Abstract
Stochastic daily weather generators commonly used for biological modeling applications do not adequately reproduce empirical distributions of global solar irradiance. The daily clearness index, the ratio of daily global-to-extraterrestrial irradiance, captures the stochastic component of solar irradiance due to atmospheric conditions. Three alternative models of daily solar irradiance (truncated Gaussian distributions, a proposed modification based on logit-transformed relative clearness, and a family of empirically derived distributions) conditioned on the occurrence of rain are described and evaluated using data from 10 U.S. locations. These models are presented in terms of monthly cumulative distributions and density functions of clearness. Strong non-normality of distributions of clearness, and improved fits obtained with a logit transformation, are demonstrated. The proposed model, based on a logit transformation of relative clearness, was superior to the other two in terms of Akaike's information criterion, and generally superior to the standard model based on truncated Gaussian distributions in terms of goodness of fit between observed and generated irradiances. Based on this evidence, the proposed model is recommended for stochastic generation of daily irradiance conditioned on daily rainfall occurrence and temperature extremes.
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