Development of a Networked Robotic System for Disaster Mitigation – Test Bed Experiments for Remote Operation over Rough Terrain and High Resolution 3D Geometry Acquisition

2006 ◽  
pp. 415-425
Author(s):  
Kazuya Yoshida ◽  
Keiji Nagatani ◽  
Kiyoshi Kiyokawa ◽  
Yasushi Yagi ◽  
Tadashi Adachi ◽  
...  
Keyword(s):  
High Resolution ◽  
Robotic System ◽  
Disaster Mitigation ◽  
Rough Terrain ◽  
Test Bed ◽  
Remote Operation ◽  
3D Geometry

scholarly journals Assessing Impacts of the High-Frequency Assimilation of Surface Observations for the Forecast of Convection Initiation on 3 April 2014 within the Dallas–Fort Worth Test Bed

2018 ◽  
Vol 146 (11) ◽  
pp. 3845-3872 ◽  
Author(s):  
Nicholas A. Gasperoni ◽  
Xuguang Wang ◽  
Keith A. Brewster ◽  
Frederick H. Carr
Keyword(s):  
High Resolution ◽  
High Frequency ◽  
Weather Prediction ◽  
National Research Council ◽  
Future Application ◽  
Fort Worth ◽  
Sources Of Information ◽  
Test Bed ◽  
Convection Initiation ◽  
The Impact

Abstract The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas–Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case. Data denial experiments tested the impact of high-frequency (5 min) assimilation of nonconventional data on the timing and location of CI and subsequent storm evolution. Results showed nonconventional observations were necessary to capture details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations in sensitive areas along the dryline gradient. Accurate metadata were found to be crucial toward the future application of nonconventional observations in high-resolution assimilation and forecast systems.

scholarly journals The effect of increased resolution of geostationary satellite imageries on predictability of tropical thunderstorms over Southeast Asia

2018 ◽  
Author(s):  
Kwonmin Lee ◽  
Hye-Sil Kim ◽  
Yong-Sang Choi
Keyword(s):  
High Resolution ◽  
Southeast Asia ◽  
Lead Time ◽  
Geostationary Satellite ◽  
Disaster Mitigation ◽  
Mature Stage ◽  
Lead Times ◽  
Low Resolution ◽  
High Resolution Imagery ◽  
Heavy Damage

Abstract. Tropical thunderstorms cause heavy damage to property and lives, and there is a strong interest in advancing the predictability of thunderstorms with more precise satellite observations. Using high-resolution (2 km and 10 minutes) imageries from the geostationary satellite (Himawari-8) recently launched over Southeast Asia, we examine how early the thunderstorms can be predicted compared to the low-resolution (4 km and 30 minutes) imageries of the former satellite. We compare the lead times for eight thunderstorms that occurred in August 2017 between high- and low-resolution imageries. These thunderstorms are identified by pixels with a brightness temperature at 10.45 μm (BT11) gradually decreasing by more than 5 K per 10 minutes (15 K per 30 minutes) compared to the previous imagery. The lead time is then calculated as the time passed from the initial to the mature stage of the thunderstorm signal, based on the time series of a minimum BT11 of these pixels. The lead time is found to be 100–180 minutes for the high-resolution imagery, while it is only found to be 30 minutes if detectable at all for the low-resolution imagery. This result suggests that the high-resolution imagery is essential for substantial disaster mitigation because of its ability to note an alarm more than two hours ahead of a matured thunderstorm.

scholarly journals Effects of high-resolution geostationary satellite imagery on the predictability of tropical thunderstorms over Southeast Asia

2019 ◽  
Vol 19 (10) ◽  
pp. 2241-2248
Author(s):  
Kwonmin Lee ◽  
Hye-Sil Kim ◽  
Yong-Sang Choi
Keyword(s):  
High Resolution ◽  
Southeast Asia ◽  
Lead Time ◽  
Geostationary Satellite ◽  
Disaster Mitigation ◽  
Lead Times ◽  
Low Resolution ◽  
Initial State ◽  
High Resolution Imagery ◽  
Significant Damage

