资源环境科技发展态势分析平台

Stochastic precipitation generators (SPGs) produce synthetic precipitation data and are frequently used to generate inputs for physical models throughout many scientific disciplines. Especially for large data sets, statistical parameter estimation is difficult due to the high dimensionality of the likelihood function. We propose techniques to estimate SPG parameters for spatiotemporal precipitation occurrence based on an emerging set of methods called Approximate Bayesian computation (ABC), which bypass the evaluation of a likelihood function. Our statistical model employs a thresholded Gaussian process that reduces to a probit regression at single sites. We identify appropriate ABC penalization metrics for our model parameters to produce simulations whose statistical characteristics closely resemble those of the observations. Spell length metrics are appropriate for single sites, while a variogram-based metric is proposed for spatial simulations. We present numerical case studies at sites in Colorado and Iowa where the estimated statistical model adequately reproduces local and domain statistics.

Plain Language Summary Statistical simulations of precipitation and other weather quantities are commonly used in many sciences. Modern datasets are extremely high dimensional, which challenge traditional model estimation paradigms. We propose a novel technique specially adapted for estimation using approximate Bayesian computation (ABC), and show how important characteristics such as dry and wet spells can be used to quantify uncertain model parameters. The proposed method offers promising future directions for further research.