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This work investigates the impact of rain on the power production of a wind turbine, including structural flexibility effects. Wind turbines are subjected to numerous conditions, including the change of weather, such as snow, hail and rain. In the available literature, the main focus has been given to the effects of snow rather than rain. In this study, a strong one-way fluid-structure interaction (FSI) approach was utilised to investigate the interconnected characteristics of the fluid and structural domains. The Reynolds-averaged Navier-Stokes (RANS) approach incorporated with multiple reference frame (MRF), discrete phase modelling (DPM) and wall-film model were used to investigate the aerodynamic behaviour of the selected geometries. The NACA 0012 and S809 aerofoils were used as the validation test cases, and then similar methodology was applied for the NREL Phase VI wind turbine. The structural response of the wind turbine was accounted by a three dimensional finite element method (FEM) utilising the shell theory. Parametric studies are included in this work to analyse the effect of rain characteristics towards the surrounding airflow. It was found that the presence of rain negatively affected the aerodynamics of these selected geometries, measured in terms of lift, drag and torque production. For the NREL Phase VI wind turbine, a maximum torque reduction by 19.97% was obtained when the freestream velocity was varied. The aerodynamic penalties were more evident by increasing the rainfall rate up to 18.0% drop of torque respectively from rainfall rate of 3.75 g/m3 to 30.0 g/m3. Nonetheless, higher torque production was found for larger raindrop diameter. An increase by 11.27% of torque was observed when the diameter is increased from 3 mm to 6 mm. In terms of structural response, the thrust showed a dominant influence. Additionally, the amount of the total deformation was dominated by the z deformation in the direction of the thrust. The blades deflections were found between 3.71% and 27.30%, measured in terms of total deflections. Both blades experienced different amount of deformation because they depended on the intensity of the rain droplets at their vicinity, due to the behaviour of the rain particles. Then, the associated stress analysis of the wind turbine was measured in term of von-Mises stress. Similarly, higher von-Mises stress was recorded for the rainy case than the dry condition. As a conclusion, the presence of rain has detrimental effects on the aerodynamic and structural characteristics of a wind turbine.

Tropical cyclones (TC)s are a major natural hazard and the jet stream is closely linked to mid-latitude storm activity. Both TCs and the jet stream can therefore have a significant impact on society, yet the extent to which ocean variability impacts on TCs and the jet stream is far from understood. This thesis aims to improve understanding of when and how the ocean modulates storm activity. The emphasis is on how airsea interactions and variability of the Atlantic Ocean circulation impact on the frequency and intensity of tropical cyclones, together with the ocean influence on the northern hemisphere jet stream. Ocean drivers of recent active Atlantic hurricane seasons are found to take two forms: late winter changes in the ocean circulation related to a reduced Atlantic Meridional Overturning Circulation and late spring/early summer changes in the airsea heat flux. Over the Atlantic, the TC rainfall rate (mm/hr) increases by 6% for a 1°C SST rise in the Main Development Region (MDR). Over land, however, the rainfall rate increases by over 30% for a 1°C rise in SST in the MDR and appears linked mainly to the increase in TC wind speed. In the subtropical Atlantic, around Bermuda, average TC intensity is found to be increasing at 5kts per decade linked to rising ocean temperatures in the region. The prediction of TC potential intensity is also found to be closer to actual intensity using the average temperature through the top 50m layer (?) as opposed to SST, with the improvement proportional to the SST - ? temperature difference. For the northern hemisphere jet stream, the ocean acts to reduce the seasonal range of jet latitude variability, particularly over the North Atlantic where the oceanic Meridional Heat Transport (MHT) is greatest. Interannual to decadal variability in jet latitude and speed is most evident in the North Pacific in winter where the Pacific Decadal Oscillation explains 50% of the variance in jet latitude since 1940. On multidecadal timescales trends vary significantly on a regional basis. The largest increasing trends in jet latitude and jet speed are observed in the North Atlantic, with increases in winter of 3° and 4.5ms-1, respectively. There are no trends in jet latitude or speed over the North Pacific.

Disasters caused by floods are a major cause of losses of properties and lives. The unpredictability in weather conditions due to changing weather patterns do not only lead to flooding but also contribute to water resource management problems. Rapid development in many tropical countries, like in Malaysia, has resulted in the loss of natural floodplains leading to an increase in flooding and water shortage. Sufficient advanced flood warning system that can save lives and properties can be developed using accurate river model. The work reported in this thesis has made significant contributions in the prediction of river flow rate based on rainfall rate in the catchment area using Artificial Neural Network (ANN). The proposed approach models the non-linear process of the rainfall-runoff in a wide variety of catchment area conditions. This study demonstrates significant improvement in the accuracy and reliability of water resource management by using ANN modelling to predict river flow rate. It also shows ANN as a fast and adaptable approach that is suitable for river flow rate modelling that does not need detailed geographical information of the catchment area. Its attractiveness is in its ability to adapt to changing conditions and therefore does not become outdated like conventional hydrology models. The research shows that river flow rate is a better parameter to be used for an early flood warning system as it is more sensitive to rainfall rate compared to the river level which is used in conventional flood warning systems. The study has also shown that ANN with a feed-backward network with one hidden layer provides the best results and it is able to produce river flow rate prediction up to 132 hours with root mean square error of 0.02 m3/s . This is a significant contribution as the flood warning system currently used in Malaysia can only predict flooding within 8 to 24 hours. The work in this thesis can assist the authorities to manage water from dams thereby effectively managing floods and ensuring sufficient water for domestic and agricultural use. The findings of this research has already been presented to the Malaysian government agency responsible for managing waterways and dams.

