Meteorological Anomalies Based on Ground-Based AMeDAS Data for the 1995 Kobe Earthquake: Critical Natural Time Analysis

Masashi Hayakawa^1,2*, Stelios M. Potirakis^3,4,5, Shinji Hirooka^6,7, Yasuhide Hobara^8,9

¹UEC Alliance Center, Hayakawa Institute of Seismo Electromagnetics, Co., Ltd. (Hi-SEM), Tokyo, Japan.
²Advanced Wireless & Communications Research Center, The University of Electro-Communications, Chofu, Japan.
³Department of Electrical and Electronics Engineering, Ancient Olive Grove Campus, University of West Attica, Athens, Greece.
⁴Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Metaxa and Vasileos Pavlou, Athens, Greece.
⁵Department of Electrical Engineering, Computer Engineering and Informatics, School of Engineering, Frederick University, Nicosia, Cyprus.
⁶HiSR Lab LLC, Tokyo, Japan.
⁷Graduate School of Science, Chiba University, Chiba, Japan.
⁸Department of Computer and Network Engineering, The University of Electro-Communications, Tokyo, Japan.
⁹Center for Space Science and Radio Engineering, The University of Electro-Communications, Tokyo, Japan.

DOI: 10.4236/ojer.2025.142006                

Abstract

Two meteorological quantities of T/Hum (T: temperature and Hum: relative humidity) and ACP (atmospheric chemical potential) of water molecules based on the “ground-based” AMeDAS (automated meteorological data acquisition system) “open” data of Japan Meteorological Agency have been utilized to explore a possibility of short-term earthquake (EQ) prediction. The target EQ is the famous 1995 Kobe EQ with magnitude of 7.3 on 17 January, 1995. Midnight (LT = 01 h) data of temperature and humidity at a particular station of Kobe, close to the EQ epicenter, have been analyzed, and we have found that there exists only a single and conspicuous peak in both quantities of T/Hum and ACP on the same day of 10 January, 1995 during the short-term EQ prediction window of one month before and two weeks after an EQ. Fortunately, during this short-term EQ prediction period, the geomagnetic and solar activity was extremely quiet, and so we think that the anomaly on this particular day, just one week before the EQ, is very likely to be a precursor to the EQ. Then, in order to find any definite causality relationship of this meteorological anomaly to the EQ, we have performed the critical analysis for the same datasets. We have applied the natural time (NT) analysis method to the daily-valued quantities of ACP and detrended T/Hum, and the NT analysis results revealed criticality in both quantities ~3 weeks before the 1995 Kobe EQ. This implies that the meteorological anomalies as a proxy of the exhalation of radon and charged aerosols from the lithosphere into the atmosphere are at the critical stage ~3 weeks before the EQ and so an anomaly in two meteorological quantities appeared one week before the EQ as a definite precursor to the EQ as suggested by this criticality.

Keywords

Earthquake (EQ) Precursors, AMeDAS Data, Meteorological Parameters, Temperature/Humidity, ACP (Atmospheric Chemical Potential), Statistical Analysis, Critical Analysis, Natural Time (NT) Analysis

1. Introduction

Even though predicting an impending earthquake (EQ) with lead time of about a week or so is highly desired by the general public in order to save human lives and economical losses, this short-term EQ prediction is still a challenging subject left in the field of geophysics. The current topic of the field of seismo-electromagnetics (or short-term EQ prediction), is the elucidation of lithosphere-atmosphere-ionosphere coupling (LAIC), because the ionosphere is found to be very sensitive to pre-EQ lithospheric activity due to the strong coupling among the three regions from the lithosphere to atmosphere and ionosphere (e.g., [1]-[5]). The bottom part of the LAIC is the lithosphere and Earth’s surface, and so the information in these bottom regions plays an essential role in these LAIC studies.

Earth’s surface monitoring (or remote sensing) from satellites has been extensively performed in order to find any significant precursors to EQs since the pioneering papers by [6]-[9], especially Tronin et al. (2002) [9] have found significant temperature increases in and around the relevant fault regions of EQs. Then, many papers have been published so far, to study different physical parameters, including meteorological parameters (temperature, humidity etc.), OLR (outgoing longwave radiation), SLHF (surface latent heat flux) etc. [10]-[26]. Of course, we understand that the main stream of seismo-electromagnetics is the elucidation of the LAIC process, so the information of Earth’s surface or near-Earth information is of essential significance as the bottom region of LAIC, because the driving agent of this LAIC is highly likely to lie within the lithosphere [27]-[40].

