Stochastic simulation of daily precipitation, temperature, and solar radiation

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, m'mimum 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. ß ß ß ß ß 0 60 120 180 240 :300 :360

Surface air temperature variability over turkey and its connection to large-scale upper air circulation via multivariate techniques

“…Since data sets of temperature time series have dominant periodic cycles, these components can easily be estimated by any linear prediction model. On the other hand, these periodic components may hide the non-seasonal components, which are called non-periodic and trend components (linear or non-linear) in the stochastic literature (Richardson, 1981;Bras and Rodriguez-Iturbe, 1993;Box et al, 1994;Hipel and Mcleod, 1994;von Storch and Zwiers, 1999). However, the variability of climate may usually be in the non-seasonal components, which may show non-stationary characteristics.…”

“…If we plot the time series of the monthly temperature series of 62 stations shown in Table I, and, for instance, of 500 hPa geopotential heights and of 500-1000 hPa geopotential thicknesses, then the irregular noise components emerge. Fourier analysis (Richardson, 1981) is one possible way of removing such noise in the data sets, but Fourier analysis assumes a stationarity constraint in the process. SSA is another possible way based on eigenvalue techniques without satisfying stationarity constraints.…”

“…The monthly temperatures have dominant periodic components preventing a true statistical analysis, for instance, in trend and in stochastic components. Hence, Fourier analysis (Richardson, 1981) may not be applied directly, since the Fourier method requires the assumption of stationarity in the process. Thus, singular spectrum analysis (SSA) is performed to decompose the variables into deterministic and mixed statistical-random components (Vautard et al, 1992;Elsner and Tsonis, 1996).…”

Stochastic simulation of daily precipitation: A copula approach

“…For the former, the Markov chain models have been used extensively and, for the latter, various stochastic models such as exponential, gamma, skewed normal and mixed exponential distributions have been used. The readers are referred to Chin (1977), Stern (1980), Richardson (1981), Stern and Coe (1984), Richardson and Wright (1984), Nicks and Gander (1994), Duan et al . (1998), Katz and Parlange (1998), Wilks (1999), Hayhoe (2000) and Wan et al .…”

Is the January Thaw a Statistical Phantom?

“…While the autoregressive parameters 0 were allowed to vary by month and location, they are generally close to 0.6 as is typical for daily temperature data (e.g., Richardson 1981Richardson , 1982. The random numbers are produced by a Gaussian random number generator using the Box-Muller method (Bratley et al 1983), and have mean zero and standard deviation…”