Climate exerts a profound control on the processes that shape landscapes and produce the sedimentary deposits with which we can interpret the Earth's history. However, we lack a complete understanding of how sensitive tectonically-active, eroding landscapes are to climate and climate change. How does a simple sediment routing system react to a change in rainfall rate? Can mountainous landscapes respond quickly enough to preserve a record of high-frequency climate changes, e.g., glacial-interglacial cycles? What effect does headwater glaciation have on downstream sediment characteristics? Can we quantify past climate changes using the sedimentological properties of terrestrial stratigraphy? Geologists lack complete answers to these questions, among many others. Theoretical work, using physical first principles and numerical models, has produced a range of hypotheses about landscape sensitivity to climate, but we now need empirical data to test and make sense of these ideas. This thesis therefore explores empirically how geomorphological and sedimentological records have responded to climatic gradients across time and space. In the first part of this thesis, the extent to which spatial climate gradients are recorded by the longitudinal geometry of river channels is investigated. I use a simple stream power erosion law to predict an inverse relationship between channel steepness and average precipitation rate, and then test this theory using data from a variety of study areas and two complementary analytical approaches. Climate is found to be an important control on river longitudinal geometry across a range of climatic and tectonic conditions, in a way that conforms to existing theoretical knowledge and also allows the climatic signal to be discriminated from tectonics. This work therefore demonstrates that a widely-used geomorphological measurement - the channel steepness index - is quantifiably sensitive to climate in tectonically-active areas, and these findings offer a new explanation for geographic variations in channel steepness that cannot be explained by tectonics alone. The second part of this thesis focuses on the sensitivity of simple mountain catchment-alluvial fan systems to climate changes associated with the last glacial-interglacial cycle, as expressed in the south-western United States. First, eight debris flow-dominated systems located in the south-eastern Sierra Nevada, California are examined. I establish a detailed chronostratigraphic model for these fan systems by building upon and integrating existing exposure age constraints reported by others, and additionally developing a new technique for estimating the ages of these fan deposits. This technique is based on calibrating the rate of enlargement of common weathering fractures observed in exposed surface boulders, which are shown to widen at a steady and predictable rate post-deposition, and can be used as reliable age indicators for > 100 ka at this location. Using the detailed temporal record of deposition established for these fan systems, a large ( > 30,000 particle) grain size data set that spans the last full glacial-interglacial cycle is examined. I demonstrate that debris flow grain size is a highly sensitive recorder of past climate changes, capturing the glacial-interglacial cycle as a sustained and high-amplitude time series with a rapid response timescale of < 10 ka. These debris flow deposits become significantly coarser-grained with warming and overall drying of the climate, and this thesis outlines quantitative reasons why this signal can be attributed to increasing storm intensity with warming. Finally, these debris flow-dominated systems are contrasted with two carefully-selected stream flow-dominated fan systems in Death Valley, California. Using measures of down-system grain size fining and a self-similarity model of sediment calibre, sediment flux estimates during arid interglacial and wetter glacial climate conditions are derived and compared. This study shows that a decrease in average rainfall rate of ~ 30 % produced a corresponding decrease in sediment flux of ~ 20 %. However, I also demonstrate the circumstances in which signal buffering due to incision and sediment recycling destroys this climate signal. Consequently, this thesis demonstrates both the causes and results of complexity in the relationship between climate change, geomorphology, and well-dated terrestrial sedimentary records. Ultimately, this is an expression of how sediment transport processes, tectonics, the magnitude-frequency distribution of rainfall, and other factors interact to generate different climate responses in different systems. Nevertheless, for both geomorphic and sedimentological records examined here, I demonstrate that the effects of climate can be quantified clearly: channel steepness can be quantified as a function of rainfall rate; debris flow sedimentology can be quantified as a function of storm intensity; and alluvial fan sedimentology in Death Valley can be quantified as a function of glacial-interglacial climate changes. Essentially, this thesis finds that terrestrial landscapes are sensitive to known climate changes in the recent geological past, and this result is profoundly important for improving our ability to decode geomorphic and stratigraphic archives effectively. The data and ideas within this Ph.D. research provide useful opportunities for (i) testing and updating our models of how sediment routing systems respond to climate, (ii) extracting quantitative information about past climates from the sedimentary record, and (iii) predicting the effects of future climate changes on the landscape.

This thesis provides a new space-time statistical rain model and a novel space-time interpolation approach for planning and dimensioning wide area high frequency satellite communication networks. Key characteristics of rainfall rate fields are modelled. These include detailed description of: (i) the first order statistical distribution, (ii) the spatial and temporal correlation functions of rainfall rate and, and (iii) the probability of rain/no-rain. With a focus on their relevance to satellite and terrestrial microwave network design, the key contribution of this study is the assessment of the impact of varying spatial and temporal integration lengths on these quantities. The issue of how these key characteristics of rainfall rate field change with different area sizes are analysed in this thesis and it is novel. A simple but accurate interpolation approach of the key characteristic parameters is presented in this thesis. The novelty of the proposed technique is that it does not rely directly on the radar/raingauge derived rainfall rate data like traditional models do but rather on fitted coefficients and computed rain characteristics. This thesis proposes rain parameter contour maps and databases covering the whole of Western Europe from which users can conveniently obtain the key rain characteristic parameters at any location within the studied area. More speculatively, the space-time interpolation approach can extrapolate to rain parameters at space-time resolutions shorter than those in the NIMROD databases. The results have been validated by comparing them with those from ITU Rec model and measurements by NIMROD rain radar. In addition, a Graphical User Interface (GUI) software has been provided that allows users to interact with the proposed model. The user can easily obtain the information of the key rain characteristics at different space-time scales by simply inputting the longitude, latitude, space resolution and time resolution of the location of interest. The detailed results are then automatically calculated and displayed by the software significantly facilitating rain rate study.

This thesis presents the development of various methods for measuring rainfall rates using horizontally-pointing millimetre-wave radars. This work builds from the combination of a T-matrix scattering model that allows the scattering from almost arbitrarily pro led rotationally symmetric particles to be calculated, and drop shape models that allow the effects of temperature and pressure on the shape to be taken into account. Many hours of rain data have been collected with 38 and 94 GHz FMCW radars, as well as with a disdrometer and weather station. These have been used to develop single- and dual-frequency techniques for measuring rainfall rate. A temperature, polarisation and attenuation corrected application of simple power-law relationships between reflectivity and rainfall rate has been successfully demonstrated at 38 GHz. However, at 94 GHz it has been found that more detailed functions relating reflectivity, attenuation and rainfall rate are beneficial. A reflectivity-based determination of attenuation has been adapted from the literature and successfully applied to the 94 GHz data, improving the estimate of rainfall rate at longer ranges. The same method for estimating attenuation has also been used in a dualfrequency technique based on the ratio of the extinction coefficients at 38 and 94 GHz, but with less success. However, a dual-frequency reflectivity ratio based approach has been successfully developed and applied, producing good estimates of rainfall rate, as well as reasonable estimates of two drop-size distribution parameters. Simulations of radar measurements of airborne volcanic ash have also been carried out, demonstrating that for most reasonable measurement configurations the optimal frequencies would typically be 35 GHz or 94 GHz, not the more commonly used 3-10 GHz. It has also been shown that various existing millimetre-wave radars could be used to detect ash. Finally, there is a discussion of the optimal frequencies for dual-frequency measurement of volcanic ash.

Current frequency allocations below Ku- band are becoming increasingly congested. The problem continues to grow as the use of telecommunications becomes more pop- ular. In order to compensate for increased in demand, telecommunication operating frequencies have to be raised, yielding larger channel capacity. However, raising the operating frequency has the adverse result of intensifying the attenuation effects of the troposphere. At high frequencies, such as V-band, rain is known to cause the most severe attenuation. It has been shown that model data for numerical weather prediction, combined with propagation prediction models, can be used to forecast telecommunication link atten- uation. The forecast of attenuation can be used to improve the effectiveness of fade mitigation techniques. A key problem in determining rain attenuation from rainfall rate is the considerable variability of the raindrop size distribution (DSD). Based on the analysis of disdrometer and numerical weather prediction model data, the aim of this work is to constrain the parameters of the DSD and ultimately generate proba- bilistic forecasts of attenuation. Using disdrometer analysis, a relationship between mean raindrop size and rainfall rate is established, and a link between drop concentration and rainfall rate is also demon- strated. The DSD is shown to vary with season and shows some small relationships with meteorological parameters. It is concluded that, despite some relation of the DSD with rainfall rate and meteorological parameters, there are too many variables to conclusively recreate the DSD. Analysis of the attenuation-rainfall rate relationship illustrates that variability due to the DSD increases with frequency above 40GHz. At 30-40GHz the DSD is shown to have little impact on the attenuation-rainfall rate relationship. Finally, this thesis examines the horizontal structure of rain cells in the UK, demonstrating the suitability of the EXCELL model and illustrating that rain cells are more elliptical in the UK.

