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Modelling and statistical analysis of spatial-temporal rainfall fields.

Northrop, Paul James; (1996) Modelling and statistical analysis of spatial-temporal rainfall fields. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Rainfall has been characterised by a hierarchical structure, the basic elements being rain cells—small areas of relatively intense precipitation. There is evidence that there is a tendency for new cells to form in the immediate vicinity of existing cells so that cells cluster in space and time. The use of cluster point processes is therefore a natural approach when mod-elling rainfall. In the model proposed in this thesis an elliptical rain cell, with random area, intensity and duration, is associated with each point of a process in which points cluster in both space and time. For most applications only a single layer of clustering, cells within storms is required. The spatial-temporal models formulated are generalisations of existing single site (i.e. purely temporal) models and are extensions of simpler models proposed by Cox and Isham (1988). Such models are fitted to radar data consisting of a temporal sequence of arrays containing the average rainfall intensities over spatial grid squares. Fitting the model to data is achieved using a moments based method. Assessment of the fit of the model is then made by comparing the observed and predicted values of properties not used in the fitting procedure. Additionally, the ability of the models to reproduce similar behaviour to data with regard to aspects not specifically built in to the model is investigated. For example, the distribution of rainfall over a range of spatial and temporal scales, the spatial structure of rainfall fields and its evolution in time, are examined.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Modelling and statistical analysis of spatial-temporal rainfall fields.
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Thesis digitised by ProQuest.
URI: https://discovery.ucl.ac.uk/id/eprint/10105099
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