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Simulating climate over western North America using stochastic weather generators

机译：使用随机天气产生器模拟北美西部的气候

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摘要

The climatologies of daily precipitation and of maximum and minimum temperatures over western North America are simulated using stochastic weather generators. Two types of generator, differentiated only by their method of modeling precipitation occurrence, are investigated. A second-order Markov model, in which the probability of the occurrence of precipitation is modeled as contingent upon its occurrence on the previous two days, is compared with a spell-length model, in which mass functions of wet-and dry-spell lengths are modeled. Both models are able to reproduce the observed annual and monthly climatology in the region to a high degree of accuracy. However, there is considerable over-dispersion in annual precipitation, resulting primarily from an underestimation in the interannual variability of precipitation intensity. The interannual variability of temperatures is similarly underestimated, and is most severe for minimum temperatures. There is a severe problem in estimating minimum temperature extremes, which can be attributed to the negatively skewed distribution of daily minimum temperatures. Non-normality in the distribution of daily temperatures is shown to be a problem in simulating extreme temperature maxima as well as of minima. It is suggested that the normal distribution used in the generation of daily temperatures in the widely used Richardson (1981) generator, and its derivations, be supplanted by a more appropriate distribution that permits skewness in either direction.
机译：使用随机天气发生器模拟了北美西部每天的降水以及最高和最低温度的气候。研究了两种类型的发电机，仅通过对降水发生的建模方法进行区分。将二阶马尔可夫模型（其中降雨发生的概率根据前两天的降雨而建模）与法术长度模型进行比较，该法术长度模型中的湿法和干法术长度的质量函数被建模。两种模型都可以高度精确地再现该地区观测到的年度和每月气候。但是，主要由于对降水强度的年际变化的低估而造成的年降水量有很大的过度分散。同样，温度的年际变化也被低估了，对于最低温度而言，变化最为严重。在估计最低温度极限时存在一个严重的问题，这可以归因于每日最低温度的负偏分布。在模拟极端温度最大值和最小值时，日温度分布的非正态性已成为一个问题。建议在广泛使用的Richardson（1981）发电机中，在日常温度生成中使用的正态分布及其推导，应被更合适的分布所取代，该分布允许任一方向上的偏斜。

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