trains propagating to both north and south directions can be recognized. On the other hand, the126

maximum amplitudes are similar to those for the Mw 6.0 Off-Iwate earthquake (Figure 1c),127

although the dominant periods for this earthquake were much shorter (~300 s, Kubota et al.,128

2020a). In addition, the dominant tsunami signals during this earthquake were observed at a few

manuscript submitted to Geophysical Research Letters

129

stations around the focal area (marked by blue arrow in Figure 1c), whereas the tsunami-like130

wave signals in 2020 are widely observed. These inconsistencies might suggest that the tsunami-

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like signals in 2020 have a different origin to the typical tsunamis generated by earthquakes.

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Figure 2. (a) Peak amplitudes and (b) arrival timings of S-net pressure records. The stations

135

marked by thick white circles are used for calculating the apparent wave propagation velocity.

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The pressure data in which the signal-to-noise ratio is low are indicated by small circles. The iso-137

depth contours are also shown. Solid and dashed contour lines are drawn at 1000 m and 500 m

138

intervals, respectively. The color scale of the sea depth is also shown. (c) Peak timings for the

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OBPs marked by thick white circles in Figure 2b and the apparent northward propagation140

velocity. Gray circles and black line denote the observed timings and its linear fitting. Those for141

the meteotsunami simulations are denoted by red circles and lines. In the simulated results, the142

moving speeds of the atmospheric pressure change are varied by 5 m/s intervals. Arrival timings

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in the simulation are aligned so that the simulated arrivals at S3N11 coincide with the144observations.

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3 A plausible cause of the pressure signals
Tsunamis are often caused by the seafloor crustal deformation due to earthquakes.
Besides, tsunami-like ocean waves are sometimes excited by meteorological phenomena. Thesemanuscript submitted to Geophysical Research Letters

149

waves are widely referred to as meteorological tsunamis, or meteotsunamis (Rabinovich, 2020).

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Meteotsunamis are generated by the interaction between atmospheric disturbances and water-

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wave propagation (e.g., Hibiya & Kajiura, 1982). One of the most important characteristics of152

meteotsunamis is that they are not accompanied by earthquakes or seismic waves, like the153tsunami-like waves reported in this study. In fact, from the weather map, a low pressure system154

is located around the northeastern Japan during the tsunami-like wave signals (18:00 on July 1,

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2020, black arrow in Figure S3b). Therefore, it seems that these tsunami-like pressure changes

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were likely induced by meteotsunamis. The significantly large horizontal extent of the tsunamis

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also supports this idea.

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The basic generation mechanism of a meteotsunami has been theoretically investigated159(e.g., An et al., 2012; Greenspan, 1956; Proudman, 1929; Saito et al., 2021; Seo & Liu, 2014).

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Meteotsunamis have been widely recorded by coastal tide gauges, which have enhanced

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meteotsunami research (e.g., Fukuzawa & Hibiya, 2020; Heidarzadeh et al. 2020; Hibiya &162

Kajiura 1982; Monserrat et al. 2006; Okal 2020; Rabinovich et al. 2020; Seo & Liu, 2014; Šepić

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et al. 2015; Williams et al. 2019). However, meteotsunami observations in the deep-ocean have164

been reported much less (Titov & Moore, 2021). In the next section, we conduct numerical165

simulations of meteotsunamis in order to confirm whether the observed wave train was due to a166

meteotsunami, and to investigate the behavior of meteotsunamis in the open ocean.

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4 Meteotsunami simulation in the region off eastern Japan169

4.1 Method

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Equations for meteotsunami propagation are given by adding an external force term

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related to the atmospheric pressure to conventional tsunami equations (e.g., Satake, 2002). In this172

study, we introduce a linear-long wave equation in Cartesian coordinates (e.g., An et al., 2012;

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Hibiya & Kajiura, 1982):

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!"
!#

!$

!&

+ !% + !' = 0
) !$

!"

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%( * !# + %( &( !% = −177%( * !# + %( &( !' = −

) !&

!"

!+!"#
!%
!+!"#
!'

,

(1)

manuscript submitted to Geophysical Research Letters

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where η(x, y, t) is the sea-surface height change and M(x, y, t) and N(x, y, t) are the horizontal

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velocity flux along the x- and y-directions, respectively. Parameter h is the seawater depth, g0 is

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the gravity acceleration (= 9.8 m/s2) and ρ0 is the seawater density. We suppose the seawater

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density to be ρ0 ~1020 kg/m3, so that a pressure change of 1 hPa is equivalent to a sea-height

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change of 1 cm (i.e., ρ0g0 = 1 hPa/cm). In the simulation, we use the GEBCO 2019 Grid184

bathymetry data. We set the spatial grid interval as 500 m and the time step interval as 1 s.

