scholarly journals GCOM-C (SHIKISAI) Second-generation Global Imager infrared observation of active volcanoes: Case studies from the Asia-Pacific region

2020 ◽  
Author(s):  
Takayuki Kaneko ◽  
Atsushi Yasuda ◽  
Kenji Takasaki ◽  
Shun Nakano ◽  
Toshitsugu Fujii ◽  
...  
Keyword(s):  
Case Studies ◽  
Large Scale ◽  
Second Generation ◽  
Lava Dome ◽  
Global Climate ◽  
Thermal Anomaly ◽  
Pyroclastic Flows ◽  
Asia Pacific ◽  
Small Scale ◽  
Infrared Observation

Abstract The GCOM-C (SHIKISAI) satellite was developed to understand the mechanisms of global climate change. The Second-generation Global Imager (SGLI) onboard GCOM-C is an optical sensor observing wavelengths from 380 nm to 12.0 µm in 19 bands. One of the notable features is that the resolution of the 1.63-, 10.8-, and 12.0-µm bands is 250 m, with an observation frequency of 2–3 days. To investigate the effective use and potential of the 250-m resolution of these SGLI bands in the study of eruptive activities, we analyzed four practical cases. As an example of large-scale effusive activity, we studied the 2018 Kilauea eruption. By analyzing the series of 10.8-µm band images using cumulative thermal anomaly maps, we could observe that the lava effused on the lower East Rift Zone, initially flowed down the southern slope to the sea, and then moved eastward. As an example of lava dome growth and generation of associated pyroclastic flows, the activity at Sheveluch between December 2018 and December 2019 was analyzed. The 1.63- and 10.8-µm bands were shown to be suitable for observing growth of the lava dome and occurrence of pyroclastic flows, respectively. We found that the pyroclastic flows occurred during periods of rapid lava dome expansion. For the study of an active crater lake, the activity of Ijen during 2019 was analyzed. The lake temperature was found to rise rapidly in mid-May and reach 38 °C in mid-June. We also analyzed the intermittent activities of small-scale Vulcanian eruptions at Sakurajima in 2019. The 1.63-µm band was useful for detecting activities that are associated with Vulcanian eruptions. Analytical results for these case studies demonstrated that the GCOM-C SGLI images are beneficial for observing various aspects of volcanic activity, and their real-time use may contribute to reducing eruption-related disasters.

scholarly journals A new infrared volcano monitoring using GCOM-C (SHIKISAI) satellite: Applications to the Asia-Pacific region

2020 ◽  
Author(s):  
Takayuki Kaneko ◽  
Atsushi Yasuda ◽  
Kenji Takasaki ◽  
Shun Nakano ◽  
Toshitsugu Fujii ◽  
...  
Keyword(s):  
Large Scale ◽  
Lava Dome ◽  
Global Climate ◽  
Thermal Anomaly ◽  
Pyroclastic Flows ◽  
Asia Pacific ◽  
Small Scale ◽  
Effusive Activity ◽  
Lake Temperature ◽  
Satellite Applications

Abstract The GCOM-C (SHIKISAI) satellite was developed to understand the mechanisms of global climate change. The Second-generation Global Imager (SGLI) onboard GCOM-C is an optical sensor observing wavelengths from 380 nm to 12.0 μm in 19 bands. One of the notable features is that the resolution of the 1.63-, 10.8-, and 12.0-µm bands is 250 m, with an observation frequency of 2-3 days. To investigate the effective use and potential of the 250-m resolution of these SGLI bands in the study of eruptive activities, we analyzed four practical cases. As an example of large-scale effusive activity, we studied the 2018 Kilauea eruption. By analyzing the series of 10.8-μm band images using cumulative thermal anomaly maps, we could observe that the lava effused on the lower East Rift Zone, initially flowed down the southern slope to the sea, and then moved eastward. As an example of lava dome growth and generation of associated pyroclastic flows, the activity at Sheveluch between December 2018 and December 2019 was analyzed. The 1.63- and 10.8-µm bands were shown to be suitable for observing growth of the lava dome and occurrence of pyroclastic flows, respectively. We found that the pyroclastic flows occurred during periods of rapid lava dome expansion. For the study of an active crater lake, the activity of Ijen during 2019 was analyzed. The lake temperature was found to rise rapidly in mid-May and reach 38 °C in mid-June. We also analyzed the intermittent activities of small-scale Vulcanian eruptions at Sakurajima in 2019. The 1.63-µm band was useful for detecting activities that are associated with Vulcanian eruptions. Analytical results for these case studies demonstrated that the GCOM-C SGLI images are beneficial for observing various aspects of volcanic activity, and their real-time use may contribute to reducing eruption-related disasters.

