TY - BOOK

T1 - Advanced analysis and visualisation techniques for atmospheric data

AU - Hyde, Richard William

PY - 2017

Y1 - 2017

N2 - Atmospheric science is the study of a large, complex system which is becoming increasinglyimportant to understand. There are many climate models which aim to contribute to thatunderstanding 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 varioussources of measurements are used. Some are ground based, others airborne sondes, but oneof the primary sources is from measurement instruments on board aircraft. Flight planningfor 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 previoussorties. There is little scope for adjusting the flight parameters during the sortie based on thedata 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 teamof mission scientists with the added complications of disseminating observations betweenshifts.Earth’s atmosphere is a non-linear system, whereas the data gathered is sampled atdiscrete temporal and spatial intervals introducing a source of variance. Clustering dataprovides a convenient way of grouping similar data while also acknowledging that, for eachdiscrete sample, a minor shift in time and/ or space could produce a range of values whichlie within its cluster region. This thesis puts forward a set of requirements to enable thepresentation 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 theproposed requirements is not found. New clustering algorithms are developed to achieve thesegoals. These novel clustering algorithms are brought together, along with other visualizationtechniques, into a software package which is used to demonstrate how the analyses canprovide information to mission scientists in flight. The ability to carry out offline analyses onhistorical data, whether to reproduce the online analyses of the current sortie, or to providecomparative analyses from previous missions, is also demonstrated. Methods for offlineanalyses of historical data prior to continuing the analyses in an online manner are alsoconsidered.The original contributions in this thesis are the development of five new clusteringalgorithms which address key challenges: speed and accuracy for typical hyper-ellipticaloffline clustering; speed and accuracy for offline arbitrarily shaped clusters; online dynamicand evolving clustering for arbitrary shaped clusters; transitions between offline and onlinetechniques and also the application of these techniques to atmospheric science data analysis.

AB - Atmospheric science is the study of a large, complex system which is becoming increasinglyimportant to understand. There are many climate models which aim to contribute to thatunderstanding 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 varioussources of measurements are used. Some are ground based, others airborne sondes, but oneof the primary sources is from measurement instruments on board aircraft. Flight planningfor 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 previoussorties. There is little scope for adjusting the flight parameters during the sortie based on thedata 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 teamof mission scientists with the added complications of disseminating observations betweenshifts.Earth’s atmosphere is a non-linear system, whereas the data gathered is sampled atdiscrete temporal and spatial intervals introducing a source of variance. Clustering dataprovides a convenient way of grouping similar data while also acknowledging that, for eachdiscrete sample, a minor shift in time and/ or space could produce a range of values whichlie within its cluster region. This thesis puts forward a set of requirements to enable thepresentation 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 theproposed requirements is not found. New clustering algorithms are developed to achieve thesegoals. These novel clustering algorithms are brought together, along with other visualizationtechniques, into a software package which is used to demonstrate how the analyses canprovide information to mission scientists in flight. The ability to carry out offline analyses onhistorical data, whether to reproduce the online analyses of the current sortie, or to providecomparative analyses from previous missions, is also demonstrated. Methods for offlineanalyses of historical data prior to continuing the analyses in an online manner are alsoconsidered.The original contributions in this thesis are the development of five new clusteringalgorithms which address key challenges: speed and accuracy for typical hyper-ellipticaloffline clustering; speed and accuracy for offline arbitrarily shaped clusters; online dynamicand evolving clustering for arbitrary shaped clusters; transitions between offline and onlinetechniques and also the application of these techniques to atmospheric science data analysis.

KW - Computer Science

KW - Atmospheric Science

KW - Clustering

KW - Arbitrary Shapes

KW - Data Anlysis

KW - Data Visualization

U2 - 10.17635/lancaster/thesis/103

DO - 10.17635/lancaster/thesis/103

M3 - Doctoral Thesis

PB - Lancaster University

ER -