Those satellite observations in many previous papers [27]-[40] are all based on “open” data from different satellite sources, but the present paper proposes the use of ground-based “open” data with higher temporal and spatial resolution than the satellite remote sensing observations. The data used in this paper are available from AMeDAS (automated meteorological data acquisition system) of Japan Meteorological Agency (JMA). In our companion paper [41], we have proposed the two physical quantities of T/Hum (T: temperature and Hum: relative humidity) and ACP (atmospheric chemical potential) of water molecules as a short-term EQ predictor. We have analyzed those quantities for the first time for the famous 1995 Kobe EQ (M = 7.3), in order to examine whether these two physical quantities themselves are a powerful tool as a possible candidate of short-term EQ prediction or not, in addition to LAIC studies, and succeeded in identifying a clear anomaly in both quantities, just one week before the EQ. Because we have found a significant precursory anomaly for this particular EQ, we will here perform the criticality analysis as the next step. An important direction in the field of possible seismic precursors is the application of critical analysis to various quantities possibly involved in LAIC. Such studies have revealed that different layers of LAIC reach a critical state before a strong EQ. Specifically, the criticality analysis method called “natural time” (NT) analysis has successfully been applied to VLF subionospheric propagation data, ULF magnetic field data, global navigation satellite system deformation (GNSS) data, surface latent heat flux (SLHF) data and very recently in stratospheric potential energy ($E_p$ ), as well to other EQ-related observables such as pre-EQ seismic electric signals (SES), foreshock seismicity, MHz fracture-induced electromagnetic emissions (FEME) recording (e.g., [42]-[50]).

The rest of this paper is organized as follows. Section 2 presents the target EQ, AMeDAS data, and solar-geomagnetic conditions. In Section 3, we review our meteorological quantities, whereas Section 4 presents the conventional statistical analysis using the confidence bounds and its results. Section 5 presents the most important part of this paper on the NT analysis and its results. Sections 6 and 7 are the discussion and conclusion, respectively.

2. EQ Studied in This Paper, and Solar, Geomagnetic Activity

2.1. Target EQ and AMeDAS Stations

The target EQ of this paper is the famous 1995 Kobe EQ that happened at 5 h 46 m (JST, local time (LT)) on January 17, 1995, having its epicenter at the geographical coordinates (34˚35.9’N, 135˚02.1’E), as shown in Figure 1, with a magnitude $M=7.3$ , and with a depth of 16 km. We have plotted a few AMeDAS meteorological stations (black boxes) close to the EQ epicenter together with the fault regions (Rokko/Awaji island faults) possibly related with this EQ.

2.2. Solar-Geomagnetic Environment

Figure 2 illustrates the temporal evolutions of the geomagnetic activity (Dst and Kp indices) and the solar radiation flux at the wavelength of 10.7 cm (f10.7) during the period of 1 May, 1994 through 31 May, 1995 (over one year) including the day of the EQ. We think that unlike the ionosphere, the meteorological parameters studied in this paper are likely to be much less influenced by solar-geomagnetic conditions and perturbations. As seen from the plot of Dst (Figure 2)

Figure 1. Location of the epicenter of the 1995 Kobe EQ (indicated by a red star) and some AMeDAS stations (shown as black boxes) including Kobe. Also, the possible fault regions are plotted by bold lines, and sea parts are given in brown.

Figure 2. Temporal evolutions of geomagnetic activity (Dst and Kp indices) and solar radiation flux (f10.7) during the period of 1 May 1994 to 31 May 1995. The time of the 1995 Kobe EQ is indicated by the vertical red line.

some geomagnetic disturbances with Dst below –50 nT are only noticed in October and November, 1994, but the geomagnetic activity after the beginning of December, 1994 till the day of the EQ, which is very important for the short-term EQ prediction, was extremely quiet, because Dst is less than –20 nT. Further, the solar radiation flux (F10.7) as an indicator of overall solar activity levels was also found to be less than 100. Taking into account the quiet solar-terrestrial conditions just before the EQ, we are ready to investigate short-term EQ precursors within the short-term EQ prediction span of one month before and two weeks after the EQ.