scholarly journals Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

2017 ◽  
Vol 10 (3) ◽  
pp. 1383-1402 ◽  
Author(s):  
Paolo Davini ◽  
Jost von Hardenberg ◽  
Susanna Corti ◽  
Hannah M. Christensen ◽  
Stephan Juricke ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Rainfall Variability ◽  
Global Climate Model ◽  
Small Scale ◽  
Low Resolution ◽  
The Impact ◽  
Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

Public Order Policing Approaches to Minimize Crowd Confrontation During Disputes and Protests in Australia

2019 ◽  
Author(s):  
David Baker
Keyword(s):  
Case Studies ◽  
Large Scale ◽  
Police Force ◽  
Asia Pacific ◽  
Public Order ◽  
Police Leadership ◽  
Police Responses ◽  
Psychological Threat ◽  
Industrial Disputes ◽  
Mega Event

Abstract Traditionally, public order clashes between police and protesters in Australia were intermittent and erratic, but police responses were often repressive and violent. By the 1990s, most police leadership was advocating a low-key strategy: one of communication and dialogue, negotiated management, and a less coercive approach to large-scale protests. This article argues that policing of demonstrations responds to the dynamics of differing protest contexts and behaviours. It explores the policing of some significant contemporary demonstrations in Australia ranging from industrial disputes to anti-globalization protests (1998 national waterfront dispute, 2007 Sydney Asia-Pacific Economic Corporation summit, and the 2014 G20 mega event in Brisbane). Although the policing approaches were markedly diverse, these case studies involved limited confrontation. Despite some notable exceptions, modern-day policing of protest in Australia has usually been non-confrontational, partly the result of police–protester liaison and dialogue. The psychological threat of police force, rather than its actual implementation, has restricted potential protest participation and limited violent clashes. A delicate and fragile balance exists between the police maintenance of order and security and the facilitation of a peaceful protest.

scholarly journals Balanced reconnection intervals: four case studies

2008 ◽  
Vol 26 (12) ◽  
pp. 3897-3912 ◽  
Author(s):  
A. D. DeJong ◽  
A. J. Ridley ◽  
C. R. Clauer
Keyword(s):  
Case Studies ◽  
Large Scale ◽  
The Other ◽  
Small Scale ◽  
Polar Cap ◽  
Reconnection Rate ◽  
Magnetospheric Convection ◽  
The Third ◽  
New Name ◽  
Definition Of

Abstract. During steady magnetospheric convection (SMC) events the magnetosphere is active, yet there are no data signatures of a large scale reconfiguration, such as a substorm. While this definition has been used for years it fails to elucidate the true physics that is occurring within the magnetosphere, which is that the dayside merging rate and the nightside reconnection rate balance. Thus, it is suggested that these events be renamed Balanced Reconnection Intervals (BRIs). This paper investigates four diverse BRI events that support the idea that new name for these events is needed. The 3–4 February 1998 event falls well into the classic definition of an SMC set forth by Sergeev et al. (1996), while the other challenge some previous notions about SMCs. The 15 February 1998 event fails to end with a substorm expansion and concludes as the magnetospheric activity slowly quiets. The third event, 22–23 December 2000, begins with a slow build up of magnetospheric activity, thus there is no initiating substorm expansion. The last event, 17 February 1998, is more active (larger AE, AL and cross polar cap potential) than previously studied SMCs. It also has more small scale activity than the other events studied here.

