Observations, model simulations, and reanalysis produce vast amounts of climate data. The unprecedented data volume and intrinsic complexity of geospatial statistics and analysis requires efficient analysis to investigate global problems such as climate change, natural disasters, diseases, and other environmental issues. However, this requirement poses grand challenges due to the unprecedented data volume and intrinsic complexity of geospatial statistics and analysis. Addressing these challenges requires efficient data management strategies, complex parallel algorithms and scalable computing resources.
By leveraging and seamlessly synthesizing the spatiotemporal index and query analytical framework as well as other third party resources such as NASA Web WorldWind. We are developing a A Scalable Online Visual Analytic System (SOVAS) for interactive Big Climate Data Analysis.
SOVAS Website: https://gidbusc.github.io/SCOVAS
SVOAS Portal: http://gis.cas.sc.edu/scovas