Monitoring storm evolution using a high-density seismic network

Abstract

Data acquired by a dense seismic network deployed in the Cerdanya basin (Eastern Pyrenees) is used to track the temporal and spatial evolution of meteorological events such as rainfall episodes or thunderstorms. Comparing seismic and meteorological data, we show that for frequencies above 40 Hz, the dominant source of seismic noise is rainfall and hence the amplitude of the seismic data can be used as a proxy of rainfall. The interstation distance of 1.5 km provides an unprecedented spatial resolution of the evolution of rainfall episodes along the basin. Two specific episodes, one dominated by stratiform rain and the second one dominated by convective rain, are analyzed in detail, using high resolution disdrometer data from a meteorological site near one of the seismic instruments. Seismic amplitude variations follow a similar evolution to radar reflectivity values, but in some stratiform precipitation cases, it differs from the radar-derived precipitation estimates in this region of abrupt topography, where radar may suffer antenna beam blockage. Hence, we demonstrate the added value of seismic data to complement other sources of information such as rain-gauge or weather radar observations to describe the evolution of ground-level rainfall fields at high spatial and temporal resolution. The seismic power and the rainfall intensity have an exponential relationship and the periods with larger seismic power are coincident. The time intervals with rain drops diameters exceeding 3.5 mm do not result in increased seismic amplitudes, suggesting that there is a threshold value from which seismic data are no longer proportional to the size of the drops. Thunderstorms can be identified by the recording of the sonic waves generated by thunders, with. Single thunders detected to distances of a few tens of kilometers. As the propagation of these acoustic waves is expected to be strongly affected by parameters as air humidity, temperature variations or wind, the seismic data could provide an excellent tool to investigate atmospheric properties variations during thunderstorms.

Recording Rainfall Intensity: Has an Optimum Method Been Found?

“…Diaz et al [195] showed that at frequencies above 40 Hz, most seismic signals at their field site in Spain were generated by rainfall. This then suggested that frequencies above this threshold could be used to monitor rainfall.…”

Experimental campaign for the characterization of precipitation in a complex terrain site using high resolution observations

“…distinguishing precipitation types such as snow or rain) or radar bright-band detection, which facilitates the data analysis (more information and public access to this post-processing software can be found in [15,17,19]). Classification of the precipitation type is an essential aspect here as it allows to separate liquid from solid precipitation, given that erosivity of rainfall is computed from particle size distributions identified as rain drops, from which kinetic energy is calculated given their terminal velocity [20,11,21]. Moreover, the application of RaProM-Pro also allows to provide the frequency of occurrence of different precipitation types, which is also relevant to study the impact of precipitation on wind turbines.…”

Seismic Signature of the Super Cyclone Amphan in Bay of Bengal Using Coastal Observatories Operating Under National Seismological Network of India

“…Several researchers have carried out different seismological analyses of SBN to characterize cyclone generated oceanic as well as seismic waves for their effective monitoring (e.g., Borzi et al., 2022; Chi et al, 2010; Diaz et al, 2023; Fan et al., 2019; Gerstoft et al., 2006; Gualteri et al, 2018; Hua et al, 2023; Retailleau & Gualtieri, 2019; Sufri et al, 2014; Tabulevich, 1971; Zhang et al, 2010). They applied a wide range of methods to monitor oceanic storms with seismological waveforms, such as single station (Sufri et al., 2014), array analysis (Borzi et al., 2022; Hua et al, 2023), and seismic interferometry (Retailleau et al., 2017).…”

“…The ambient seismic ground vibration, also called seismic background noise (SBN) of the solid earth is originated mainly due to oceanic‐driven winds such as tropical cyclones, extratropical cyclones, and other stormy phenomena (e.g., Brwomirski, 2001; Chi et al., 2010; Díaz et al., 2023; Gualtieri al., 2018; Hua et al., 2023; Retailleau & Gualtieri, 2021; Schulte‐Pelkum et al., 2004; Webb, 1992) and it can be recorded globally using seismographs. The accepted mechanisms responsible for the generation of SBNs are (a) direct coupling between oceanic waves in shallower water and that near the seafloor in the 10–20 s period (Webb, 2002) and (b) nonlinear interaction of two sets of opposite moving oceanic gravity waves having similar frequency content that generate <10 s microseism (Ekström & Ekström, 2005; Longuet‐Higgins, 1950; Rhie & Romanowicz, 2004).…”