Climate simulations provide valuable information to represent the situations of the atmosphere, ocean, and land. Increasingly advanced computational technologies and Earth observation capabilities have enabled the climate models to have higher spatial and temporal resolution, providing an ever realistic coverage of the Earth (Yang et al., 2017). The high spatiotemporal resolution provides us the opportunity to more precisely pinpoint and identify the occurrence of extreme weather events, e.g., cyclones (Hewson and Titley, 2010), dust storms (Yu and Yang, 2017), and ocean eddies (Matsuoka et al., 2016). The precise identification, along with the temporal movement tracking, enables the scientists a better understanding of the spatiotemporal dynamics of these events (Yu et al., 2018), e.g., their common origins, their temporal (diurnal, monthly, seasonal, or inter-annual) trends in a regional or global scale, or their impacting spatial scales.
This session is established to discuss (but not limited to) the following topics:
1. Understand the spatiotemporal dynamics of extreme weather events
2. Explore the relationship of these events with other physical and social factors
3. Heterogeneous data integration to enhance the disaster situation awareness, response and mitigation
Hewson, T.D. and Titley, H.A., 2010. Objective identification, typing and tracking of the complete life‐cycles of cyclonic features at high spatial resolution. Meteorological Applications, 17(3), pp.355-381.
Matsuoka, D., Araki, F., Inoue, Y. and Sasaki, H., 2016. A new approach to ocean eddy detection, tracking, and event visualization–application to the northwest Pacific Ocean. Procedia Computer Science, 80, pp.1601-1611.
Yang, C., Yu, M., Hu, F., Jiang, Y. and Li, Y., 2017. Utilizing cloud computing to address big geospatial data challenges. Computers, Environment and Urban Systems, 61, pp.120-128.
Yu, M. and Yang, C., 2017. A 3D multi-threshold, region-growing algorithm for identifying dust storm features from model simulations. International Journal of Geographical Information Science, 31(5), pp.939-961.
Yu, M., Yang, C. and Jin, B., 2018. A framework for natural phenomena movement tracking–Using 4D dust simulation as an example. Computers & Geosciences.
|Presenter||Wanyun Shao*, The University of Alabama, Barry Keim, Louisiana State University, Siyuan Xian, Princeton University, Flood Hazards and Perceptions – A Comparative Study of Two Cities in Alabama||15||8:00 AM|
|Presenter||Yasin Wahid Rabby*, University of Tennessee, Yingkui Li, University of Tennessee, Knoxville, Landslide Susceptibility Map of Rangmati Municipality, Bangladesh, investigated using Three Sampling Strategies in the Multivariate Logistic Regression Model||15||8:15 AM|
|Presenter||Rubayet Mostafiz*, Louisiana State University, Carol J. Friedland, Associate Professor, Bert Turner Department of Construction Management, Louisiana State University, Robert V. Rohli, Professor, Department of Oceanography & Coastal Sciences, Louisiana State University, Melanie Gall, Co-Director and Research Professor, Center for Emergency Management and Homeland Security, Arizona State University, Joshua M. Gilliland, Instructor, Department of Geography, Geology, and the Environment, Slippery Rock University, Assessing Risk of Atmospheric Natural Hazards at the Census Block Level||15||8:30 AM|
|Presenter||Larry Stanislawski*, USGS - CEGIS, Tyler Brockmeyer, USGS - CEGIS, Ethan Shavers, USGS - CEGIS, Barbara P Buttenfield, University of Colorado-Boulder, Barry J Kronenfeld, Eastern Illinois University, Geomorphic Classification to Support Hydrographic Feature Extraction||15||8:45 AM|
|Presenter||Yinan He*, University of North Carolina - Charlotte, Gang Chen, University of North Carolina - Charlotte, Assessing the effects of fire, disease and climate on forest dynamics using multi-sensor remote sensing||15||9:00 AM|
|Presenter||Yazidhi Bamutaze*, Makerere University, Effect of morphometric and terrain factors on spatial patterns of landslide hazards on Mt. Elgon, Uganda||15||9:15 AM|
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