3. Meteorological Parameters from the Japanese AMeDAS Data

When thinking of a possible short-term EQ predictor, we try to use the openly available data from ground-based observations of meteorological parameters. Even though there have recently been published many papers on the study of these meteorological parameters such as temperature, humidity, etc. from the “open” data of satellite remote sensing observations [6]-[40]. So, our companion paper [41] is the first attempt to use the ground-based open data. Before going to the critical analysis, we are obliged to describe the data and analysis results [41] briefly again in this paper, for completeness reasons.

3.1. AMeDAS Data

JMA has established an AMeDAS since 1975, and it has been in operation very regularly. Four meteorological parameters are being measured at each station: 1) precipitation (or rainfall) (in units of 0.5 mm), 2) air temperature (in units of 0.1˚C) and air relative humidity (in %), 3) sunshine duration, and 4) wind direction/speed, and this AMeDAS network includes as many as 1300 stations all over Japan, so the stations are located, on average, with an interval of 17 km.

3.2. Meteorological Parameters Used in This Paper

We have studied the following two meteorological quantities proposed in Haya-kawa et al. (2022) [34] and Schekotov et al. (2023) [51]. The fundamental idea may be that radon (Rn) rises to the Earth’s surface more intensely along seismic faults or volcanic fumaroles during the EQ preparation phase, and the subsequent air ionization leads to a decrease in the air humidity and an increase in its temperature [51]. So, the first quantity of our interest is the simple ratio of temperature, T (in ˚C) to humidity (relative humidity), Hum (in %), i.e., T/Hum, because of our expectation of an enhanced seismogenic effect of increasing T and decreasing Hum. The second quantity is ACP (atmospheric chemical potential) which is given by the following equation [2]:

$ACP\left(\text{in}\text{eV}\right)=5.8\times {10}^{-10}{\left(20T+5463\right)}^2\ln \left(100/Hum\right)$ (1)

Here we indicate the reason why we call this the chemical potential. Because at the moment of water molecule condensation/evaporation or attachment/detachment to the ions, the latent heat release/absorption is equal to the chemical potential [5]. This ACP value is supposed to yield an increase during the process of air ionization by any agents including galactic cosmic rays etc., one of them being by the exhalation of radon (Rn) and charged aerosols which is known to be released during the pre-EQ enhancement as reported by several workers (e.g., [52]-[56]).

We have analyzed the data from some other AMeDAS of stations near the EQ epicenter in the western part of Japan: Kobe (34˚41'N, 135˚12'E), Osaka (34˚41'N, 135˚31'E), Sumoto (34˚19'N, 134˚51'E), and Himeji (34˚54'N, 134˚40'E) as indicated by black boxes in Figure 1 and we have confirmed the presence of the same anomalous day at all of these stations. So, in the following, we will present again the results only for one particular station of Kobe from [41] as a representative example.

4. Statistical Analysis Based on the Mean and Standard Deviation

4.1. Midnight Data

The basic quantities of T and Hum only around midnight are used, and we choose LT = 01 h because daytime is strongly influenced by strong solar radiation and we think that the time period around midnight is considered to be most suitable for our analysis by excluding this solar effect.

4.2. Conventional Statistical Results

We will first repeat the statistical results from [41]. Figure 3(a) is the temporal evolution of the detrended daily value of $T/Hum$ (i.e., $\delta \left(T/Hum\right)=\left(T/Hum\right)-m$ (where $m$ is the running mean during the previous 30 days), and the standard deviation of σ is also estimated during the previous 30 days, as followed by Hayakawa et al. (2022) [34]. Each vertical black bar indicates the current value for each day and the abscissa indicates the date. The day of EQ (17 January, 1995) is indicated by a vertical red line. When there is no vertical bar on a particular day, it means that the current value is below the means value of m. There are plotted three curves in the figure; the bottom green curve refers to $m+\sigma$ , the orange curve to $m+2\sigma$ , and the red curve to $m+3\sigma$ . The period of presentation in Figure 3 is a time window of about one month before and a few weeks after an EQ as short-time EQ prediction window. Now we will try to identify any anomalous variations by using the confidence bounds with $m$ and $\sigma$ . In our previous works it has been taken a criterion of $m+2\sigma$ , as it is usually done when dealing with different physical parameters (e.g., [3]), but in this paper we adopt a much more severe criterion of $m+3\sigma$ as used conventionally in the field of astrophysics. Figure 3(a) indicates that we can notice a single and a very remarkable peak exceeding $m+3\sigma$ only on 10 January, 1995, just one week before the EQ. Other weather effects (such as storm, fog, etc) could not influence this anomaly.

