Stochastic simulation of daily precipitation, temperature, and solar radiation

TLDR

Long weather data samples are required to assess long‑term hydrologic impacts, yet deterministic models rely on daily weather inputs, so stochastic generation provides an attractive alternative. This paper presents an approach that may be used to generate long samples of daily precipitation, maximum temperature, minimum temperature, and solar radiation. Precipitation is generated independently via a Markov‑chain exponential model, while temperature and solar radiation are produced by a multivariate model conditioned on wet/dry status derived from the precipitation model. The generated daily weather samples preserve the seasonal and statistical characteristics of each variable and the interrelations among the four variables that exist in the observed data.

Abstract

Long samples of weather data are frequently needed to evaluate the long‐term effects of proposed hydrologic changes. The evaluations are often undertaken using deterministic mathematical models that require daily weather data as input. Stochastic generation of the required weather data offers an attractive alternative to the use of observed weather records. This paper presents an approach that may be used to generate long samples of daily precipitation, maximum temperature, minimum temperature, and solar radiation. Precipitation is generated independently of the other variables by using a Markov chain‐exponential model. The other three variables are generated by using a multivariate model with the means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Daily weather samples that are generated with this approach preserve the seasonal and statistical characteristics of each variable and the interrelations among the four variables that exist in the observed data.