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4.2 Assumption of the atmospheric pressure
The wind condition at heights of ~500 hPa (altitude of ~5000 m) around the northeastern

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Japan showed a strong wind with ~40 m/s heading east to northeast (Figure S4). Similar

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situations were also documented in the past meteotsunamis (e.g., Heidarzadeh et al., 2020; Šepić

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et al., 2015; Vilibić & Šepić, 2017). Also, the microbarograph data of three stations in the

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Ofunato area, Iwate, showed that the low pressure with amplitude of ~−0.5 hPa passed through192

around this area (Figures 3a–3b). We estimated the propagation velocity and direction based on

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an array analysis (details are explained in Texts S1 and S3). We obtained the propagation194

direction was almost northeast (azimuth of ~40–70°) with a speed of ~40 m/s (Figure S5).

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manuscript submitted to Geophysical Research Letters

(b)

958

142°

Ofunato

(a)

957
961
960

OfunatoMassaki

Ryori

959
962

Tide gauge

39°

Massaki

961

Ryori
17

Aomori1819(c) Coastal tide gauge
Iwa

te

10
5
0
−5
Bandpass 120-3600s
−10
18
19

10
Mi

ya

gi

0
38.8°

Obs. Cal.

17

Atmospheric pressure [hPa]141.8°Height [cm]141.6°
39.2°

959

Time on 1 July 2020 UTC [h]

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Figure 3. (a) Locations of coastal barographs (colored inverted triangles) and coastal tide gauges

198(black triangle) in the Ofunato area, Iwate. (b) Time series of the atmospheric pressure observed

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by the barographs. Timings of the peak pressure depressions are marked by stars. (c) Time series

200of the coastal tide gauge at the station Ofunato. Bandpass filter with passband of 120–3600 s was

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applied.

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Therefore, as the atmospheric disturbance, we suppose a plane-wave of the low-pressure

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system moving northeastward (azimuth of φp = 40–70°, Figures 4 and S6–S8) with a moving

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speed of V0. Introducing the Cartesian coordinates in which the x- and y-directions coincide with206

the east and north directions, respectively, the spatiotemporal evolution of the atmospheric207

pressure is assumed as (e.g., Saito et al., 2021):

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209

(,-. (*, ,, -) = () (*, ,, -)/(-) = 0( exp 4− 5

% /01 2$ 3' 45/ 2$ 67% # :8% /:6 7 /(-),

(2)

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211

where P0 is the amplitude of the atmospheric pressure disturbance and L0 is the horizontal extent

212

of the plane wave. We assume L0 so that the apparent northward horizontal extent of the movingmanuscript submitted to Geophysical Research Letters213

pressure, Lapparent = L0/cosφp, equals ~50 km, based on the apparent wavelength in the north214

direction (~0.5 × c'T', e.g., Heidarzadeh & Gusman, 2021, Figure 2). Because it is unlikely that215

the atmospheric pressure suddenly increases at t = 0 s, we suppose that the moving pressure

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increases gradually over time with a time scale of T0 (An et al. 2012):

217
#

:

/(-) = 81 − exp[− 5; /:6 ]<.

218

%

(3)

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We suppose the duration to be T0 = 5400 s. We vary the moving speed V0 of the plane wave and221

its amplitude P0 to find optimal values for the two parameters by comparing the simulated and222

observed waves, particularly in terms of the apparent propagation velocity and the amplitude.

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In order to calculate the pressure change at the sea bottom pbot(x, y, t), we consider that224

pbot is given by the sum of the pressure changes due to tsunami peta(x, y, t) and the atmospheric225

pressure disturbance patm(x, y, t), as (e.g., Inazu et al., 2012; Saito et al., 2021):226(<5- (*, ,, -) = (=-, (*, ,, -) + (,-. (*, ,, -).

227

(4)

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229

Here, the pressure changes due to tsunamis are expressed as:

230(=-, (*, ,, -) = %( &( =(*, ,, -).

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(5)

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4.3 Results

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In Figure 4, we show the meteotsunami simulation result with φp = 70°, V0 = 45 m/s and236

P0 = −0.5 hPa, which explains best the observed pressure changes. Figure 4a shows snapshots of237

the atmospheric pressure (left panels), sea-surface height (middle panels), and the seafloor

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pressure (right panels). The sea-surface subsidence and the seafloor negative pressure

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disturbance propagates to the north as the leading wave. The amplitudes of the leading waves are240

higher in the region closest to the coast (marked by blue arrows in Figure 4a). The dominant sea-

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surface elevation follows the leading negative wave (red arrows). The wave front extends widely242

in the east-west direction corresponding to the region of the atmospheric low pressure (blackmanuscript submitted to Geophysical Research Letters

243

arrows). Seafloor pressure increases are confirmed only in the region near the coast. This is due

244

to the hydrostatic equilibrium, in which the pressure change by the sea-surface elevation is245

cancelled by the atmospheric pressure (gray arrows).