Figure 3. Temporal evolutions of daily values (vertical black bars) of (a) detrended $T/Hum$ , $\delta \left(T/Hum\right)$ , and (b) detrended $ACP,\delta \left(ACP\right)$ , during a short-term EQ window of about one month before and a few weeks after the EQ. A vertical red line indicates the day of the EQ. Three colored curves: bottom green curve refers to $m+\sigma$ , orange to $m+2\sigma$ , and red to $m+3\sigma$ . An anomalous day is clearly noticed on 10 January, 1995.

Now we move on to Figure 3(b) for the plot of detrended $ACP$ , i.e. $\delta \left(ACP\right)$ . Quite similarly as in Figure 3(a), we can identify the same anomaly on the same day in Figure 3(b). Its anomalous value is not exceeding this criterion of $m+3\sigma$ , but stands out well above $m+2\sigma$ . This peak is again the only one within the short-term EQ prediction window.

Finally, we can conclude that the significant peaks in the two parameters, as seen in Figure 3(a) and Figure 3(b), both appear on 10 January, 1995, just one week before the EQ. Hence, it is highly likely that an anomaly on 10 January, 1995 is a possible precursor to the EQ. Further, we will apply a critical analysis to the same meteorological quantities, $T/Hum$ and $ACP$ , in order to identify whether the above presented peaks are due to the criticality or the causality relationship with this EQ.

5. Natural Time (NT) Analysis

5.1. NT Analysis Method

The NT time series analysis method has initially been applied to the ultra-low frequency (≤1 Hz) seismic electric signals (SES) [57]-[59], and has been shown to be optimal for enhancing the signals in the time-frequency space [60]. The full theoretical details of this method can be found in the monograph by Varotsos et al. [42] (and references therein), while the application of NT analysis to various seismo-EM signals has been presented in detail in [46]. Also, recent studies using the NT time series method to other observable quantities of LAIC have shown the existence of critical dynamics before EQs [46]-[48]. In what follows, we will briefly present the key notions of this method and the process of applying it to our meteorological data.

Initially, for a number of $N$ events, we determine the NT of the k-th (in order of occurrence) event as ${\chi }_k=k/N$ , which is actually the order of occurrence normalized in the interval (0, 1]. Next, we determine the “energy” of each event in NT, which is symbolized as $Q_k$ for the k-th event. At this point we have to mention that $Q_k$ corresponds to different kinds of quantities, depending on the time series under analysis. For example, in the case of seismic events $Q_k$ is the seismic energy released (seismic moment), while for the dichotomous SES signals $Q_k$ corresponds to the SES pulse duration [59]. On the other hand, in the case of the fracto-electromagnetic emission signals in the MHz band, which are non-dichotomous signals, $Q_k$ denotes the energy of each event by using consecutive amplitude values above a noise threshold as described in [44].

Then, we study the resulting time series $\left({\chi }_k,Q_k\right)$ . The approach of a dynamical system to criticality is identified by means of the variance ${\kappa }_1=\langle {\chi }^2\rangle -{\langle \chi \rangle }^2$ of NT, where $\langle f\left(\chi \right)\rangle ={\displaystyle {\sum }_{k=1}^N{p}_{k}f\left({\chi }_k\right)}$ , where $p_k=Q_k/{\displaystyle {\sum }_{n=1}^N{Q}_n}$ is the normalized energy released during the k-th event. Hence, the quantity ${\kappa }_1$ can be written as ${\kappa }_1={\displaystyle {\sum }_{k=1}^N{p}_k{\chi }_k^2}-{\left({\displaystyle {\sum }_{k=1}^N{p}_k{\chi }_k}\right)}^2$ . Moreover, the entropy in NT is defined as $S_{nt}={\displaystyle {\sum }_{k=1}^N{p}_k{\chi }_k\ln {\chi }_k}-\left({\displaystyle {\sum }_{k=1}^N{p}_k{\chi }_k}\right)\ln \left({\displaystyle {\sum }_{k=1}^N{p}_k{\chi }_k}\right)$ [61]. The entropy in NT is a dynamic entropy, depending on the order of the events [60]. Also, $S_{nt-}$ , the entropy under time reversal, i.e., by reversing the order of the considered events (which of course changes the natural time used for the calculations), is also studied [61].

