Simulation and visualization of the cyclonic storm chapala over the arabian sea: a case study
KAUST DepartmentEarth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
KAUST Visualization Laboratory (KVL)
Physical Science and Engineering (PSE) Division
Red Sea Research Center (RSRC)
Online Publication Date2016-12-01
Print Publication Date2016-11
Permanent link to this recordhttp://hdl.handle.net/10754/622602
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AbstractWe use the high resolution Weather Research and Forecasting (WRF) model to predict the characteristics of an intense cyclone, Chapala, which formed over the Arabian Sea in October/November 2015. The implemented model consists of two-way interactive nested domains of 9 and 3km. The prediction experiment of the cyclone started on 1200UTC of 26 October 2015 to forecast its landfall and its intensity based on NCEP global model forecasting fields. The results show that the movement of Chapala is well reproduced by our model up to 72 hours, after which track errors become significant. The intensity and cloud features of the extreme event as well as the distribution of hydrometeors is well represented by the model. All the characteristics including eye and eye-wall regions, mesoscale convective systems and distribution of different hydrometers during the lifetime of Chapala are very well simulated. The model output results in several hundred gigabytes of data, we analyze and visualize these data using state of the art computational and visualization software for representing different characteristics of Chapala and to verify the accuracy of the model. We further demonstrate the usefulness of a 3D virtual reality environment and its potential importance in decision-making system development.
CitationTheubl T, Dasari HP, Hoteit I, Srinivasan M (2016) Simulation and visualization of the cyclonic storm chapala over the arabian sea: a case study. 2016 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT). Available: http://dx.doi.org/10.1109/KACSTIT.2016.7756074.
SponsorsThis work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia and the Saudi ARAMCO-KAUST Marine Environmental Research Center (SAKMERC). This research made use of the resources of the Supercomputing Laboratory at KAUST.
Journal2016 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT)