An Extended Version of the Richardson Model for Simulating Daily Weather Variables

Abstract

The Richardson model is a popular technique for stochastic simulation of daily weather variables, including precipitation amount, maximum and minimum temperature, and solar radiation. This model is extended to include two additional variables, daily mean wind speed and dewpoint, because these variables (or related quantities such as relative humidity) are required as inputs for certain ecological/vegetation response and agricultural management models. To allow for the positively skewed distribution of wind speed, a power transformation is applied. Solar radiation also is transformed to make the shape of its modeled distribution more realistic. A model identification criterion is used as an aid in determining whether the distributions of these two variables depend on precipitation occurrence. The approach can be viewed as an integration of what is known about the statistical properties of individual weather variables into a single multivariate model. As an application, this extended model is fitted to weather data in the Pacific Northwest. To aid in understanding how such a stochastic weather generator works, considerable attention is devoted to its statistical properties. In particular, marginal and conditional distributions of wind speed and solar radiation are examined, with the model being capable of representing relationships between variables in which the variance is not constant, as well as certain forms of nonlinearity.