In many dynamical systems studied by the NT analysis method, it has been found that the value of ${\kappa }_1$ is a measure to quantify the extent of the organization of the system at the onset of the critical stage [41]. The criticality is reached when (a) ${\kappa }_1$ takes the value ${\kappa }_1=0.07$ , and (b) at the same time both the entropy in NT and the entropy under time reversal satisfy the following condition $S_{nt},S_{nt-}<S_u=\left(\ln 2/2\right)-1/4$ [42] [62], where $S_u$ is the entropy of the uniform distribution in NT [41] [62].

In the special case of NT analysis of foreshock seismicity [57] [59] [61] [63], we study the evolution of the quantities ${\kappa }_1,S_{nt},S_{nt-}$ , and $\langle D\rangle$ over time, where $\langle D\rangle$ is the “average” distance between the normalized power spectra $\text{Π}\left(\tilde{\omega }\right)={\left|{\displaystyle {\sum }_{k=1}^N{p}_k\exp \left(j\tilde{\omega }{\chi }_k\right)}\right|}^2$ ($\tilde{\omega }$ stands for the angular frequency in NT) of the evolving seismicity and the theoretical estimation of $\text{Π}\left(\tilde{\omega }\right)$ for ${\kappa }_1=0.07$ , ${\Pi }_{critical}\left(\tilde{\omega }\right)\approx 1-{\kappa }_1{\tilde{\omega }}^2$ . Moreover, an “event” for the NT analysis of seismicity is considered to be any data point of the original seismicity time series (time series of magnitudes of EQs) that surpasses a magnitude threshold, $M_{Thres}$ .

The analysis starts with an appropriate low threshold and taking into account only an adequate number of, first in the order of occurrence, events. Next, the subsequent events, in their original order, are one-by-one taken into account. For each additional event that is taken into account, the quantity ${\chi }_k$ is rescaled within the interval (0, 1] and all ${\kappa }_1,S_{nt},S_{nt-}$ , and $\langle D\rangle$ are re-calculated. This way, a temporal evolution of these quantities is obtained. The described procedure is repeated for several, increasing, values of $M_{Thres}$ for each studied geographic area, and everything is repeated for different overlapping areas.

The seismicity is considered to be in a true critical state, a “true coincidence” is achieved, as soon as (a) ${\kappa }_1$ takes the value ${\kappa }_1=0.07$ , (b) at the same time for both the entropy in NT and the entropy under time reversal satisfy the condition $S_{nt},S_{nt-}<S_u$ , and three additional conditions are satisfied: (c) The “average” distance $\langle D\rangle$ should be smaller than 10⁻², i.e., $\langle D\rangle =\left|\Pi \left(\tilde{\omega }\right)-{\Pi }_{critical}\left(\tilde{\omega }\right)\right|<{10}^{-2}$ (this is a practical criterion for signaling the achievement of spectral coincidence) [42]; (d) the parameter ${\kappa }_1$ should approach the value ${\kappa }_1=0.070$ “by descending from above”, i.e., before the main event the parameter ${\kappa }_1$ should gradually decrease until it reaches the critical value 0.070 (this rule was found empirically) [42] [57]; (e) the above-mentioned conditions (a)-(d) should continue to be satisfied even if the considered $M_{Thres}$ or the area within which the seismicity is studied are changed (within reasonable limits).

The use of the magnitude threshold excepts some of the weaker EQ events (those events that their magnitude is $<M_{Thres}$ ) from the NT analysis. However, the usage of the magnitude threshold is valid for the reason that some recorded magnitudes are not considered reliable due to the seismographic network. On the other hand, the application of various $M_{Thres}$ values are useful in determining the time range within which criticality is reached. This is because, in some cases, it is found that more than one time-points may satisfy the rest of the NT critical state conditions (a)-(d), and criterion (e) is the one that finally reveals the true time of criticality.