246manuscript submitted to Geophysical Research Letters247

manuscript submitted to Geophysical Research Letters

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Figure 4. Result of the meteotsunami simulation supposing φp = 70° with V0 = 45 m/s. (a)249

Snapshots of the simulations at elapsed times of 5400, 6600, 7200, and 8400 s. In each subfigure,250

the pressure changes due to the atmosphere disturbance (left), sea-surface height (middle), and251

sea-bottom pressure (right) are shown. (b) Comparison of the observed (gray) and simulated

252(red) bottom pressure waveforms. The pressure waveforms and the envelope waveforms are

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shown in left and right panels, respectively. (c) Comparison of the observed (black) and254

simulated pressure changes. Red thick , pink dash-and-dotted and dark red dashed traces are the255

simulated waveforms supposing P0 = −0.5 hPa, P0 = −1.0 hPa, and −0.2 hPa, respectively. (d)256Time series of the sea-surface height changes simulated from the optimum simulation results

257(blue traces). Light pink traces are the simulated pressure changes, which are identical to the red

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traces in Figure 4c. Gray traces are the atmospheric pressure changes.

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Figure 4b shows a comparison between the observed and simulated pressure waveforms

261(left panel). To visualize more clearly the characteristics of the apparent arrival delays of the262

wave packets, we plot its envelope waveforms (right panel in Figure 4b). The arrival timings of263

the peak amplitudes are explained well. We obtain the apparent propagation velocity along the264north-south direction as c' = 102.1 ± 5.5 m/s. This is consistent with that estimated from the265

observation (Figure 2c). When supposing different moving directions of the pressure

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disturbance, the observed arrival times were also explained (φp = 40° and V0 = 85 m/s, Figure S6;267

φp = 60° and V0 = 60 m/s, Figures S7; φp = 50° and V0 = 80 m/s, Figure S8), although the268

supposed moving speed was larger than that expected from the onshore atmospheric pressure269

observation (Figures S4 and S5) as well as those previously documented (~40 m/s, e.g., Šepić et270al., 2015; Titov & Moore, 2021). From these simulations, we conclude that these tsunami-like271

pressure changes are meteotsunamis excited by a moving low pressure system.

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We also examined the observed coastal tide gauge record at Ofunato, Iwate Prefecture273(Figure 3c, see also Figure S9). We found the amplitude took a large peak of ~10 cm while the274

atmospheric low pressure passes nearby. Although it is difficult to recognize the meteotsunami

275

signals explicitly only with tide gauges, because of the small fluctuations probably due to the276trapped waves or wave reflections from the nearshore bathymetry, the simulations reproduced277

the observed arrival time well.

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manuscript submitted to Geophysical Research Letters

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4.4 Uncertainty of the moving speed and amplitude of the atmospheric low pressure
When assuming northeastward-moving atmospheric pressure disturbances with slower

282

or faster speeds (φp = 70°, V0 = 20 m/s or 100 m/s, Figures S10 and S11), neither simulation

283

could explain the observed apparent arrivals and amplitudes. To evaluate the movement speed in284

further detail, we examined the apparent arrival delays of the meteotsunamis from the285

simulations with atmospheric pressures moving at different speeds (Figure 2c). When V0 is

286assumed to be 45 or 50 m/s, the peak arrival timings of the observed pressure changes are

287

explained (red solid lines), while the simulations with faster or slower speeds do not (thin red

288

dashed lines). This suggests that the movement speed of the low pressure region is V0 ~45–50

289

m/s. This value is consistent with the moving speed expected from the onshore atmospheric290

observation (Figure S5).

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Figure 4c compares the observed (black) and simulated pressure changes at292

representative OBP stations. The amplitude of the atmospheric pressure disturbance supposing

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P0 = −0.5 hPa explains the observed amplitudes well (red traces). The peak-to-peak amplitudes

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of the pressure changes for the observation and simulation are consistent. If we assume P0 = −1.0295hPa (pink dash-and-dot traces in Figure 4c) or P0 = −0.2 hPa (dark red dashed traces), then the296

simulated amplitudes are not consistent with the observations. If we assume a range between 80297

and 120% of the observed peak-to-peak amplitude at S3N11, the plausible amplitude range of298

atmospheric pressure disturbance is roughly estimated as P0 ~0.5 ± 0.1 hPa.