scholarly journals Modelling erosion and deposition in geophysical granular mass flows

2021 ◽  
Vol 52 (1) ◽  
pp. 29-32
Author(s):  
Sylvain Viroulet ◽  
Chris Johnson ◽  
Nico Gray
Keyword(s):  
Debris Flows ◽  
Large Scale ◽  
Laboratory Experiments ◽  
Pyroclastic Flows ◽  
Small Scale ◽  
Snow Avalanches ◽  
Erosion And Deposition ◽  
Granular Rheology ◽  
Scale Models ◽  
Mass Flows

During hazardous geophysical mass flows, such as rock or snow avalanches, debris flows and volcanic pyroclastic flows, a continuous exchange of material can occur between the slide and the bed. The net balance between erosion and deposition of particles can drastically influence the behaviour of these flows. Recent advances in describing the non-monotonic effective basal friction and the internal granular rheology in depth averaged theories have enabled small scale laboratory experiments (see fig. 1) to be quantitatively reproduced and can also be implemented in large scale models to improve hazard mitigation.

scholarly journals Differences between downscaling with spectral and grid nudging using WRF

2012 ◽  
Vol 12 (8) ◽  
pp. 3601-3610 ◽  
Author(s):  
P. Liu ◽  
A. P. Tsimpidi ◽  
Y. Hu ◽  
B. Stone ◽  
A. G. Russell ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Wrf Model ◽  
Global Climate Models ◽  
Small Scale ◽  
Data Set ◽  
Spectral Nudging

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

scholarly journals A Hybrid Downscaling Approach for Future Temperature and Precipitation Change

2020 ◽  
Vol 59 (11) ◽  
pp. 1793-1807 ◽  
Author(s):  
Helene Birkelund Erlandsen ◽  
Kajsa M. Parding ◽  
Rasmus Benestad ◽  
Abdelkader Mezghani ◽  
Marie Pontoppidan
Keyword(s):  
Regional Climate Model ◽  
Statistical Models ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Small Scale ◽  
Regional Climate Model Output

AbstractWe used empirical–statistical downscaling in a pseudoreality context, in which both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model, and the small-scale conditions were taken from dynamical downscaling of the same global model with a convection-permitting regional climate model covering southern Norway. This hybrid downscaling approach, a “perfect model”–type experiment, provided 120 years of data under the CMIP5 high-emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical–statistical model configurations and predictor choices and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the interannual correlation and long-term trends in seasonal 2-m temperature T2m, wet-day frequency fw, and wet-day mean precipitation μ. We found that different 30-yr calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and it provides a robust set of empirically downscaled GCM runs.

scholarly journals Detection of Deception in a Virtual World

2013 ◽  
Vol 1 (1) ◽  
pp. 51-66 ◽  
Author(s):  
Lauren B. Collister
Keyword(s):  
Case Studies ◽  
Virtual Worlds ◽  
Virtual World ◽  
Online Community ◽  
Large Scale ◽  
World Of Warcraft ◽  
Small Scale ◽  
Detection Of Deception

This work explores the role of multimodal cues in detection of deception in a virtual world, an online community of World of Warcraft players. Case studies from a five-year ethnography are presented in three categories: small-scale deception in text, deception by avoidance, and large-scale deception in game-external modes. Each case study is analyzed in terms of how the affordances of the medium enabled or hampered deception as well as how the members of the community ultimately detected the deception. The ramifications of deception on the community are discussed, as well as the need for researchers to have a deep community knowledge when attempting to understand the role of deception in a complex society. Finally, recommendations are given for assessment of behavior in virtual worlds and the unique considerations that investigators must give to the rules and procedures of online communities.

scholarly journals Multi-Task Collaboration Deep Learning Framework for Infrared Precipitation Estimation