In this thesis, a midlatitude climatology of cut-off lows was done and then used for studying the synoptic-scale patterns associated with extreme rainfall rate events due to the passage of cut-off lows in central Chile. The climatology was developed using the NCEP-NCAR reanalysis data from 1960 to 2017 to detect 200- and 500-hPa cut-off lows. It was found that the seasonality of cut-off lows is level-dependent in both the Northern and Southern Hemispheres. Additionally, the climatology detected a positive trend in the yearly number of events especially in the Southern Hemisphere that is not associated with any of the natural climate variability modes. The results of these analyses are summarized in paper 1. For paper 2 the applicability of the climatology was tested by examining 500-hPa cut-off lows impinging upon central Chile between 1979 and 2017. From these cut-off lows, only those that were associated with extreme rainfall rates were selected for further analyses. Those cut-off lows associated with high precipitation rate (WET events) had a different moisture distribution than cut-off lows associated with low precipitation rate (DRY events). Whereas in WET events the moisture plume is mostly located equatorward of the cut-off low centre, in DRY events the moisture plume is mostly located westward and poleward. For WET events, its associated configuration of the flow facilitates the input of moisture to the upper-level low's leading edge. In contrast, for DRY events the moisture input is mostly directed towards the polar edge of the cyclonic circulation associated with the cut-off low, thus preventing moisture reaching the leading edge. Additionally, WET cut-off lows were less persistent than DRY cut-off lows even though both groups tended to occur more frequently during the rainy season, with no statistically significant trend at the interannual timescale.

Quantification of hurricane hazard, which includes wind, rainfall and storm surge, is essential for engineering design as well as financial loss assessment. The objective of this study is to develop a stochastic simulation framework which integrates the simulation process of hurricane rainfall and wind hazard. This study is divided into two parts. The main objective of the first part is to develop a method for the estimation of the hurricane wind field parameters radius to maximum wind speeds (R_max) and Holland B parameter (B). The second part develops a stochastic model to simulate hurricane rainfall, which is named the ‘NormRain’ model. The first part develops a hurricane wind speed computation method, which is validated by comparing with wind speed observations from meteorological stations. Then, a method to estimate R_max and B for historical storms is developed using this wind speed computation algorithm. Finally, based on the analysis of historical storms using the R_max and B estimation method, equations for stochastic simulation of R_max and B time history are developed. These equations can simulate the temporal correlation (i.e. correlation of the simulated value in current time-step with the previous timesteps) of R_max and B, which is an improvement over other commonly used method. Besides R_max and B estimation, another important application of the wind speed computation framework is to develop a database of hurricane wind speed hazard curves for the 30 Eastern US states, which is expected to aid the research of performance-based wind engineering. The hurricane rainfall distribution about the storm center can be very asymmetric and irregular, with high rainfall rates far from storm center. The existing statistical models for hurricane rainfall usually estimate the mean rainfall rate profile, and do not explicitly consider the total rainfall volume. However, the mean rainfall rate profiles cannot account for high localized rainfall rates which can be much larger than the mean value and can contribute a significant portion of the total rainfall volume. To overcome this limitation, this study develops a rainfall simulation model which explicitly simulates total rainfall volume using central pressure, relative vorticity and total precipitable water. Since the irregular shape of hurricane rain field is difficult to describe using equations, this study simulates the hurricane rain field using the concept of normalized rain field shape from historical storms. The hurricane simulation model can thus simulate realistic hurricane rain fields. The hurricane rain simulation model ‘NormRain’ developed in the second part of this study consists of two parts. The first part estimates the total rainfall volume and extent of hurricane rain field at any time-step, and the second part determines how the rainfall rates associated with this rainfall volume are distributed within the rain field extent.

In a changing climate with an increasing risk of flooding, developing a sustainable approach to flood management is paramount. Sustainable Drainage Systems (SuDS) present a change in thinking with regards to drainage; storing water in the urban environment as opposed to rapidly removing it to outflows. The Non-Statutory Standards for SuDS (DEFRA 2015a) presented a requirement for all developments to integrate SuDS in their design to reduce runoff. This research models the impact on water quantity of combining different SuDS devices to demonstrate their success as a flood management system, as compared to conventional pipe based drainage. The research uses MicroDrainage®, the UK industry standard flood modelling tool which has an integrated SuDS function, to simulate the role of SuDS in a management train. As space is often cited as the primary reason for rejecting SuDS, determining the most effective technique at reducing runoff is critical. Detention basins were concluded as being highly effective at reducing peak flow (150 l/s when combined with swales), however Porous Pavement Systems (PPS) was nearly twice as effective per m3, reducing peak flow by up to 0.075 l/s/m3 compared to 0.025 l/s/m3. This therefore suggests that both detention basins and PPS should be high priority devices when developing new sites, but that no matter what combination of modelled SuDS are installed a reduction in runoff in comparison to conventional drainage can be achieved. A SuDS decision support tool was developed to assist design in MicroDrainage® by reducing the time spent determining the number of SuDS required for a site. The tool uses outputs from MicroDrainage® to rapidly predict the minimum and maximum peak flow for a site, in comparison to greenfield runoff, based on the site parameters of area, rainfall rate, infiltration, combined with the planned SuDS. The tool was underpinned by a model analysis for each site parameter and each SuDS device, which produced r2 values >0.8, with 70% above 0.9. This ensured a high level of confidence in the outputs, enabling a regression analysis between runoff and each site parameter and SuDS device at the 99% confidence level, with the outputs combined to create the tool. The final aspect of the research validated MicroDrainage® to analyse the accuracy of the software at predicting runoff. Using field data from Hamilton, Leicester, and laboratory data for PPS and filter drains, a comparison could be made with the output from MicroDrainage®. The field data created a Nash-Sutcliffe Efficiency (NSE) of 0.88, with filter drains and PPS providing an NSE of 0.98 and 0.94 respectively. This demonstrates the success with which MicroDrainage® predicts runoff and provides credibility to the outputs of the research. Furthermore, it offers SuDS specialists the confidence to use MicroDrainage® to predict runoff when using SuDS.