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300

5. Discussion

301

5.1 Generation mechanism of the meteotsunami event

302

The atmospheric observations (Figures S4 and S5) indicated the atmospheric pressure

303

propagated to the northeast, whereas the apparent propagation direction of the meteotsunami was304

almost the north (Figures 2 and S2). In order to see the reason for this difference, we conducted305

an additional meteotsunami simulation supposing the pressure disturbance moving northward (φp306= 0°, Figure S12). When supposing the moving speed of V0 = 110 m/s, the apparent arrivals of307

the observed pressure was explained well and the growth of the sea-surface height was

308

reproduced, as demonstrated by the simulations with northeastward-moving pressure disturbance309(Figure 3). Although this moving speed was unrealistic for the atmospheric observations, thismanuscript submitted to Geophysical Research Letters

310

speed was consistent with the apparent northward-moving speed in the simulations with the311northeastward-moving pressure, Vapparent = V0/cosφp (Figures 4 and S6–S8). In this region, the312water depth is almost uniform along the north-south, or coast-parallel, direction. Meteotsunamis313

are often generated when a pressure disturbance propagates parallel to the coast line (Greenspan,314

1956; Munk, 1956), due to the coastally-trapped edge waves. Our simulation result suggests the315

apparent moving speed along the north-south direction (Vapparent) is important for meteotsunami

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generation in the region off eastern Japan, rather than the actual speed and direction. This

317

interpretation is supported by previous theoretical investigations of the meteotsunami generation318

due to a pressure disturbance crossing the coast line (e.g., Vennell 2010; Thiebaut & Vennell

3192011). These studies suggested that when the apparent coast-parallel moving speed of the320

pressure disturbance matches the tsunami propagation velocity the tsunami heights are

321

effectively amplified by the multiple reflections of the waves between the coast and the322

continental shelf break.

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5.2 Importance of incorporating atmospheric pressure to estimate the sea-surface height from325

ocean-bottom pressure

326

Considering Eq. (4), the OBPs observe the pressure changes at the seafloor (pbot), but not327

the sea-surface height (peta). Using the simulation results, we calculated the time series of the328

sea-surface height changes (blue traces in Figure 4d). At station S4N27, the peak amplitude of329

the seafloor pressure change during the atmospheric pressure disturbance passing above this330

station was ~0.3 hPa, while the maximum pressure change due to the sea-surface height was ~0.7

331hPa. In other words, the maximum amplitude of the sea-surface height was, up to twice, larger

332

than that expected from the observed seafloor pressure, without considering the atmospheric333

pressure. This suggests the seafloor pressure changes pbot cannot be directly converted to the sea-

334

surface heights η, as often done in seismic tsunamis that are not affected by the atmospheric335

pressure (pbot = peta). For meteotsunamis, we should consider the effect of the atmospheric336

pressure to appropriately estimate the sea-surface height.

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338

6 Conclusions

339

This study revealed that the S-net seafloor OBP network can detect the generation and340

propagation of meteotsunamis off eastern Japan. We first summarized the characteristics of themanuscript submitted to Geophysical Research Letters341

observed records, and supposed that the most plausible sources of these pressure change signals

342

were meteotsunamis. We then conducted numerical simulations of the meteotsunami to confirm

343

whether these pressure changes were a meteotsunami. The simulation results reproduced the344

arrival delay when assuming a northeastward-moving atmospheric pressure disturbance (azimuth345

of ~70°) with a speed of 45–50 m/s and a maximum pressure depression of −0.5 ± 0.1 hPa. Our

346

simulations suggested that the apparent speed of the moving pressure system in the north-south

347

direction along the coast is important for meteotsunami generation in the Off-eastern Japan348

region. We also found that the change in the peak amplitude of the sea-surface height was larger

349

than that expected from the observed seafloor if we do not consider the atmospheric pressure

350

change at the sea-surface. This indicates that the seafloor pressure cannot be directly converted351

to the sea-surface height and we need to appropriately consider the contribution of the352

atmospheric pressure.

353

So far, meteotsunami observations have mostly depended on near-shore data recorded by354

coastal tide gauges or seafloor pressure gauges inside bays (Rabinovich, 2020); however, these355

regions are typically characterized by complex coastal site effects, making it difficult to study the356

generation and propagation processes of meteotsunamis. In contrast, deep-ocean OBP networks

357

are free from such near-shore site effects. Our study demonstrated that S-net can contribute to358

research on meteotsunamis and other meteorological and oceanographic studies, which are of359

impotence for Japan based on a recent study by Heidarzadeh and Rabinovich (2021).

360
361
362

Data Availability Statement
The S-net pressure gauge data is available at the website of the National Research

363

Institute for Earth Science and Disaster Resilience (NIED) (NIED, 2019;364

https://doi.org/10.17598/NIED.0007). The GEBCO2019 Grid bathymetry data, in which the grid

365

resolution of 15 arcsec, are available at366

https://www.gebco.net/data_and_products/historical_data_sets/#gebco_2019. The atmospheric

367

data of the Meso-scale model (MSM) (Figures S3 and S4) were downloaded at368

http://database.rish.kyoto-u.ac.jp/arch/jmadata/ (only available in Japanese), and are originally369

provided by the Japan Meteorological Business Support Center370(http://www.jmbsc.or.jp/en/index-e.html). The microbarograph data (Figure 3b) were371

downloaded from the website of Infrasound Monitoring Network (http://micos-

manuscript submitted to Geophysical Research Letters372

sc.jwa.or.jp/infrasound-net/observed/, only available in Japanese), maintained by the Japan

373

Weather Association. The coastal tide gauge data at Ofunato (Figure 3c) were downloaded from374

the website of the Intergovernmental Oceanographic Commission (IOC) (http://www.ioc-375

sealevelmonitoring.org). We used Seismic Analysis Code (SAC) software for data processing

376(Goldstein et al., 2003). The F-net earthquake mechanism catalog (Fukuyama et al., 1998) is377

available at https://www.fnet.bosai.go.jp/top.php?LANG=en. Figures were prepared using

378

Generic Mapping Tools Version 6 (GMT6) software (Wessel et al., 2019).

379
380
381

Acknowledgments, Samples, and Data
This work was financially supported by JSPS KAKENHI Grant Numbers JP19H02409

382

and JP19K14818 from the Japan Society for the Promotion of Science. We thank Yuusuke

383

Tanaka of Japan Agency for Marine-Earth Science and Technology for the comments and384

discussions. The English in this manuscript was edited by Forte Science Communications

385(www.forte-science.co.jp). We thank the Editor Kathleen Donohue and three anonymous

386

reviewers for their constructive comments.

387

manuscript submitted to Geophysical Research Letters

388

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Geophysical Research Letters
Supporting Information for
Meteotsunami observed by the deep-ocean seafloor pressure gauge network
off northeastern Japan

Tatsuya Kubota1, Tatsuhiko Saito1, Naotaka Y. Chikasada1, and Osamu Sandanbata1
National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Ibaraki, Japan.

1

Contents of this file
Texts S1 to S3
Figures S1 to S12Introduction
Text S1 explains the methodology of the semblance analysis. Texts S2 and S3 describe theprocedure of the application of the semblance analysis to the S-net pressure data and theatmospheric pressure data, respectively. Figure S1 shows the processed waveforms shown in
different depth bins. Propagation direction and speed of the meteotsunami waves estimatedby the array analysis are shown in Figure S2. Figure S3 shows the weather map during themeteotsunami event. Figure S4 shows wind speed and direction at 500 hPa (~altitude of 5000m) during the meteotsunam. Figure S5 shows the result of the array analysis of the coastal
barograph data in Ofunato. In Figures S6 to S8, the meteotsunami simulations assuming theatmospheric disturbances moving to the northeast are shown. Figure S9 is the comparison of
the waveforms at the coastal tide gauge at Ofunato. The meteotsunami simulation results
assuming the atmospheric disturbances with slower and faster moving speed are shown in
Figures S10 and S11. Figures S12 shows the meteotsunami simulation assuming theatmospheric disturbances moving to the north.

1

Text S1.
This text explains the procedure to calculate the propagation direction andpropagation velocity of the tsunami-like waves observed by the S-net, based on thesemblance analysis (e.g., Asano et al., 2008; Neidell & Taner, 1971).
In order to detect coherent waves and to estimate the apparent slowness vectors of
the propagating waves, we used the semblance analysis. Introducing the Cartesian coordinate
in which the x- and y-directions coincide with the east and north directions, respectively, we
here assume the propagating wave is approximated as a plane wave with constant slowness
vector of s = (sx, sy). Using the slowness vector, the velocity v and direction θ (measured
clockwise from north) of the propagating wave is expressed as:!

! = |#| =!

(S1)$%!" &%#"

and%# = arctan ) ! *.

(S2)

%#

In this situation, the arrival time delay of the propagating wave at i-th station τi, located at xi =(xi, yi) is expressed as+' = , ∙ . ' .

(S3)Using this time delay, the semblance coefficient C is described as:
/=(

!

*

*

-&%&*∑ $%&%'() + (-./ )1
∑.+,
'
'
'+,

"

*
*
$%&%'() ∑ -&%& 2∑ $%&%'() {+' (-./' )}" 5
.+,
'+,

,

(S4)

where ui(t) is the observed waveforms at the i-th station, and Nstation is the total number of thestation and Ndata is the total number of the data at each station. The semblance coefficient C
takes between 0 and 1. The apparent slowness vector that maximizes the semblancecoefficient C is estimated using the grid-search algorithm. When the optimum slowness vector
sest = (sxest, syest) is obtained, the propagation velocity vest and propagation direction θest(measured clockwise from north) are obtained as:!

