Using adaptively coupled models and high-performance computing for enabling the computability of dust storm forecasting

  • Authors:

  • Qunying Huang;Chaowei Yang;Karl Benedict;Abdelmounaam Rezgui;Jibo Xie;Jizhe Xia;Songqing Chen

  • Affiliations:

  • Center of Intelligent Spatial Computing for Water/Energy Sciences and Department of Geography, GeoInformation Sciences, George Mason University, Fairfax, VA, USA;Center of Intelligent Spatial Computing for Water/Energy Sciences and Department of Geography, GeoInformation Sciences, George Mason University, Fairfax, VA, USA;Earth Data Analysis Center, University of New Mexico, Albuquerque, NM, USA;Center of Intelligent Spatial Computing for Water/Energy Sciences and Department of Geography, GeoInformation Sciences, George Mason University, Fairfax, VA, USA;Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, China;Center of Intelligent Spatial Computing for Water/Energy Sciences and Department of Geography, GeoInformation Sciences, George Mason University, Fairfax, VA, USA;Department of Computer Science, George Mason University, Fairfax, VA, USA

  • Venue:
  • International Journal of Geographical Information Science
  • Year:
  • 2013

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Abstract

Forecasting dust storms for large geographical areas with high resolution poses great challenges for scientific and computational research. Limitations of computing power and the scalability of parallel systems preclude an immediate solution to such challenges. This article reports our research on using adaptively coupled models to resolve the computational challenges and enable the computability of dust storm forecasting by dividing the large geographical domain into multiple subdomains based on spatiotemporal distributions of the dust storm. A dust storm model Eta-8bin performs a quick forecasting with low resolution 22 km to identify potential hotspots with high dust concentration. A finer model, non-hydrostatic mesoscale model NMM-dust performs high-resolution 3 km forecasting over the much smaller hotspots in parallel to reduce computational requirements and computing time. We also adopted spatiotemporal principles among computing resources and subdomains to optimize parallel systems and improve the performance of high-resolution NMM-dust model. This research enabled the computability of high-resolution, large-area dust storm forecasting using the adaptively coupled execution of the two models Eta-8bin and NMM-dust.