For the application of NT analysis to the meteorological quantities analyzed in this study, we follow the paradigm of the NT analysis of seismicity. Specifically, in the case of the atmospheric chemical potential (ACP) the necessary threshold values are defined in terms of ACP values and the “energy” $Q_k$ values are daily ACP values exceeding the considered threshold. Similarly, in the case of the temperature over humidity ($T/Hum$ ), the positive detrended daily values $\delta \left(T/Hum\right)$ are used to define the necessary threshold values and the “energy” $Q_k$ . The detrended daily values are calculated by subtracting the running mean of $T/Hum$ (see Section 4.2 and Figure 3(a)) from the $T/Hum$ daily values, in order to remove $T/Hum$ seasonal variation.

5.2. NT Analysis Results

In this section we present the results obtained by the application of the NT analysis method to the daily-valued time series of ACP (Figure 4) and $\delta \left(T/Hum\right)$ (Figure 5). We have applied the NT method to the above-mentioned meteorological quantities as explained in Section 5.1. Specifically, for each one of the aforementioned meteorological quantities, we consider as an event any daily value of the meteorological quantity under analysis, that is higher than a certain threshold; that is, we exclude values which are weaker than the specific threshold. Then, the “energy” $Q_k$ of the k-th revealed event is considered to be equal to the corresponding daily value of the analyzed meteorological quantity. Finally, the NT analysis is applied to the time series of the revealed events of each meteorological quantity, as in the case of the pre-EQ seismic activity (see [45] [46]).

Figure 4 presents the NT analysis results for the ACP time series over a time period of three months from 17 November 1994 to 17 February 1995 (two months before and one month after the occurrence of the 1995 Kobe EQ). As can be seen from Figure 4, the NT criticality criteria were satisfied during the time period 23-30 December 1994, since during that time interval, simultaneously, ${\kappa }_1$ approaches the value ${\kappa }_1=0.070$ “ by descending from above”, $S_{nt},S_{nt-}<S_u$ and $\langle D\rangle <{10}^{-2}$ for all four presented $AC{P}_{Th}$ threshold values (see also Subsection 5.1). The magenta patches in Figure 4 show the time intervals during which the approach to criticality was found for each $AC{P}_{Th}$ , while their intersection reveals that “true coincidence”, i.e., true critical state (see Subsection 5.1), was achieved during the time period 23-30 December 1994. This means that critical dynamics are embedded in the ACP time series for a time period of 7 days, ~3 weeks before the 1995 Kobe EQ (from 25 days up to 18 days before the EQ occurrence on17 January 1995).

For the $\delta \left(T/Hum\right)$ quantity, the NT analysis results are portrayed in Figure 5. Similarly, for this meteorological quantity, the NT criticality criteria were satisfied on 27 December 1994, also ~ 3 weeks before the 1995 Kobe EQ. As already mentioned in Subsection 5.1, for the NT analysis of $\delta \left(T/Hum\right)$ only the positive values of the $\delta \left(T/Hum\right)$ time series were considered as events. Due to

Figure 4. NT analysis of the $ACP$ meteorological quantity time series for the time period from 17 November 1994 to 17 February 1995. The presented temporal variations of the NT parameters correspond to the different thresholds of $AC{P}_{Th}$ : (a) 0, (b) 0.0014, (c) 0.0021, (d) 0.0035, respectively. The limit value of the entropy $S_u\left(\approx 0.0966\right)$ appears as a horizontal solid light green line, while the ${\kappa }_1$ value 0.07 along with a region of ±0.05 around it, are denoted by a horizontal solid grey and two horizontal dashed grey lines, respectively. The 10⁻² $\langle D\rangle$ threshold is shown as a horizontal brown line. The events presented in each panel depend on the corresponding $AC{P}_{Th}$ . Moreover, although the conventional time (date) of the occurrence of each corresponding event is noted in the x-axis tick values, the x-axis scale actually follows the NT representation; for this reason, the x-axis is not linear in conventional time.

that, the number of $\delta \left(T/Hum\right)$ events during the time period used for the ACP case was quite low for NT analysis. Consequently, in order to increase the number of $\delta \left(T/Hum\right)$ events taken into account, the NT analysis of $\delta \left(T/Hum\right)$ has been performed for a longer time period than in the ACP case, namely from 17 October 1994 to 17 February 1995 (three months before and one month after the occurrence of the 1995 Kobe EQ).

