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Description
This research addresses current trends in big data, and their application to mathematical, statistical, and climate science. The first chapter review several big data visualization tools currently available, describes the utility of such tools, and introduces opportunity for improvement. The second chapter describes a toolkit for snow-cover area calculation and display based on the Interactive Multisensor Snow and Ice Mapping System (IMS). The paper uses the Tibetan Plateau (TP) region to describe the toolkit’s methods, results, and usage. The toolkit generates the time-series of the daily snow-covered area for any region over the Northern Hemisphere from 4 February 1997. The toolkit also creates maps showing snow and ice coverage. The total TP area calculated by the sum of the areas of all the grid boxes approximates the true TP area of a spherical polygon bounded by (25◦ − 45◦N) × (65◦ − 105◦E) with a difference 0.046 % for the 24 km grid and 0.033 % for the 4 km grid. The differences in the snow-cover area reported by the 24 km and 4 km grids vary between -2.34 and 6.24 %. The temporal variations of the daily TP snow cover are displayed in time series from 4 February 1997 to present with 4 km and 24 km resolutions. The third chapter describes Argovis, a fully fledged web app for the Argo dataset. Argovis at www.argovis.com provides easy access to Argo profile data and gridded products for both scientists and the general public. Users query a MongoDB database filled with Argo profiles. Users input a set of latitude-longitude coordinates, date range, and pressure range. The Argovis app sends a response: either raw JSON or HTML page with profile measurements and metadata that fall within these queries. Time series and gridded products are generated by API, offering a tool for researchers to tailor the web app to their specific needs. The topics covered in this thesis entail big data sets and how to visualize and retrieve data quickly. Open source toolkits and web apps for data visualization and accessibility pertain big data sets in general.