!

!678 = %/01 = |#/01 | =!
""$%!/01 &%#/01

(S5)

and
% /01

# 678 = arctan )%!/01 *.
#

(S6)

2

Text S2.
We applied the semblance analysis to the S-net pressure time series, to estimate theapparent propagation direction and propagation velocity (Figure S2). We used the S-net
stations located off Miyagi, Iwate and Aomori prefectures with water depth of 500–4000 m(marked by red circles in Figure S2a). We used the time window of 17:00 to 19:00 UTC (FigureS2b). We set the origin of the coordinate (x, y) = (0, 0) at the center of the array. As a result, we
obtained the optimum propagation velocity and propagation direction as v = 117 m/s and φ =340° (Figure S2c). These values are consistent with the apparent propagation direction andvelocity estimated using four S-net located at the water depth of ~1000m (Figure 2c).
Text S3.
We applied the semblance analysis to the atmospheric pressure time series toestimate the propagation direction and propagation velocity of the atmospheric pressure(Figure S5). We used the time series from three barographs located at the Ofunato, Massaki,
and Ryori junior high schools, at the coast of the Iwate prefecture, from the InfrasoundMonitoring Network, maintained by Japan Weather Association (http://micossc.jwa.or.jp/infrasound-net/observed/, Figure 3a). We used the time window during 17:30 and
18:30 to calculate the semblance (Figure 3b). Using the optimum slowness vector, we obtain
the optimum propagation velocity of v = 38.2 m/s (Figure S5). This value is constant with thewind speed at 500 hPa (~altitude of 5000 m). The propagation direction was estimated to be
the northeastward direction (φ =43.4°, Figure S5), although the constraint of the propagation
direction is not so good, probably attributed to the shape of the array. However, this result
suggests that the low-pressure atmospheric system propagates to the east to northeast
direction.

3

(a) < 1500m
43°

Pressure (Bandpass 100-3600 s)

Hokkaido

42°
41°

Aomori

Miy
ag

38°

35°

40°

28 27

shima

a Ibara

ki

Fuku

39°

11 10
14 15

26 25 24 23
01
02 03 04

38°

13 12
11
14
15
16

37°

26

36°

Chib

36°

41°

15 14
16
1718 19 20

12
13

i

39°

37°

42°

20 19
21
22
2302 03

Iw

ate

40°

35°

0 100

34°

19

2 hPa ∆P
20 16

Pressure (Bandpass 100-3600 s)

17

18

19

20

Time on 1 July 2020 UTC [h]Envelope (Bandpass 100-3600 s)

42°

13 11
14
17

41°

04 05

Aomori41°

06 071312 11

40°

40°

21

Iw
ate

26 25
03 04

ag

16

Miy

38°

39°

09 08

i

39°

38°

21
05

ima
Fukush37°

a Ibara

ki

10

36°

Chib

35°

18

43°

Hokkaido

42°

36°

17

Time on 1 July 2020 UTC [h](b) 1500-4000m

37°

2 hPa ∆P
16

146°

140°

km
144°

142°

34°

43°

Envelope (Bandpass 100-3600 s)

43°

35°

0 100

34°
17

18

19

2 hPa ∆P
20 16Time on 1 July 2020 UTC [h](c) > 4000m
43°

2 hPa ∆P
16

146°

140°

km
144°

142°

34°

Pressure (Bandpass 100-3600 s)

17

18

19

20

Time on 1 July 2020 UTC [h]Envelope (Bandpass 100-3600 s)

43°

Hokkaido

42°

42°

41°

41°

08
09

Aomori

40°

40°

05
06
07

39°

Iw

ate

22

Miy

ag

i

39°
38°

35°

i

37°

arak

36°

38°

20

ima

Fukush

21

Chib
a Ib

37°

35°

0 100

34°

146°

142°

km
144°

140°

34°

36°

2 hPa ∆P
16

17

18

19

Time on 1 July 2020 UTC [h]2 hPa ∆P
20 16

17

18

19

20

Time on 1 July 2020 UTC [h]Figure S1. S-net pressure waveforms and envelope waveforms for different water depth bins.
Waveforms for the OBPs with depths of (a) < 1500 m, (b) 1500–4000 m, and (c) > 4000 m.

