scholarly journals The 2018 phreatic eruption at Mt. Motoshirane of Kusatsu–Shirane volcano, Japan: eruption and intrusion of hydrothermal fluid observed by a borehole tiltmeter network

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Akihiko Terada ◽  
Wataru Kanda ◽  
Yasuo Ogawa ◽  
Taishi Yamada ◽  
Mare Yamamoto ◽  
...  

AbstractWe estimate the mass and energy budgets for the 2018 phreatic eruption of Mt. Motoshirane on Kusatsu–Shirane volcano, Japan, based on data obtained from a network of eight tiltmeters and weather radar echoes. The tilt records can be explained by a subvertical crack model. Small craters that were formed by previous eruptions are aligned WNW–ESE, which is consistent with the strike of the crack modeled in this study. The direction of maximum compressive stress in this region is horizontal and oriented WNW–ESE, allowing fluid to intrude from depth through a crack with this orientation. Based on the crack model, hypocenter distribution, and MT resistivity structure, we infer that fluid from a hydrothermal reservoir at a depth of 2 km below Kusatsu–Shirane volcano has repeatedly ascended through a pre-existing subvertical crack. The inflation and deflation volumes during the 2018 eruption are estimated to have been 5.1 × 105 and 3.6 × 105 m3, respectively, meaning that 1.5 × 105 m3 of expanded volume formed underground. The total heat associated with the expanded volume is estimated to have been ≥ 1014 J, similar to or exceeding the annual heat released from Yugama Crater Lake of Mt. Shirane and that from the largest eruption during the past 130 year. Although the ejecta mass of the 2018 phreatic eruption was small, the eruption at Mt. Motoshirane was not negligible in terms of the energy budget of Kusatsu–Shirane volcano. A water mass of 0.1–2.0 × 107 kg was discharged as a volcanic cloud, based on weather radar echoes, which is smaller than the mass associated with the deflation. We suggest that underground water acted as a buffer against the sudden intrusion of hydrothermal fluids, absorbing some of the fluid that ascended through the crack.

2021 ◽  
Author(s):  
Akihiko Terada ◽  
Wataru Kanda ◽  
Yasuo Ogawa ◽  
Taishi Yamada ◽  
Mare Yamamoto ◽  
...  

Abstract We estimate the mass and energy budgets for the 2018 phreatic eruption of Mt. Motoshirane on Kusatsu–Shirane volcano, Japan, based on data obtained from a network of eight tiltmeters and weather radar echoes. The tilt records can be explained by a subvertical crack model. Small craters that were formed by previous eruptions are aligned WNW–ESE, which is consistent with the crack azimuth modeled in this study. The direction of maximum compressive stress in this region is horizontal and oriented WNW–ESE, allowing fluid to intrude from depth through a crack with this orientation. Based on the crack model, hypocenter distribution, and MT resistivity structure, we infer that fluid from a hydrothermal reservoir at a depth of 2 km below Kusatsu–Shirane volcano has repeatedly ascended through a pre-existing subvertical crack. The inflation and deflation volumes during the 2018 eruption are estimated to have been 5.1 * 10 5 and 3.6 * 10 5 m 3 , respectively, meaning that 1.5 * 10 5 m 3 of expanded volume formed underground. The total heat associated with the expanded volume is estimated to have been ≥10 14 J, similar to or exceeding the annual heat released from Yugama Crater Lake of Mt. Shirane and that from the largest eruption during the past 130 yr. Although the ejecta mass of the 2018 phreatic eruption was small, the 2018 MPCG eruption was not negligible in terms of the energy budget of Kusatsu–Shirane volcano. A water mass of 0.1–2.0 * 10 7 kg was discharged as a volcanic cloud, based on weather radar echoes, which is smaller than the mass associated with the deflation. We suggest that underground water acted as a buffer against the sudden intrusion of hydrothermal fluids, absorbing some of the fluid that ascended through the crack.


2009 ◽  
Vol 105 (3-4) ◽  
pp. 121-132 ◽  
Author(s):  
Adolfo Vicente Magaldi ◽  
Joan Bech ◽  
Jerónimo Lorente
Keyword(s):  

Author(s):  
Joaquin Cuomo ◽  
V. Chandrasekar

AbstractNowcasting based on weather radar uses the current and past observations to make estimations of future radar echoes. There are many types of operationally deployed nowcasting systems, but none of them are currently based on deep learning, despite it being an active area of research in the last few years. This paper explores deep learning models as alternatives to current methods by proposing different architectures and comparing them against some operational nowcasting systems. The methods proposed here, harnessing residual convolutional encoder-decoder architectures, reach a level of performance expected of current systems and in certain scenarios can even outperform them. Finally, some of the potential drawbacks of using deep learning are analyzed. No decay in the performance on a different geographical area from where the models were trained was found. No edge or checkerboard artifact, common in convolutional operations, was found that affects the nowcasting metrics.