Precipitation as an essential component of the hydrologic cycle has a great importance to be measured accurately due to various applications such as hydrologic modeling, extreme weather analysis, and water resources management. Among different methods, meteorological satellites are one of the instruments that are widely used for precipitation estimation in fine spatial and temporal resolution. Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Network Cloud Classification System (PERSIANN-CCS) uses infrared (IR) data from Geostationary Earth Orbit (GEO) satellites to retrieve precipitation based on relationship between clout top temperature (Tb) and rainfall rate (RR) using a neural network technique. The complexity of Tb-RR relationship for estimating precipitation causes uncertainty in PERSIANN-CCS rainfall product. This research is focused on improving PERSIANN-CCS rainfall retrieval using several approaches: 1) Bias adjustment of PERSIANN-CCS rainfall estimates using PMW satellite rainfall data: Using multi satellite data can enhance the quality of rainfall estimation considerably; in this research we have combined the rainfall data from PERSIANN-CCS and PMW rainfall to enhance the bias of PERSIANN-CCS precipitation estimates. The results showed improvement of rainfall estimation during summer and winter time. 2) Increasing the rainfall detection by including warm clouds rainfall: PERSIANN-CCS currently cannot detect rainfall from clouds with temperature warmer than 253 K. This study explores the impacts of increasing the temperature threshold on precipitation estimation. The results show that increasing the threshold level can improve the PERSIANN-CCS rainfall detection. 3) Generating a probabilistic framework for precipitation retrieval: The current version of PERSIANN-CCS retrieves precipitation based on the exponential function fitted to Tb-RR. The major assumption behind this relationship is that the heavier rainfalls are associated with colder clouds which cause underestimation of warmer clouds and overestimation of colder clouds rainfall. The probabilistic approach uses the corresponding sample relationship between cloud temperature and rainfall rate. The model is evaluated during a full summer season which showed improvement in both detection and estimation of rainfall in compare with the current PERSIANN-CCS algorithm.

This thesis presents an informatics-based approach to dissect observed or simulated meteorological data by the synoptic-scale weather conditions, and the local-scale geographical characteristics of the data locality relative to the direction of synoptic alr-flow. The approach can be used to investigate climate variability. or to assess model performance, and is applied in two contrasting study areas; Cumbria, northwest England, for 400" rainfall gauges, and South Georgia, sub-Antarctica, for a single temperature record. For Cumbria, the method investigates patterns in the frequencies and characteristics of precipitating weather types for six distinct sub-regions defined using GIS techniques; background-coastal, windward-lowland, windward-upland, leeward-upland, leeward-lowland and secondary-upland. From 1961-2007, the total winter rainfall associated with south-westerly and westerly weather types Increased, particularly In upland regions. These increases result from an increased frequency of these weather types, and for westerlies, a change In the weather type characteristics towards higher rainfall rates. This change is linked to higher mean temperatures and wind speeds associated with the positive trend In the North Atlantic Oscillation Index. Additionally, the performance of the Weather Research and Forecasting model is evaluated for westerly weather types for different conditions of temperature, wind speed, and relative humidity. The model under-predicts the daily rainfall rate in all sub-regions except the secondary-upland, where the model over-predicts, suggesting an incorrect representation of atmospheric processes. For South Georgia, analyses from 1920 to 2009 reveal how climatic changes between periods of glacier advance, glacier retreat, and the recent decade, are composed of alterations in the temperature and frequency of regional air-masses. The conditional method summarises large datasets In order to provide detailed insight into the relationship between local and synoptic-scale atmospheric processes. The method Is cost-effective, user-friendly, and portable between study areas of different sizes and data availability. As such, the scope for further applications is considerable.

The main aim of this project is to investigate the characteristics of the propagation of laser beams through the atmosphere at the wavelengths of 0.83 and 1.55[mu]m across a 4km path in central London, a densely urbanised terrain. The thesis reports practical measurements of the propagation characteristics in clear air and in also in rain and mist conditions. Meteorological measurements of rainfall rate and wind speed were also made to supplement the propagation measurements. Measurements were also made at 1.55[mu]m with the transmitter modulated at 155Mbits/s. The analysis of the results is divided into four sections. (1) The statistical and spectral analysis of amplitude scintillations and angle of arrival in dry weather, (2) in rain, (3) modelling of the rainfall induced attenuation of the received optical power and (4) the performance evaluation of the prototype of a 155Mbits/s digital free space optical communications system operating at 1.55[mu]m. Results for studies (1) and (2) indicate that the gamma is the best distribution for the received amplitude scintillations for varying turbulence conditions in clear air and also in rain conditions. The normal distribution is the best fit for angle of arrival data, regardless of the strength of turbulence and weather. Level crossing statistics of amplitude scintillations are presented for clear air conditions. Spectral analysis of the scintillations and angle of arrival showed results that confirm theoretical assumptions in dry but not in rain conditions. No evidence of saturation of scintillations was found. In study (3), cumulative distributions of attenuation and rainfall for a year of measurements are presented and the rainfall induced attenuation is evaluated. Statistical comparison of the performances of both wavelengths in rain are shown and indicate that the 0.83[mu]m system has a slightly better overall performance. For study (4), reliability and quality analysis results are presented. Availability figures show values as high as 94.5[percent]. Analysis of bit error rates using level crossing statistics showed an approximate overall 92[percent]-95[percent] probability of error-free operation for the system.

Rivers form elaborate drainage networks with morphologies that express the unique environments in which they developed. My research seeks to understand how drainage networks evolve in low-gradient, poorly-drained landscapes by integrating internally-drained areas, referred to as non-contributing area (NCA), through time. I assessed the erosional processes of channel growth and NCA integration by conducting experiments in a small-scale drainage basin. My experiments developed channels by overland flow or seepage erosion to varying extents depending on the substrate composition, rainfall rate, and relief of the drainage basin. These factors mediated whether the drainage basin primarily routed precipitation to channels by the surface or subsurface, causing more channelization by overland flow or seepage erosion, respectively. Overland flow channels formed at the onset of experiments and expanded over a majority of the basin area by forming broad dendritic networks. Large surface water contributing areas to overland flow channels supported numerous first-order channels that integrated more NCA than seepage erosion. When overland flow was the dominant process, channels integrated NCA at a similar, consistent rate under all experimental conditions. Seepage erosion began later in experiments after channels had incised enough for exfiltrating groundwater to initiate mass wasting of headwalls. Periodic mass wasting of channel heads caused them to assume an amphitheater-shaped morphology. Since groundwater drove seepage erosion, seepage-driven channel heads had smaller surface water contributing areas that captured less NCA than overland flow. The experimental results provide insight into drainage networks that formed in glacial sediment throughout the Central Lowland province (USA) and other areas affected by Pleistocene continental glaciation. The region has broad areas of subtle topography comprised of glacial sediments in which rivers are creating drainage networks. The geologic diversity, coupled with changing climate, vegetation, and other characteristics of the landscape, may have driven different processes of channel development through time. Consequently, drainage networks throughout the region represent distinct, complex histories of channel development by different processes that resulted in varying degrees of network integration.

