It is the purpose of this investigation to determine the amount of rain fall to be expected in various areas of the State of Florida. To date there are no published papers which are concerned with this aspect of rain fall in the state. Although this study is mainly concerned with the amount of rain fall which may be expected at ten per cent intervals throughout the state, it is probable that it has many ramifications which will be of value in other phases of water research. In speaking of the ten per cent intervals of time we are actually dealing with percentages of the total time. Hence, 25 years out of 50 years should not be construed to mean the next 25 years out of the next 50 years. / Typescript. / "August, 1949." / "Submitted to the Graduate Council of Florida State University in partial fulfillment of the requirements for the degree of Master of Science under Plan II." / Advisor: LeRoy Babcock, Professor Directing Paper. / Includes bibliographical references (leaf 58).

Simulations based on a month of radar data from Florida, and a summer of radar data from Nelspruit, South Africa, were used to quantify the errors in the measurement of mean areal rainfall which arise simply as a result of the extreme variability of convective rainfall, even with perfect remote sensing instruments. The raingauge network measurement errors were established for random and regular network configurations using daily and monthly radar-rainfall accumulations over large areas. A relationship to predict the measurement error for mean areal rainfall using sparse networks as a function of raining area, number of gauges, and the variability of the rainfield was developed and tested. The manner in which the rainfield probability distribution is transformed under increasing spatial and temporal averaging was investigated from two perspectives. Firstly, an empirical relationship was developed to transform the probability distribution based on some measurement scale, into a distribution based on a standard measurement length. Secondly, a conceptual model based on multiplicative cascades was used to derive a scale independent probability distribution.

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