1965 ◽  
Vol 46 (8) ◽  
pp. 443-447 ◽  
Author(s):  
Edwin Kessler ◽  
Jean T. Lee ◽  
Kenneth E. Wilk

Aircraft have been guided with the aid of radar data to measure turbulence in thunderstorm areas. Although turbulence is frequently encountered in areas containing highly reflective and sharp-edged echoes, no unique correspondence has been discovered between single-echo parameters and collocated within-storm turbulence. A theory embracing some of the time-dependent relationships between fields of wind and precipitation suggests that the correspondence between instantaneous distributions of radar echoes and turbulence is statistical rather than precise. Statistical bases for study of radar echo-turbulence relationships are outlined.


2007 ◽  
Vol 7 (3) ◽  
pp. 391-398 ◽  
Author(s):  
D. Bebbington ◽  
S. Rae ◽  
J. Bech ◽  
B. Codina ◽  
M. Picanyol

Abstract. Contamination of weather radar echoes by anomalous propagation (anaprop) mechanisms remains a serious issue in quality control of radar precipitation estimates. Although significant progress has been made identifying clutter due to anaprop there is no unique method that solves the question of data reliability without removing genuine data. The work described here relates to the development of a software application that uses a numerical weather prediction (NWP) model to obtain the temperature, humidity and pressure fields to calculate the three dimensional structure of the atmospheric refractive index structure, from which a physically based prediction of the incidence of clutter can be made. This technique can be used in conjunction with existing methods for clutter removal by modifying parameters of detectors or filters according to the physical evidence for anomalous propagation conditions. The parabolic equation method (PEM) is a well established technique for solving the equations for beam propagation in a non-uniformly stratified atmosphere, but although intrinsically very efficient, is not sufficiently fast to be practicable for near real-time modelling of clutter over the entire area observed by a typical weather radar. We demonstrate a fast hybrid PEM technique that is capable of providing acceptable results in conjunction with a high-resolution terrain elevation model, using a standard desktop personal computer. We discuss the performance of the method and approaches for the improvement of the model profiles in the lowest levels of the troposphere.


2013 ◽  
Vol 10 (4) ◽  
pp. 855-859 ◽  
Author(s):  
Yinguang Li ◽  
Guifu Zhang ◽  
Richard Doviak ◽  
Darcy Saxion

The scan-to-scan correlation method to discriminate weather signals from ground clutter, described in this letter, takes advantage of the fact that the correlation time of radar echoes from hydrometeors is typically much shorter than that from ground objects. In this letter, the scan-to-scan correlation method is applied to data from the WSR-88D, and its results are compared with those produced by the WSR-88D's ground clutter detector. A subjective comparison with an operational clutter detection algorithm used on the network of weather radars shows that the scan-to-scan correlation method produces a similar clutter field but presents clutter locations with higher spatial resolution.


Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1157
Author(s):  
Suzanna Maria Bonnet ◽  
Alexandre Evsukoff ◽  
Carlos Augusto Morales Rodriguez

Precipitation nowcasting can predict and alert for any possibility of abrupt weather changes which may cause both human and material risks. Most of the conventional nowcasting methods extrapolate weather radar echoes, but precipitation nowcasting is still a challenge, mainly due to rapid changes in meteorological systems and time required for numerical simulations. Recently video prediction deep learning (VPDL) algorithms have been applied in precipitation nowcasting. In this study, we use the VPDL PredRNN++ and sequences of radar reflectivity images to predict the future sequence of reflectivity images for up to 1-h lead time for São Paulo, Brazil. We also verify the feasibility for the continuous use of the VPDL model, providing the meteorologist with trends and forecasts in precipitation edges regardless of the weather event occurring. The results obtained confirm the potential of the VPDL model as an additional tool to assist nowcasting. Even though meteorological systems that trigger natural disasters vary by location, a general solution can contribute as a tool to assist decision-makers and consequently issue efficient alerts.


1989 ◽  
Vol 109 (2) ◽  
pp. 66-74
Author(s):  
Tadaomi Miyazaki ◽  
Yasushi Nishida ◽  
Shuichiro Kawamata
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document