The estimation of weather parameters such as attenuation and rainfall rates from weather radar data has been based mainly on deterministic regression models. The applications of a Bayesian approach to weather parameters classification and estimation have also been limited by a single Gaussian assumption. A computational intelligence model, i.e., Gaussian mixture model (GMM), is introduced in this work to characterize the prior distribution of weather parameters and the corresponding radar observation variables. Since a GMM would converge to any given distribution as the number of mixture increases, it provides an efficient way to accommodate extra information from antenna and frequency diversities and an `omnipotent' solution to extract and model the `knowledge' from training data. Hydrometeor classification and weather parameters estimation through a Bayesian approach are also made possible by the precisely represented prior distribution. A linear Bayesian estimator based on GMM, namely the Gaussian Mixture Parameter Estimator (GMPE), is then developed and tested in applications such as drop size distribution (DSD) retrieval, rainfall rate estimation and attenuation correction. The advantages of GMPE include 1) it is a `best' estimator in terms of minimum-variance, unbiased performance; 2) it can easily include/exclude different radar observation variables and remains a `best' estimator; 3) it provides a general framework that is applicable to different radar-meteorological applications. GMPE is further extended to explore the spatial relations with a Kalman Filter structure. Applications of the Kalman filter GMPE to rainfall rate estimation at X-band are analyzed and discussed.

Detection of unexpectedly high concentrations of agricultural pollutants in ground water have inspired investigations of the role of preferential movement of chemicals through agricultural soils. This research focuses on preferential flow and solute transport processes and the effects of agricultural management practices on these processes. Experimental methods for monitoring preferential flow and solute transport in the field as well as a stochastic, physically-based model for predicting water flow and transport of non-reactive chemicals in heterogeneous soils with naturally occurring macropores were developed and evaluated. Field experiments, aimed at monitoring the occurrence of preferential flow and solute transport, were conducted in a conventionally-tilled and a no-till soybean field in the Coastal Plain of Virginia. A rainfall simulator was used to apply a one-hour storm at rates of 5.0, 6.5 and 7.5 cm/hr to six 1.83 by 1.83 m plots. Chloride was added to the water to serve as a non-reactive tracer. Electrical conductivity equipment provided a useful method for monitoring solute transport. The moisture and solute conditions, observed during a 28-hour period after the start of the rainfall event, clearly indicated the occurrence of preferential flow and solute movement in the field plots. The variability of the solute concentrations in the field plots was generally higher in the no-till plots than in the conventionally-tilled plots. The plots that received rain at 6.5 and 7.5 cm/hr showed more variability than the plots that received rain at 5 cm/hr. The observed solute concentrations indicated that if the solute transport would have taken place by advection only, 61% of the solute transport in the conventionally-tilled plots and 50% of the solute transport in the no-till plots could be attributed to preferential flow. A physically-based, finite element model for simulating flow and solute transport in variably-saturated soils with macropores (MICMAC) was developed. Flow and solute transport are described by the Richards' equation and the convection-dispersion equation. Flow in the macropores is described by the Hagen-Poiseuille equation. An axisymmetric coordinate system is used to simulate the flow and solute transport from the macropore into the surrounding soil matrix, assuming a vertically oriented, surface-vented, cylindrical macropore. Flow and solute transport between the macropore and the soil matrix are driven by the pressure head at the macropore-matrix boundary. To assess the natural heterogeneity of the soil properties a stochastic component was added to the model. Flow and solute transport at the field scale were simulated by regarding the field as a collection of statistically independent, non-interacting vertical soil columns, using Monte Carlo simulation. The sensitivity analysis of the model indicated that, for a soil with macropores, the model is most sensitive to the saturated water content of the soil matrix, the initial moisture content, and the rainfall rate. The model is not very sensitive to the macropore dimensions. Examination of the stochastic approach indicated that the representation of a heterogeneous field as a collection of non-interacting stream columns may substantially underestimate water and solute leaching. A change of 5% in the soil properties of the neighboring soil columns may underpredict the solute leaching, 24 hours after a rainstorm, by 157% for a soil column with a macropore, and by 58% for a soil column without a macropore. These differences decreased to 47% and 8%, respectively, 168 hours after the rainfall. Field application of the model suggested that the model underestimates the leaching of water and solutes from the root zone. However, the computed results were substantially better than the results obtained when no preferential flow component was included in the model. The model performed best under conditions that favored preferential flow, i.e., a high rainfall rate and high initial moisture conditions. The simulated and observed solute concentrations in the root zone agreed reasonably well, although the maxima of the observed data were generally higher than those of the simulated data.

Single column models have been used to help develop algorithms for remote sensing retrievals. Assumptions in the single-column models may affect the assumptions of the remote sensing retrievals. Studies of the melting layer that use single column models often assume environments that are near or at water saturation. This study investigates the effects of evaporation upon melting particles to determine whether the assumption of negligible mass loss still holds within subsaturated melting layers. A single column, melting layer model is modified to include the effects of sublimation and evaporation upon the particles. Other changes to the model include switching the order in which the model loops over particle sizes and model layers; including a particle sedimentation scheme; adding aggregation, accretion, and collision and coalescence processes; allowing environmental variables such as the water vapor diffusivity and the Schmidt number to vary with the changes in the environment; adding explicitly calculated particle temperature, changing the particle terminal velocity parameterization; and using a newly-derived effective density-dimensional relationship for use in particle mass calculations. Simulations of idealized melting layer environments show that significant mass loss due to evaporation during melting is possible within subsaturated environments. Short melting distances, accelerating particle fall speeds, and short melting times help constrain the amount of mass lost due to evaporation while melting is occurring, even in subsaturated profiles. Sublimation prior to melting can also be a significant source of mass loss. The trends shown on the particle scale also appear in the bulk distribution parameters such as rainfall rate and ice water content. Simulations incorporating observed melting layer environments show that significant mass loss due to evaporation during the melting process is possible under certain environmental conditions. A profile such as the first melting layer profile on 10 May 2011 from the Midlatitude Continental Convective Clouds Experiment (MC3E) that is neither too saturated nor too subsaturated is possible and shows considerable mass loss for all particle sizes. Most melting layer profiles sampled during MC3E were too saturated for more than a dozen or two of the smallest particle sizes to experience significant mass loss. The aggregation, accretion, and collision and coalescence processes also countered significant mass loss at the largest particles sizes because these particles are efficient at collecting smaller particles due to their relative large sweep-out area. From these results, it appears that the assumption of negligible mass loss due to evaporation while melting is occurring is not always valid. Studies that use large, low-density snowflakes and high RH environments can safely use the assumption of negligible mass loss. Studies that use small ice particles or low RH environments (RH less than about 80%) cannot use the assumption of negligible mass loss due to evaporation. Retrieval algorithms may be overestimating surface precipitation rates and intensities in subsaturated environments due to the assumptions of negligible mass loss while melting and near-saturated melting layer environments.