2021 ◽  
Vol 13 (12) ◽  
pp. 2310
Author(s):  
Xuying Yang ◽  
Peng Sun ◽  
Feng Zhang ◽  
Zhenhong Du ◽  
Renyi Liu
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Small Scale ◽  
Estimation Model ◽  
Infrared Observation ◽  
Precipitation Estimation ◽  
Error Accumulation ◽  
Task Collaboration ◽  
Combination Mode

Infrared observation is an all-weather, real-time, large-scale precipitation observation method with high spatio-temporal resolution. A high-precision deep learning algorithm of infrared precipitation estimation can provide powerful data support for precipitation nowcasting and other hydrological studies with high timeliness requirements. The “classification-estimation” two-stage framework is widely used for balancing the data distribution in precipitation estimation algorithms, but still has the error accumulation issue due to its simple series-wound combination mode. In this paper, we propose a multi-task collaboration framework (MTCF), i.e., a novel combination mode of the classification and estimation model, which alleviates the error accumulation and retains the ability to improve the data balance. Specifically, we design a novel positive information feedback loop composed of a consistency constraint mechanism, which largely improves the information abundance and the prediction accuracy of the classification branch, and a cross-branch interaction module (CBIM), which realizes the soft feature transformation between branches via the soft spatial attention mechanism. In addition, we also model and analyze the importance of the input infrared bands, which lay a foundation for further optimizing the input and improving the generalization of the model on other infrared data. Extensive experiments based on Himawari-8 demonstrate that compared with the baseline model, our MTCF obtains a significant improvement by 3.2%, 3.71%, 5.13%, 4.04% in F1-score when the precipitation intensity is 0.5, 2, 5, 10 mm/h, respectively. Moreover, it also has a satisfactory performance in identifying precipitation spatial distribution details and small-scale precipitation, and strong stability to the extreme-precipitation of typhoons.

scholarly journals ASSESSMENT OF SOLAR PV POWER POTENTIAL IN THE ASIA PACIFIC REGION WITH REMOTE SENSING CONSIDERING THE EFFECTS OF HIGH TEMPERATURE, DUST AND SNOW

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 339-346
Author(s):  
J. A. Principe ◽  
W. Takeuchi
Keyword(s):  
High Temperature ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Energy Utilization ◽  
Asia Pacific ◽  
Shortwave Radiation ◽  
Small Scale ◽  
Pacific Region ◽  
Solar Pv ◽  
Asia Pacific Region

Abstract. The last half century has witnessed the increasing trend of renewable energy utilization with solar photovoltaic (PV) systems as one of the most popular option. Solar PV continues to supplement the main grid in powering both commercial establishments (mainly for reduced electricity expense) as well as residential houses in isolated areas (for basic energy requirement such as for lighting purposes). The objective of this study is to assess the available solar PV power (PPV) potential considering the effects of high temperature, dust and snow in the Asia Pacific region. The PPV potential was estimated considering the effects of the said meteorological parameters using several satellite data including shortwave radiation from Advanced Himawari Imager 8 (AHI8), MOD04 aerosol data from Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation rate from Global Satellite Mapping of Precipitation (GSMaP), air temperature from NCEP/DOE AMIP-II Reanalysis-2 data, and snow water equivalent (SWE) from Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The model is validated by comparing its outputs with the measured PV power from two solar PV installations in Bangkok, Thailand and Perth, Australia. Results show that maximum PPV is estimated at 2.5 GW (cell efficiency of 17.47%) for the region with the maximum decrease in PPV estimated to be about < 2%, 22% and 100% due to high temperature (temperature coefficient of power = 0.47%/K), dust and snow, respectively. Moreover, areas in India and Northern China were observed to experience the effects of both dust and temperature during March-April-May (MAM) season. Meanwhile, countries located in the higher latitudes were severely affected by snow while Australia by high temperature during Dec-Jan-Feb (DJF) season. The model has a mean percentage prediction error (PPE) range of 5% to18% and 7% to 23% in seasonal and monthly estimations, respectively. Outputs from this study can be used by stakeholders of solar PV in planning for small-scale or large-scale solar PV projects in the solar rich region of Asia Pacific.

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