4

(a)

41°

43°

(b)

0.02

Bandpass 100-3600 s(c)

Hokkaido

Aomori

39°

35°

gi
Mi
ya

09 08

ima

Fukush

arak

i

39°

Chib
a Ib

36°

40°

24

03 04
11 10

38°

37°

28 27 26 25

Iwa

te

40°

15 14 1312 11
161718 19 20 21

146°

144°

142°

0.4
0.3

−0.01

2 hPa ∆P

0 100
140°

0.00

0.2

km

34°

0.01

38°
17

18

19

Time on 1 July 2020 [h]

−0.02
−0.02

−0.01

0.00

0.01

0.02

Semblance41°

Y slowness [s/m]

42°

0.1

X slowness [s/m]Figure S2. (a) Station location used for the semblance analysis. (b) Pressure waveforms used for the semblance analysis. (c) Result of thesemblance analysis for the S-net pressure data. The optimum slowness value in which the semblance value takes maximum is noted by awhite star, and the optimum propagation direction is shown by a white arrow.

1

Figure S3. Atmospheric pressure distribution during the meteotsunami(http://database.rish.kyoto-u.ac.jp/arch/jmadata/). Contour lines are drawn by 1 hPa intervals,
and the thick contours are drawn by 2 hPa intervals. The atmospheric low pressure whichmight be the plausible source of the meteotsunamis is shown by black arrow.

1

41°

18:00 on 01 July 2020

40

40°
39°

20

38°
37°
36°

km
0

[m/s]

30

10

100
40m/s

0

35°
Figure S4. Wind speed and direction at 500 hPa (~altitude of 5000 m) during themeteotsunami (18:00 UTC on 1 July, 2021), from the mesoscale meteorological model.

2

Arrival direction from barograph

0.1

0.4

0.0

0.9
0.8

−0.1

0.4

0.7
0.6

−0.2
−0.2

−0.1

0.0

0.1

0.2

Semblance

1.0
0.8

Y slowness [s/m]

0.2

0.5

X slowness [s/m]Figure S5. Result of the semblance analysis of the coastal barograph waveforms. The
optimum slowness value in which the semblance value takes maximum is noted by a whitestar. The optimum propagation direction estimated from the analysis is shown by a whitearrow.

3

φP = 40°, V0 = 85 m/s, P0 = -0.5 hPa, L0 = 38.0 km
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

t = 6600 s
Patm

Pbot

Peta

Pbot

35°
0 100

0 100

34°

34°
142°

42°

Peta

144°

142°

t = 7800 s
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

144°

t = 9000 s
Patm

1

0

[hPa]

42°

(a) Snapshotst = 5400 s

35°
0 100

0 100

34°

−1

34°
142°

144°

142°

144°

Observed(b) Waveforms
43° Pressure

Calculated

Envelope

42°

Latitude [°N]

41°
40°
39°
38°
37°
36°
35°

2 hPa ∆P

2 hPa ∆P

34°
16

17

18

19

Time on 1 July 2020 UTC [h]

20

16

17

18

19

20

Time on 1 July 2020 UTC [h]Figure S6. Results of the meteotsunami simulation supposing atmospheric pressure moving
to the northeast (φp = 40°) with V0 = 85 m/s. See Figure 4 for detailed explanations of thisfigure.

4

φP = 50°, V0 = 80 m/s, P0 = -0.5 hPa, L0 = 32.0 km
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

t = 6600 s
Patm

Pbot

Peta

Pbot

35°
0 100

0 100

34°

34°
142°

42°

Peta

144°

142°

t = 7800 s
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

144°

t = 9000 s
Patm

1

0

[hPa]

42°

(a) Snapshotst = 5400 s

35°
0 100

0 100

34°

−1

34°
142°

144°

142°

144°

Observed(b) Waveforms
43° Pressure

Calculated

Envelope

42°

Latitude [°N]

41°
40°
39°
38°
37°
36°
35°

2 hPa ∆P

2 hPa ∆P

34°
16

17

18

19

Time on 1 July 2020 UTC [h]

20

16

17

18

19

20

Time on 1 July 2020 UTC [h]Figure S7. Results of the meteotsunami simulation supposing atmospheric pressure moving
to the northeast (φp = 50°) with V0 = 80 m/s. See Figure 4 for detailed explanations of thisfigure.

5φP = 60°, V0 = 60 m/s, P0 = -0.5 hPa, L0 = 25.0 km
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

t = 6600 s
Patm

Pbot

Peta

Pbot

35°
0 100

0 100

34°

34°
142°

42°

Peta

144°

142°

t = 7800 s
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

144°

t = 9000 s
Patm

1

0

[hPa]

42°

(a) Snapshotst = 5400 s

35°
0 100

0 100

34°

−1

34°
142°

144°

142°

144°

Observed(b) Waveforms
43° Pressure

Calculated

Envelope

42°

Latitude [°N]

41°
40°
39°
38°
37°
36°
35°

2 hPa ∆P

2 hPa ∆P

34°
16

17

18

19

Time on 1 July 2020 UTC [h]

20

16

17

18

19

20

Time on 1 July 2020 UTC [h]Figure S8. Results of the meteotsunami simulation supposing atmospheric pressure moving
to the northeast (φp = 60°) with V0 = 60 m/s. See Figure 4 for detailed explanations of thisfigure.