Weather radar (radio detection and ranging) is a specialized meteorological tool used to sample and track meteorological objects. This tool is critical for meteorologists and public decision-makers to inform and provide for their constituents in a timely manner, often with the protection of lives and property on the line. With the application of using meteorological and geospatial data in the realm of geographic information systems (G.I.S.), the task of blending the two sciences to inhibit further research and dissemination of information occurs. This study focuses on the creation and implementation of a new geospatial tool, the Radar and Rainfall Analyzed in GIS (R2AIn-GIS) tool. The R2AIn-GIS tool was built upon the initial concepts from Zhang and Srinivasan’s (2010) NEXRAD validation and calibration (NEXRAD-VC) tool for G.I.S. R2AIn-GIS is updated to support the latest software features present in the geospatial world as well as analyze dual-polarization radar products. To test the R2AIn-GIS tool, a warm seasonal precipitation study along with statistical analysis was performed over the Glacial Ridge National Wildlife Refuge in Minnesota, the largest prairie and wetland restoration site. Utilizing rain gauges operated by the United States Geological Survey, warm season precipitation events from 24 May 2012 to 31 August 2013 were analyzed using the R2AIn-GIS tool. The R2AIn-GIS tool calculates the values from various dual-polarization radar products in conjunction with the recorded precipitation gauges to provide a detailed depiction of the weather event. Statistical tests including several iterations of multiple-linear regression of various combinations of dual-polarization radar variables allowed determination of rainfall rate prediction equations over the study area. This contributes to the body of radar literature regarding the best prediction equations for other locations. Unlike treatments in prior literature, most of the various assumptions in multiple linear regression are considered herein. Based off the findings of the various statistical tests that adhere to the linear regression assumptions, regression models utilizing both reflectivity and correlation coefficient were the best models found during this study. These two variables had statistical significant p-values and their Durbin-Watson scores were among the highest even compared with the other radar variables of differential reflectivity and specific differential phase. Models including the radar variables reflectivity and correlation coefficient were found to be heteroscedastic along with the highest R Squared values. While the overall rainfall amounts were too small in terms of effective precipitation sampling, the results still positively contribute to the literature and provides the opportunity for future work.

Specific differential phase shift is one of three new base products that have become available to operational weather forecasters since the WSR-88D upgrades were completed in May 2013. Though most often used as a component in quantitative precipitation estimates, this research focused on determining if KDP is strong enough of a stand-alone product for meteorologists to use in weather warning decision-making. Radar and observation data from 17 convective storms and 8 locations during two land-falling tropical storms were studied. It was shown for convective storms that the single greatest KDP pixel value for each radar scan during the studied storms had a stronger relationship to observed rainfall rate than the highest base reflectivity pixel. It was also shown that current methods of precipitation estimate routinely overestimate rainfall amounts, while a new method that utilizes a Z-R relationship that uses only the base reflectivity values associated with the maximum KDP value provides a rainfall amount closer to in situ observations. These results lead to the conclusion that, using the methods utilized in this research, KDP is indeed strong enough to be used as a stand-alone product for forecasters.

As Deep Convective Systems (DCSs) are responsible for most severe weather events, increased understanding of these systems along with more accurate satellite precipitation estimates will improve NWS (National Weather Service) warnings and monitoring of hazardous weather conditions. A DCS can be classified into convective core (CC) regions (heavy rain), stratiform (SR) regions (moderate-light rain), and anvil (AC) regions (no rain). These regions share similar infrared (IR) brightness temperatures (BT), which can create large errors for many existing rain detection algorithms. This study assesses the performance of the National Mosaic and Multi-sensor Quantitative Precipitation Estimation System (NMQ) Q2, and a simplified version of the GOES-R Rainfall Rate algorithm (also known as the Self-Calibrating Multivariate Precipitation Retrieval, or SCaMPR), over the state of Oklahoma (OK) using OK MESONET observations as ground truth. While the average annual Q2 precipitation estimates were about 35% higher than MESONET observations , there were very strong correlations between these two data sets for multiple temporal and spatial scales. Additionally, the Q2 estimated precipitation distributions over the CC, SR, and AC regions of DCSs strongly resembled the MESONET observed ones, indicating that Q2 can accurately capture the precipitation characteristics of DCSs although it has a wet bias . SCaMPR retrievals were typically three to four times higher than the collocated MESONET observations, with relatively weak correlations during a year of comparisons in 2012. Overestimates from SCaMPR retrievals that produced a high false alarm rate were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated MESONET stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the SCaMPR false alarm rate of retrieved precipitation especially over non-precipitating (anvil) regions of a DCS. Preliminary testing of this new algorithm to identify precipitating area has produced significant improvements over the current SCaMPR algorithm. This modified version of SCaMPR can be used to provide precipitation estimates in gaps of radar and rain gauge coverage to aid in hydrological and flood forecasting.

During the past two decades, the accuracy of rainfall retrieval based on passive microwave observations has been greatly improved, particularly over ocean. However, rainfall retrieval over land remains to be problematic. The objective of this study is to develop a new rainfall retrieval algorithm that provides better rainfall estimates over land. Toward that end, in the first part of this study, we focus on better understanding three key physical aspects which significantly influence the algorithm development, including signature from both high and low frequencies, the surface emissivity effect and rainfall profile structure. Although it has been long believed that the dominant signature of over land rainfall is the brightness temperature depression caused by ice scattering at high microwave frequencies (e.g., 85 GHz), our results in chapter 3 showed that the brightness temperature combinations from 19 and 37 GHz, i.e., V19-V37 (the letter V denotes vertical polarization, and the numbers denote frequency in GHz. Similar notations are used hereafter) or V21-V37 can explain ~10% more variance of near surface rainfall rate than the V85 brightness temperature. A plausible explanation to this result is that in addition to ice scattering signature, the V19-V37 channel contains liquid water information as well, which is more directly related to surface rain than ice water aloft. In addition, to better utilized the information from low frequency, we analyzed the instantaneous microwave land surface emissivity (MLSE) and its response to the previous rainfall (Chapter 4). Current rainfall retrieval algorithm over land has not yet taken the MLSE effect into consideration. Results showed that over grass, closed shrub and cropland, previous rainfall can cause the horizontally-polarized 10 GHz brightness temperature (TB) to drop by as much as 20 K with a corresponding emissivity drop of approximately 0.06, whereby previous rain exhibited little influence on the emissivity over forest due to the dense vegetation. We developed a technique to estimate the emissivity underneath precipitating radiometric scenes. Further, in chapter 5 the relationship between water paths and the surface rain is evaluated. Results showed that corresponding to a similar surface rainrate ice water path has large spatial variability, and the most prominent characteristic for the ice water path spatial distribution is the contrast between land and ocean. On average, the correlation (R2) between ice water path and surface rainrate is also larger over land than over ocean. Over the majority of land areas, R2 is ~0.36, with the exception of arid regions and the Indian subcontinent (~0.25). In the second part of this study (chapter 6 and chapter 7), a new Principal Component Analysis (PCA) based Bayesian algorithm is proposed to take full advantage all the brightness temperature observations. Results from this algorithm was compared with that from the TRMM facility algorithm. The unique features of the new retrieval algorithm are (1) physical parameters, including surface temperature, land cover type, elevation, freezing level height and storm height, are used to categorize the land surface conditions and rainfall profile structures. (2) the covariance matrix in the Bayesian framework is calculated based on real observations and is perfectly diagonal through the Principal Component Analysis transformation. It is demonstrated that the retrieved surface rain rate agrees much better with observations from TRMM precipitation Radar, compared to the results from TRMM facility algorithm over land. Particularly, no obvious over-estimations are observed when rainrate is less than 10 mm/hr. Validation using one year data show that the correlation between retrieved rainrate and observations is 0.73, while it is 0.65 between retrieved rainrate by TRMM facility algorithm and observations. The root mean square error (RMSE) is lowered by about 35%. In terms of the computational time, this algorithm is several order faster than other published Bayesian based algorithms. In addition, this algorithm can be conveniently adapted to other satellite platforms (e.g., SSM/I) due to its location and season independent characteristics.

