Title page, abstract and table of contents only. The complete thesis in print form is available from the University of Adelaide Library. / This thesis describes the development of a new technique which significantly extends the applicability of stochastic point rainfall models that require historical data for calibration. The technique is demonstrated using a high-resolution point rainfall model based on wet-dry alternating storm events. A model has been produced that is well defined and can be vigorously compared against numerous observed statistics in a quantative manner. The rainfall model presented in this thesis can be used to provide accurate synthetic rainfall data at sites with minimal historical rainfall data providing a powerful tool for application in hydrological risk analysis across Australia. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1282259 / Thesis (Ph.D.) -- University of Adelaide, Dept. of Civil and Environmental Engineering, 2007

Title page, abstract and table of contents only. The complete thesis in print form is available from the University of Adelaide Library. / This thesis describes the development of a new technique which significantly extends the applicability of stochastic point rainfall models that require historical data for calibration. The technique is demonstrated using a high-resolution point rainfall model based on wet-dry alternating storm events. A model has been produced that is well defined and can be vigorously compared against numerous observed statistics in a quantative manner. The rainfall model presented in this thesis can be used to provide accurate synthetic rainfall data at sites with minimal historical rainfall data providing a powerful tool for application in hydrological risk analysis across Australia. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1282259 / Thesis (Ph.D.) -- University of Adelaide, Dept. of Civil and Environmental Engineering, 2007

published_or_final_version / Electrical Engineering / Doctoral / Doctor of Philosophy

This thesis describes the use of a distrometer and two IBM-PC's to collect data in a large propagation experiment. The uses and methods of collecting drop size distribution are discussed as are the uses of IBM-PC's for both data collection and control. Methods of requiring the PC's to operate in real time are also included. / M.S.

No description available.

Lau, Stephen Po Wing. / "August 2011." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 62-66). / Abstracts in English and Chinese. / List of figures --- p.vi / List of tables --- p.ix / Chapter Chapter 1: --- Background --- p.1 / Chapter Chapter 2: --- Changes in Rainfall Characteristics in Hong Kong --- p.3 / Chapter 2.1 --- Introduction --- p.3 / Chapter 2.2 --- Data --- p.5 / Chapter 2.3 --- Method --- p.6 / Chapter 2.3.1 --- Linear Regression Analysis --- p.8 / Chapter 2.3.2 --- Empirical Mode Decomposition --- p.9 / Chapter 2.4 --- Result --- p.12 / Chapter 2.4.1 --- Rainfall Rate Categories --- p.12 / Chapter 2.4.2 --- Rainfall Events Statistics --- p.20 / Chapter 2.4.3 --- Diurnal Variation --- p.26 / Chapter 2.4.4 --- Rainfall Rate Distribution --- p.30 / Chapter Chapter 3: --- Reflectivity-rainfall Rate Relationships in Hong Kong --- p.33 / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.2 --- Data --- p.35 / Chapter 3.3 --- Method --- p.37 / Chapter 3.3.1 --- Linear Regression Analysis --- p.37 / Chapter 3.4 --- Result --- p.38 / Chapter 3.4.1 --- Comparison of Rainfall Rate from Various Sources --- p.39 / Chapter 3.4.2 --- Raindrop Size Distributions under Various Classification Schemes --- p.42 / Chapter 3.4.3 --- Z-R Relationships Derived from Various Sources --- p.48 / Chapter Chapter 4: --- Conclusion --- p.58 / Bibliography --- p.62

An extreme precipitation categorization scheme, developed to temporally and spatially visualize and track the multi-scale variability of extreme precipitation climatology, is introduced over the continental United States and used as the basis for an observational dataset intercomparison. The categorization scheme groups three-day precipitation totals exceeding 100 mm into five precipitation categories, or "P-Cats". To assess observational uncertainty across a range of precipitation measurement approaches, we compare in situ station data from the Global Historical Climatology Network-Daily (GHCN-D), satellite derived data from the Tropical Rainfall Measuring Mission (TRMM), gridded station data from the Parameter-elevation Regression on Independent Slopes Model (PRISM), global reanalysis from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA 2), and regional reanalysis from the North American Regional Reanalysis (NARR). While all datasets capture the principal spatial patterns of extreme precipitation climatology, results show considerable variability across the five-platform suite in P-Cat frequency, spatial extent, and magnitude. Higher resolution datasets, PRISM and TRMM, most closely resemble GHCN-D and capture a greater frequency of high-end totals relative to lower resolution products, NARR and MERRA-2. When all datasets are regridded to a common coarser grid, differences persist with datasets originally constructed at a high resolution maintaining the highest frequency and magnitude of P-Cats. Potential future applications of this scheme include tracking change in P-Cats over space and time, climate model evaluation, and assessment of model projected change.

No description available.

No description available.

Infrared (IR) imagery collected by geostationary satellites provides useful information about the dirunal evolution of cloud systems. These IR images can be analyzed to indicate the location of clouds as well as the pattern of cloud top temperatures (Tbs). During the past several decades, a number of different approaches for estimation of rainfall rate (RR) from Tb have been explored and concluded that the Tb-RR relationship is (1) highly nonlinear, and (2) seasonally and regionally dependent. Therefore, to properly model the relationship, the model must be able to: (1) detect and identify a non-linear mapping of the Tb-RR relationship; (2) Incorporate information about various cloud properties extracted from IR image; (3) Use feedback obtained from RR observations to adaptively adjust to seasonal and regional variations; and (4) Effectively and efficiently process large amounts of satellite image data in real -time. In this study, a kind of artificial neural network (ANN), called Modified Counter Propagation Network (MCPN), that incorporates these features, has been developed. The model was calibrated using the data around the Japanese Islands provided by the Global Precipitation Climatology Project (GPCP) First Algorithm Intercomparison Project (AIP-I). Validation results over the Japanese Islands and Florida peninsula show that by providing limited ground-truth observation, the MCPN model is effective in monthly and hourly rainfall estimation. Comparison of results from MCPN model and GOES Precipitation Index (GPI) approach is also provided in the study.