The use of Meteosat satellite data for spatial rainfall estimations and hydrological simulations

Satellite information, in combination with conventional point source measurements, can be a valuable source of information. This thesis is devoted to the spatial estimation of areal rainfall over a region using both the measurements from a dense and sparse network of rain-gauges and images from the meteorological satellites. A primary concern is to study the effects of such satellite assisted rainfall estimates on the performance of rainfall-runoff models. Low-cost image processing systems and peripherals are used to process and manipulate the data. Both secondary as well as primary satellite images were used for analysis. The secondary data was obtained from the in-house satellite receiver and the primary data was obtained from an outside source. Ground truth data was obtained from the local Water Authority. A number of algorithms are presented that combine the satellite and conventional data sources to produce areal rainfall estimates and the results are compared with some of the more traditional methodologies. The results indicate that the satellite cloud information is valuable in the assessment of the spatial distribution of areal rainfall, for both half-hourly as well as daily estimates of rainfall. It is also demonstrated how the performance of the simple multiple regression rainfall-runoff model is improved when satellite cloud information is used as a separate input in addition to rainfall estimates from conventional means. The use of low-cost equipment, from image processing systems to satellite imagery, makes it possible for developing countries to introduce such systems in areas where the benefits are greatest.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Pathways utilized by heavy metal pollutants in urban stormwater runoff

The research concerned the assessment of the pathways utilized by heavy metal pollutants in urban stormwater runoff. A separately sewered urban residential catchment of approximately 107 hectares in Chelmsley Wood, north-east Birmingham was the subject of the field investigation. The catchment area, almost entirely residential, had no immediate industrial heavy metal input, however, industry was situated to the north of the catchment. The perimeter of the catchment was bounded by the M6 motorway on the northern and eastern sides and was believed to contribute to aerial deposition. Metal inputs to the ground surface were assumed to be confined to normal suburban activities, namely, aerial deposition, vehicular activity and anthropological activities. A programme of field work was undertaken over a 12 month period, from July 1983 to July 1984. Monthly deposition rates were monitored using a network of deposit cannisters and roadside sediment and soil samples were taken. Stormwater samples were obtained for 19 separate events. All samples were analysed for iron, lead, zinc, copper, chromium, nickel and cadmium content. Rainfall was recorded on site with additional meteorological data obtained from local Meteorological Offices. Use was made of a simple conceptual model designed for the catchment to substantiate hypotheses derived from site investigations and literature, to investigate the pathways utilized for the transportation of heavy metals throughout the catchment.

High-frequency conductivity of optically excited charge carriers in hydrogenated nanocrystalline silicon investigated by spectroscopic femtosecond pump-probe reflectivity measurements

We report an investigation into the high-frequency conductivity of optically excited charge carriers far from equilibrium with the lattice. The investigated samples consist of hydrogenated nanocrystalline silicon films grown on a thin film of silicon oxide on top of a silicon substrate. For the investigation, we used an optical femtosecond pump-probe setup to measure the reflectance change of a probe beam. The pump beam ranged between 580 and 820nm, whereas the probe wavelength spanned 770 to 810nm. The pump fluence was fixed at 0.6mJ/cm2. We show that at a fixed delay time of 300fs, the conductivity of the excited electron-hole plasma is described well by a classical conductivity model of a hot charge carrier gas found at Maxwell-Boltzmann distribution, while Fermi-Dirac statics is not suitable. This is corroborated by values retrieved from pump-probe reflectance measurements of the conductivity and its dependence on the excitation wavelength and carrier temperature. The conductivity decreases monotonically as a function of the excitation wavelength, as expected for a nondegenerate charge carrier gas.

Gravimetric and density profiling using the combination of surface acoustic waves and neutron reflectivity

A new approach is described herein, where neutron reflectivity measurements that probe changes in the density profile of thin films as they absorb material from the gas phase have been combined with a Love wave based gravimetric assay that measures the mass of absorbed material. This combination of techniques not only determines the spatial distribution of absorbed molecules, but also reveals the amount of void space within the thin film (a quantity that can be difficult to assess using neutron reflectivity measurements alone). The uptake of organic solvent vapours into spun cast films of polystyrene has been used as a model system with a view to this method having the potential for extension to the study of other systems. These could include, for example, humidity sensors, hydrogel swelling, biomolecule adsorption or transformations of electroactive and chemically reactive thin films. This is the first ever demonstration of combined neutron reflectivity and Love wave-based gravimetry and the experimental caveats, limitations and scope of the method are explored and discussed in detail.