Ground clutter is a long standing issue in radar meteorology, considering that it can bring significant bias to the estimations of weather moments, polarimetric parameters, rainfall rate, hydrometeor identification, etc. Bayes' theorem is introduced and applied to signal processing of weather radar signals which distinguishes it from existing empirical methods to improve data quality. Five ground clutter detection algorithms are discussed, which are the Spectrum Clutter Identification (SCI), Simple Bayesian Classifier applied to the Dual-Scan discriminants (SBC-DS), test statistic obtained from the Generalized Likelihood Ratio Test (GLRT), Simple Bayesian Classifier applied to the Dual-Pol discriminants (SBC-DP), and Simple Bayesian Classifier applied to the Dual-Pol Dual-Scan discriminants (SBC-DPDS). One ground clutter filtering algorithm is developed, which is the Bi-Gaussian Model Adaptive Processing (BGMAP). The BGMAP algorithm will be applied to the clutter contaminated gates identified by ground clutter detection algorithms. The performances of the clutter detection and filtering algorithms are evaluated using the data collected by the OU-PRIME (University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering) 5-cm polarimetric radar and PX-1000 3-cm polarimetric transportable radar.

This thesis presents the analysis and results of aerosol physical and optical properties over Darwin, Australia, a coastal tropical site which is affected by smoke aerosols from biomass burning inland and sea salt aerosols brought in by the sea breeze. Continuous Multi Filter Rotating Shadow Radiometer (MFRSR) spectral measurements for the period March 2002 -June 2003, have been obtained from the Tropical Western Pacific facilities site in Darwin, part of the D.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program. Atmospheric optical thickness was obtained using the Langley regression analysis and aerosol optical thickness was then obtained by removing the molecular and ozone contributions from the total atmosphere optical thickness. A regression analysis was performed on the aerosol optical thickness and the Angstrom exponent to identify the trends in seasonality. It shows a strong seasonal cycle with low optical thickness in autumn and high optical thickness in spring, coinciding with the peak of the biomass burning season. The aerosol optical thickness data were inverted using the Constrained Linear Inversion technique to obtain the size distributions. A novel method, combining several different criteria, was used for classification of the size distribution into classes which could be related to biomass burning aerosols and sea salt aerosols. These classes showed variations with season and meteorological parameters. The wind diurnal cycle showed a significant signature on the aerosol size and loading, and the aerosol loading was also strongly linked to fire activity. The aerosols observed over Darwin reflected different proportions of biomass and sea salt and variations in size over the whole year. The seasonal pattern in the aerosol properties over Darwin is found to be owing to the location of Darwin in the Tropical Warm Pool and the variety of aerosol types: smoke particles produced by the dry season burning conditions, and the sea salt aerosols associated with the introduction of cleaner maritime westerly and northwesterly winds. Aerosols are also affected by the high rainfall rate during the wet season, which washes out the aerosols from the atmosphere.

The remote estimation of rainfall rate R is essential for the aviation industry, agriculture, and food warning. Radar, the current means of R estimation, is not available in much of the world. In addition, this measurement involves a level of inaccuracy. Using lightning to detect rain is a relatively inexpensive alternative to radar systems and can be done from existing satellites. Previous research has revealed correlations between lightning and rain, suggesting either that it is possible to estimate R using lightning, or that it is possible to use it to correct for a portion of the radar inaccuracies. These correlations are not only between the amount of lightning and the amount of rain, but also between other parameters, including statistics describing raindrop size. Rain, lightning, and radar data were collected in Central Florida over a two month period in the summer of 2005. Rain data, including raindrop size statistics, were collected from a single point using a disdrometer. Lightning data were collected using the Los Alamos Sferic Array. Radar data were obtained from the WSR-88D radar network. Rain rate R and the raindrop size statistics were compared to lightning statistics to determine which rain/lightning parameter pairs were most correlated. The degree of correlation between rain and lightning parameters was evaluated using the correlation coeffiecint r. Diffrent lightning types (Cloud-to-Ground, Intra-Cloud, Narrow-Bipolar-Event, Total) were considered, and various circular areas were used for lightning collection to optimize the strength of the correlations. Four models using lightning and/or radar for the estimation of R were developed and then compared for accuracy. The rst model is based on the relationship between R and the radar reflectivity factor Z, as is the current practice. Two models using only lightning for the estimation of R were evaluated, and a final model used both radar and lightning data to estimate R. The performance of each model was evaluated using the RMS error. The correlations between rain and lightning parameters were generally weak (r < 0.5), although some pairs clearly produced stronger correlations than others. Results show that the strongest correlations are between lightning density (strokes/km2/hr) and Lambda, a parameter of the raindrop size distribution. This correlation was strongest for Intra-Cloud (IC) lightning measured on a 75 km diameter. Results from the R estimation models indicate that the use of lightning alone is a valid alternative to the use of radar for the conditions studied (R > 0.1 mm/hr, lightning present). The method combining radar and lightning parameters produces more accurate estimations of R than either type alone. Based on these results, lightning data can be used in addition to radar to provide greater accuracy to publicly available rain estimates, and it can be used to provide rain estimation capability to new locations, including greater food warning ability.

Conservation of atmospheric mass is one of the fundamental concepts used in the study of meteorology. Any time precipitation occurs, however, atmospheric mass is not conserved. As precipitation is removed from the atmosphere, the hydrostatic pressure in the precipitating region is reduced. In a tropical cyclone, the heaviest precipitation occurs in the eyewall, and this localized region of heavy precipitation leads to mass and moisture convergence towards the center of the tropical cyclone. The Coriolis force deflects the converging air, thereby contributing to vorticity generation and increasing the cyclonic wind speed. Moisture convergence can also enhance precipitation. In addition, potential vorticity (PV) increases as a result of the precipitation mass sink. To assess the significance of the precipitation mass sink, several hypothesis tests are performed. The MM5 model is used to create a physically realistic dataset of Hurricane Lili (2002) from which mass and PV budgets can be performed. The mass budget reveals that in a 100-km radius cylinder around the model storm center, the 5-h total and 1-h average pressure-equivalent mass loss due to precipitation during forecast hours 30 to 35 are -7.25 hPa and -1.45 hPa h⁻¹, respectively, while the 5-h total and 1-h average model surface pressure change during that time are -2.29 hPa and -0.46 hPa h⁻¹, respectively. Although the surface pressure change in the cylinder is mostly due to large cancellation between strong low-level convergence and stronger upper-level divergence, the continuous removal of mass via precipitation represents a non-negligible effect in the mass budget. The PV budget reveals that in the same cylinder, the average hourly instantaneous mass sink PV tendency during forecast hours 30 to 35 is 0.42 PVU day-1, while the average hourly instantaneous diabatic PV tendency is -2.19 PVU day⁻¹. The diabatic PV tendency exhibits large spatial and temporal cancellation, so the small but continuously positive PV contribution from the precipitation mass sink has a non-negligible effect on the PV budget. In addition, according to the PV tendency terms, an air parcel rising through the troposphere in the eyewall should experience nearly continuous PV generation via the precipitation mass sink but both PV generation and destruction via latent heat release, leading to large cancellation from the latter. Therefore, the parcel upon reaching the upper troposphere can likely attribute a non-negligible amount of its PV to the precipitation mass sink, but trajectory computations would be needed to quantify the relative contributions of the PV tendency terms on a given parcel. In addition to the mass and PV budgets, the workstation version of the Eta model is used to perform multiple sensitivity experiments with and without the precipitation mass sink. The most realistic of these sensitivity experiments reveals that the precipitation mass sink generally reduces the central pressure by ~5-7 hPa, increases the wind field by ~5-15 kt, and increases the precipitation rate by ~5-25 mm h⁻¹, but the rainfall rate difference in particular exhibits large spatial and temporal variation. PV and geopotential height cross sections show maximum precipitation mass sink-induced PV increase (>5 PVU) and geopotential height reduction (>4 dam) near the surface and near the melting layer. The results of this study suggest that the precipitation mass sink should not be neglected in tropical cyclones. Further research possibilities include detailed analysis of the impact of the precipitation mass sink on the tropical cyclone track, as well as the importance of the precipitation mass sink in heavily precipitating systems other than tropical cyclones.