6

Height [cm]φp = 40°, V0 = 85 m/s φp = 50°, V0 = 80 m/s
φp = 60°, V0 = 60 m/s φp = 70°, V0 = 45 m/s
10
5
0
−5
−10
17

Bandpass 120-3600s

18

19Time on 1 July 2020 UTC [h]Figure S9. Comparison of the observed (black) and simulated tide gauge waveform at Ofunato. The colored waveforms denote the simulated
waveforms in which atmospheric pressure moves to the northeast.

1φP = 70°, V0 = 20 m/s, P0 = -0.5 hPa, L0 = 17.0 km
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

t = 6600 s
Patm

Pbot

Peta

Pbot

35°
0 100

0 100

34°

34°
142°

42°

Peta

144°

142°

t = 7800 s
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

144°

t = 9000 s
Patm

1

0

[hPa]

42°

(a) Snapshotst = 5400 s

35°
0 100

0 100

34°

−1

34°
142°

144°

142°

144°

Observed(b) Waveforms
43° Pressure

Calculated

Envelope

42°

Latitude [°N]

41°
40°
39°
38°
37°
36°
35°

2 hPa ∆P

2 hPa ∆P

34°
16

17

18

19

Time on 1 July 2020 UTC [h]

20

16

17

18

19

20

Time on 1 July 2020 UTC [h]Figure S10. Results of the meteotsunami simulation with V0 = 20 m/s (φp = 70°). See Figure 4
for detailed explanations of this figure.

1

φP = 70°, V0 = 100 m/s, P0 = -0.5 hPa, L0 = 17.0 km
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

t = 6600 s
Patm

Pbot

Peta

Pbot

35°
0 100

0 100

34°

34°
142°

42°

Peta

144°

142°

t = 7800 s
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

144°

t = 9000 s
Patm

1

0

[hPa]

42°

(a) Snapshotst = 5400 s

35°
0 100

0 100

34°

−1

34°
142°

144°

142°

144°

Observed(b) Waveforms
43° Pressure

Calculated

Envelope

42°

Latitude [°N]

41°
40°
39°
38°
37°
36°
35°

2 hPa ∆P

2 hPa ∆P

34°
16

17

18

19

Time on 1 July 2020 UTC [h]

20

16

17

18

19

20

Time on 1 July 2020 UTC [h]Figure S11. Results of the meteotsunami simulation with V0 = 100 m/s (φp = 70°). See Figure 4
for detailed explanations of this figure.

2

φP = 0°, V0 = 110 m/s, P0 = -0.5 hPa, L0 = 50.0 km
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

t = 6600 s
Patm

Pbot

Peta

Pbot

35°
0 100

0 100

34°

34°
142°

42°

Peta

144°

142°

t = 7800 s
Patm

Peta

Pbot

42°

41°

41°

40°

40°

39°

39°

38°

38°

37°

37°

36°

36°

35°

144°

t = 9000 s
Patm

1

0

[hPa]

42°

(a) Snapshotst = 5400 s

35°
0 100

0 100

34°

−1

34°
142°

144°

142°

144°

Observed(b) Waveforms
43° Pressure

Calculated

Envelope

42°

Latitude [°N]

41°
40°
39°
38°
37°
36°
35°

2 hPa ∆P

2 hPa ∆P

34°
16

17

18

19

20

16

Time on 1 July 2020 UTC [h]N-S Distance from S3N11 [km](c) Seafloor pressure
200

150

17

18

19

20

Time on 1 July 2020 UTC [h](d) Sea-surface height (P0=-0.5 hPa)

S4N14

S4N18

100 S4N27

50

P0=-1.0 hPa

2 hPa ∆P
~2 cm ∆H2O

0

Pbot

S3N11

Patm

P0=-0.2 hPa

17.8

18.0

18.2

2 cm ∆H2O

Peta

P0=-0.5 hPa

18.4

18.6

Time on 1 July 2020 UTC [h]

17.8

18.0

18.2

18.4

18.6

Time on 1 July 2020 UTC [h]Figure S12. Results of the meteotsunami simulation with the northward moving atmosphericpressure disturbance with V0 = 110 m/s (φp = 0°). See Figure 4 for detailed explanations of thisfigure.

3