Understanding and modeling the rainfall drop size distribution is important in a number of applications, in particular predicting and mitigating attenuation of satellite signals in the millimeter band. Various standard statistical distributions have been proposed as suitable models, the first widely accepted being the exponential distribution. Subsequently, gamma and lognormal distributions have been shown to provide better rainfall rate computations. Some empirical studies have revealed bimodal distributions under some circumstances. A natural question to ask therefore is how often gamma and lognormal distributions fit the empirical data. In this paper we fit lognormal and gamma distributions to 1 min slices of rainfall drop size distributions taken from 7 year data from the Chilbolton Observatory in southern England. The chi-square goodness of fit of the models against the data is calculated, and it is found that failure to fit is greater than would normally be expected. This failure to fit is broken down and examined against seasonal variations, different rain rates, atmospheric temperature, and wind speed. Possible reasons for the lack of fit are explored, and alternative fits using models based on Gaussian Mixture Models are developed and found to be an improvement.
|Publication status||Published - 1 Sep 2015|
- rainfall drop size distribution
- Gaussian Mixture Model
- gamma distribution
- lognormal distribution