Two greenroofs were constructed for research and demonstration purposes in eastern North Carolina; the first was constructed at the Neuseway Nature Center in Kinston in April 2002 and the second greenroof was constructed at Wayne Community College (WCC) in Goldsboro in May 2002. The Nature Center Greenroof was a 27 m2 greenroof with a 3% pitch; this extensive greenroof was constructed with 102 mm deep media. The extensive, 70 m2 WCC Greenroof in Goldsboro was constructed with two different media depths of 102 mm and 51 mm to research the effect of soil media depth on plant growth. Each greenroof was compared to a reference roof, referred to as the control roof, on site. Each greenroof site was monitored with a Sigma 900Max™ automatic sampler to measure the amount of runoff that drained from the greenroof and a control roof at each site as runoff flowed over a weir. A rain gage was also installed at each research site to accurately measure rainfall on location. Data was gathered at the WCC Greenroof for a consecutive nine month period from April 2003 to December 2003. Data was gathered at the Nature Center Greenroof during a portion of July 2003, August 2003, November 2003, and December 2003. Less data is available at the Nature Center Greenroof due to equipment malfunctions at the site. Because the two greenroofs are located approximately 48 km apart, the two greenroofs could not be directly compared; therefore, each greenroof was analyzed separately. Over the entire monitoring period, the WCC Greenroof retained 62% of the total 901 mm of precipitation observed at the site. The Nature Center Greenroof retained 63% of the 262 mm of precipitation measured. Variability of percent precipitation retained within each month of data was dependant upon the rainfall pattern, how moist the soil was from a prior rain event, and the seasonal affects on evapotranspiration rates. More precipitation will be retained during months with higher evapotranspiration rates. Percent flow reduction of the greenroof was determined by comparing the greenroof runoff peak flow and the peak rainfall rate. The Nature Center Greenroof observed an average 87% reduction in peak flow from the greenroof during the monitoring period. The WCC Greenroof observed an average peak flow reduction of 78% during the monitoring period. Water quality samples were taken of the greenroof runoff, the rainfall, and the control roof runoff at each site for select rain events. There were no statistical trends relating the concentrations or the mass loadings of nitrogen in the greenroof runoff to the rainfall or to the control roof at each site. However, in general, higher concentrations and higher mass loadings of nitrogen were observed in the greenroof runoff. The concentration and mass loading of phosphorus was statistically higher in the greenroof runoff than in the rainfall and the control roof runoff. This was an indication that the soil media was serving as an additional source of phosphorus to the greenroof runoff, whereas, the loading of nitrogen was more dependant upon the concentration of nitrogen in the rainfall and the volume of runoff observed from the greenroof. A brief laboratory study was performed to determine the leaching effects of nitrogen and phosphorus from various greenroof soil media with varying percentages of compost in the soil mix. The results of this study indicated that less nitrogen will leach from media with less compost present in the mix and less phosphorus will leach from greenroof soil media with less compost present in the soil mix. Results also indicated that concentrations of nitrogen leaching from the soil media will decrease to a minimum over time. Plant growth in 102 mm deep soil media was determined to be significantly higher than plant growth in 51 mm deep soil media. Extensive greenroof plant species recommended for growth in North Carolina are Delosperma nubigenum, Sedum album, Sedum album murale, Sedum floriferum, Sedum reflexum, Sedum sexangulare, and Sedum spurium fuldaglut.

The work described in this thesis is mainly confined to atmospheric propagation effects occuring in a town enviroment at a frequency of 36 GHz on a 4.1 Km link. Precipitation effects on a 110 GHz link on the same path have also been measured and are included. Measurements have been made on the amplitude scintillations and the results, under different atmospheric conditions, are compared with the theoretical predictions of Tatarski. Direct meteorological measurements of the important atmospheric parameters are described and are correlated with the effects occuring on the link, and the values of these parameters deduced from the behaviour of the radio signals. Measurements of signal attenuation due to'fog and snow have also been made and the study of some other anomalous scintillation phenomena are included. Long term statistics have been collected. Attempts have been made to verify the theoretical relation between the attenuation and rainfall rate at 36 and 110 GHz. A dual-frequency technique is used to investigate the validity of the Laws-Parsons raindrop size distribution. All the instrumentation and electronic processing used in this experiment are described.

Rational runoff coefficients have been derived for seven combinations of land use and treatment on single-crop agricultural watersheds near Coshocton, Ohio. Runoff coefficient is the ratio of peak runoff rate to rainfall rate. The rational runoff formula has been employed to predict peak runoff rates from twelve experimental mixed-cover watersheds in Ohio, Wisconsin, Nebraska, and Virginia. These peak rates were estimated using the derived runoff coefficients and five different methods of estimating time of concentration. The computed peak runoff rates have been evaluated against runoff records of the watersheds. The SCS-Curve-No. method of estimating time of concentration gave relatively better results.

This thesis deals with the theoretical analysis of the effect of the water droplets in the atmosphere on the propagation of a linearly polarized plane wave. These effects are (1) scattering - it is found that the scattered power is proportional to the sixth power of the radius of the droplet, also the scattered power varies as the fourth power of the frequency. (2) Absorption - it is found that the dielectric loss is much greater than the conductivity loss in the frequency range 10 M Hz. to 300 M Hz. The absorbed power is found to be much greater than the scattered power in the frequency range 10 M Hz. to 300 M Hz. Multiple scattering is neglected because scattered power is very small compared with the power absorbed. At the end of the thesis an expression for the attenuation constant is derived for homogeneous distribution of rain-drops of particular size falling at a particular rainfall rate.