6. Discussion

In our companion paper [41], we have proposed two meteorological quantities of T/Hum and ACP as a possible candidate of short-term EQ prediction, and we have examined those values especially at midnight that are free from strong solar radiation, for a particular famous 1995 Kobe EQ ($M=7.3$ ) on 17 January, 1995. Based on the data during over one year data around the day of EQ, we have found

Figure 5. NT analysis of the $\delta \left(T/Hum\right)$ meteorological quantity time series for the time period from 17 October 1994 to 17 February 1995. The presented temporal variations of the NT parameters correspond to the different thresholds of $\delta {\left(T/Hum\right)}_{Th}$ : (a) 0, (b) 0.015, (c) 0.03, (d) 0.035, respectively. Figure format follows the format of Figure 4.

a very conspicuous anomaly on 10 January, 1995, just one week before the EQ in both quantities within the short-term EQ prediction window of one month before and two weeks after an EQ. Specifically, T/Hum exhibited an anomaly exceeding $m+3\sigma$ , whereas the corresponding ACP anomaly exceeded $m+2\sigma$ . If a meteorological process is ending up to an observed non-seismogenic statistical anomaly, e.g., due to rainfall, this anomaly is not related to a phase transition or a course to an extreme event (in the sense of complex system theory), therefore criticality is not expected to precede. So, we have applied the NT critical analysis to the same two meteorological quantities, and we have found that criticality signatures were clearly identified in both the ACP and the detrended T/Hum ~ 3 weeks before the EQ. So, we can conclude that the observed conspicuous anomaly on 10 January, 1995, just one week before the EQ, is considered to be the consequence of this preceding critical signature observed three weeks before the EQ, and so that this anomaly is highly likely to be a possible precursor to the Kobe EQ. The presence of this critical signature may indicate that these meteorological anomalies can be the possible candidate of seismogenic effects in the lithosphere probably as a proxy to the emanation of radon and charged aerosols during the EQ preparation phase (i.e., [64]).

7. Conclusions

We will summarize the important results emerged from this work.

1) Based on the data over one year around the 1995 Kobe EQ happened on 17 January, 1995, the statistical analysis using the confidence bounds has found a remarkable anomaly on 10 January, 1995, just one week before the Kobe EQ (within the short-term EQ prediction window) both for the two quantities of T/Hum and ACP. The peak on this particular day exceeded $m+3\sigma$ for T/Hum, and well above $m+2\sigma$ for ACP [41]. This peak on 10 January, 1995 has been confirmed at other stations, providing further confidence on this presence.

2) By applying the critical NT analysis to the same data, we have found critical signatures about 3 weeks before the EQ in both quantities, preceding the above anomaly on 10 January, 1995. So, we can conclude that the statistical anomaly on 10 January, 1995 and the corresponding criticality of 25/12/1994 (ACP) and 23/12/1994 (T/Hum) are highly likely to be due to the EQ preparation. This may suggest that our meteorological quantities reflect the critical state associated with these quantities, probably as the consequence of emanation of radon and charged aerosols during EQ preparation phase, resulting in the notable changes in T and Hum.

3) A combinational use of two quantities of T/Hum and ACP is recommended for the study of short-term EQ prediction, because of taking into account the advantage of each quantity. Unfortunately, the quantity of T/Hum shows a strong seasonal variation, and so it is useful especially during winter-time like the Kobe EQ [41]. On the other hand, the other quantity ACP is found to be a stable EQ predictor and can be utilized during the whole year. This paper has provided quantitative evidence on the usefulness of ACP, which has been recently extensively studied (but unfortunately only qualitatively) [65]-[68].

Acknowledgements

The authors are grateful to the staffs of Hi-SEM and UEC for their extensive support. The AMeDAS data are available from the site of https://www.data,jma.go.jp/risk/obsdl/index.php (accessed on 1 April, 2023), and the data of geomagnetic and solar activities can be downloaded from the OMNIWEB (https://omniweb.gsfc.nasa.gov/form/dx1.html, accessed on 1 January, 2024).

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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