Statistical Modeling of Daily Rainfall Using Zero-tweaked Data
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Abstract
The significant amount of zero rainfall led to a highly skewed distribution of rainfall, creating challenges in rainfall modeling. This study aims to introduce a zero-tweaking method for handling a large proportion of zero rainfall data and apply it to daily rainfall data collected at four stations in southern Thailand from 2010 to 2022. The fourth root transformation was used to handle the right skew of rainfall. Zero-tweaking techniques were employed, with zeros substituted by normally distributed random numbers that permitted negative values. The patterns and trends in rainfall at each of the four stations were investigated using natural cubic splines. The trend projection analysis for four rainfall stations up to 2030 revealed an increase in rainfall at two stations in the Gulf of Thailand; however, this increase was not statistically significant. However, this study introduced the zero-tweaking method to handle the zero data, which enabled the use of conventional statistical methods and enhanced the model's validity.
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