Abstract. Tropical thunderstorms cause significant damage to property and lives, and a strong research interest exists in the advances and improvement of thunderstorm predictability by satellite observations. Using high-resolution (2 km and 10 min) imagery from the geostationary satellite, Himawari-8, recently launched over Southeast Asia, we examined the earliest possible time for the prediction of thunderstorms as compared to the potential of low-resolution (4 km and 30 min) imagery of the former satellite. We compared the lead times of high- and low-resolution imageries of 60 tropical thunderstorms that occurred in August 2017. These thunderstorms were identified by the decreasing trend in the 10.45 µm brightness temperature (BT11) by over 5 K per 10 min for the high-resolution imagery and 15 K per 30 min for the low-resolution imagery. The lead time was then calculated over the time from the initial state to the mature state of the thunderstorm, based on the time series of a minimum BT11 of thunderstorm pixels. The lead time was found to be 90–180 min for the high-resolution imagery, whereas it was only 60 min (if detectable) for the low-resolution imagery. These results indicate that high-resolution imagery is essential for substantial disaster mitigation owing to its ability to raise an alarm more than 2 h ahead of the mature state of a tropical thunderstorm.

Corrigendum to “A test-bed for optimizing high-resolution single particle reconstructions” [J. Struct. Biol. 163 (2008) 29–39]

2010 ◽  
Vol 171 (2) ◽  
pp. 244
Author(s):  
Scott M. Stagg ◽  
Gabriel C. Lander ◽  
Joel Quispe ◽  
Neil R. Voss ◽  
Anchi Cheng ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Particle ◽  
Test Bed ◽  
Resolution Single

scholarly journals Storm Surge Inundation Analysis with Consideration of Building Shape and Layout at Ise Bay by Maximum Potential Typhoon

2020 ◽  
Vol 8 (12) ◽  
pp. 1024
Author(s):  
Masaki Nimura ◽  
Shuzo Nishida ◽  
Koji Kawasaki ◽  
Tomokazu Murakami ◽  
Shinya Shimokawa
Keyword(s):  
Global Warming ◽  
High Resolution ◽  
Sea Level ◽  
Storm Surge ◽  
High Velocity ◽  
High Tide ◽  
Disaster Mitigation ◽  
Ise Bay ◽  
Building Shape ◽  
Tide Level

Global warming is feared to cause sea-level rise and intensification of typhoons, and these changes will lead to an increase in storm surge levels. For that reason, it is essential to predict the inundation areas for the maximum potential typhoon and evaluate the disaster mitigation effect of seawalls. In this study, we analyzed storm surge inundation of the inner part of Ise Bay (coast of Aichi and Mie Prefecture, Japan) due to the maximum potential typhoon in the future climate with global warming. In the analysis, a high-resolution topographical model was constructed considering buildings’ shape and arrangement and investigated the inundation process inside the seawall in detail. The results showed that buildings strongly influence the storm surge inundation process inside the seawall, and a high-velocity current is generated in some areas. It is also found that closing the seawall door delays the inundation inside the seawall, but the evacuation after inundation is more difficult under the seawall doors closed condition than opened condition when the high tide level exceeds the seawall.

scholarly journals RANDOM-FOREST-ENSEMBLE-BASED CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGES AND NDSM OVER URBAN AREAS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 887-892
Author(s):  
X. F. Sun ◽  
X. G. Lin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Random Forest ◽  
Urban Areas ◽  
Vegetation Index ◽  
Conditional Random Field ◽  
Remote Sensing Data ◽  
Intermediate Step ◽  
Sensing Data ◽  
3D Geometry

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample’s category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

Development of a Networked Robotic System for Disaster Mitigation

2008 ◽  
pp. 453-462 ◽  
Author(s):  
Keiji Nagatani ◽  
Kazuya Yoshida ◽  
Kiyoshi Kiyokawa ◽  
Yasushi Yagi ◽  
Tadashi Adachi ◽  
...  
Keyword(s):  
Robotic System ◽  
Disaster Mitigation
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