217 leaves : ill. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.1972) from the Dept. of Physics, University of Adelaide

Thesis (Ph.D. 1972) from the Dept. of Physics, University of Adelaide.

Thesis (M.S.)--University of Wisconsin--Madison, 1966. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

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Thesis (M.S.)--Wisconsin. / Includes bibliographical references (leaf 35).

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Radio occultation measurements from global navigation satellite systems, such as the Global Positioning System (GPS), represent a new source of numerical weather prediction information. Conventionally, radio occultation measurements are inverted using an Abel integral transform to obtain a profile of refractivity, and subsequently pressure, on geometric height levels via the hydrostatic relation. Although accurate temperature/water vapour retrievals are possible with a background estimate of water vapour/temperature, it is not possible to retrieve these quantities simultaneously using this method. Also, using this method, refractivity retrieval errors are introduced by assuming no horizontal structure local to ray periapsis (the spherical symmetry assumption), and the method is subject to 'first-guess' errors when the hydrostatic relation is initialised. Results from an investigation into how departures from spherical symmetry affect the performance of the Abelian inverse method are presented. It is shown that realistic horizontal humidity inhomogeneities can compensate for or reinforce horizontal temperature inhomogeneities, and therefore are important to consider in both the forward and inverse modelling. Using the results from this experiment, regression coefficients are fitted in an effort to predict temperature and refractivity retrieval errors from the horizontal temperature and humidity structure local to the measurement. The largest contribution to the predicted errors is shown to be from the local parabolic component of the horizontal structure, but is found to predict only a small fraction of the total error. A non-linear optimal estimation inverse method is presented with which it is possible to retrieve simultaneously profiles of temperature, humidity and surface pressure. Using this method, the measurements are assimilated with a priori information utilising error estimates of the a priori information and the measurements. The method implemented is validated using an ensemble of numerical simulations. Real observations from the GPS/MET pilot experiment are used to retrieve profiles of temperature, humidity and surface pressure which are validated using collocated ECMWF and NMC model analyses, radiosondes, and the Abelian inversion results. Temperature and refractivity comparisons between the optimal estimate and results from the Abelian inverse method show good agreement at high latitudes for all altitudes, resolving small-scale structure not shown by the model analyses. At low latitudes there is good agreement above the tropopause, below which a temperature bias ensues between the Abelian inversion and all other correlative data. Retrieved values for water vapour and surface pressure compare well with model analyses and collocated radiosondes. Biases between the UKMO and the ECMWF model analyses are consistent with known differences between the two models at the time of the dataset.

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