Advanced Analysis and Visualisation Techniques for Atmospheric Data
Creators: name: Hyde, Richard William
Publisher: name: Lancaster University
Year: 2017
Subjects: Computer Science
Atmospheric Science
Clustering
Arbitrary Shapes
Data Anlysis
Data Visualization

description: Atmospheric science is the study of a large, complex system which is
      becoming increasingly
      
      important to understand. There are many climate models which aim to
      contribute to that
      
      understanding by computational simulation of the atmosphere. To generate
      these models,
      
      and to confirm the accuracy of their outputs, requires the collection of
      large amounts of data.
      
      These data are typically gathered during campaigns lasting a few weeks,
      during which various
      
      sources of measurements are used. Some are ground based, others airborne
      sondes, but one
      
      of the primary sources is from measurement instruments on board aircraft.
      Flight planning
      
      for the numerous sorties is based on pre-determined goals with
      unpredictable influences,
      
      such as weather patterns, and the results of some limited analyses of data
      from previous
      
      sorties. There is little scope for adjusting the flight parameters during
      the sortie based on the
      
      data received due to the large volumes of data and difficulty in
      processing the data online.
      
      The introduction of unmanned aircraft with extended flight durations also
      requires a team
      
      of mission scientists with the added complications of disseminating
      observations between
      
      shifts.
      
      Earth’s atmosphere is a non-linear system, whereas the data gathered is
      sampled at
      
      discrete temporal and spatial intervals introducing a source of variance.
      Clustering data
      
      provides a convenient way of grouping similar data while also
      acknowledging that, for each
      
      discrete sample, a minor shift in time and/ or space could produce a range
      of values which
      
      lie within its cluster region. This thesis puts forward a set of
      requirements to enable the
      
      presentation of cluster analyses to the mission scientist in a convenient
      and functional manner.
      
      This will enable in-flight decision making as well as rapid feedback for
      future flight planning.
      
      Current state of the art clustering algorithms are analysed and a solution
      to all of the
      
      proposed requirements is not found. New clustering algorithms are
      developed to achieve these
      
      goals. These novel clustering algorithms are brought together, along with
      other visualization
      
      techniques, into a software package which is used to demonstrate how the
      analyses can
      
      provide information to mission scientists in flight. The ability to carry
      out offline analyses on
      
      historical data, whether to reproduce the online analyses of the current
      sortie, or to provide
      
      comparative analyses from previous missions, is also demonstrated. Methods
      for offline
      
      analyses of historical data prior to continuing the analyses in an online
      manner are also
      
      considered.
      
      The original contributions in this thesis are the development of five new
      clustering
      
      algorithms which address key challenges: speed and accuracy for typical
      hyper-elliptical
      
      offline clustering; speed and accuracy for offline arbitrarily shaped
      clusters; online dynamic
      
      and evolving clustering for arbitrary shaped clusters; transitions between
      offline and online
      
      techniques and also the application of these techniques to atmospheric
      science data analysis.