论文标题
使用隐藏的Markov模型随机天气生成器分析降水模式的趋势
Analyzing trends in precipitation patterns using Hidden Markov model stochastic weather generators
论文作者
论文摘要
我们开发了一个基于柔性样条的贝叶斯隐藏模型随机天气生成器，以统计地在单个位置按季节对每日降水进行统计模型。该模型自然说明数据缺失（被认为是随机丢失），避免了系统的缺失模式的潜在灵敏度，或者使用任意临界值来处理每日沉淀数据的指标时处理丢失。然后，拟合的模型可以用于推断降水行为的任意度量趋势，要么通过丢失的数据的多次插补，然后进行频繁分析，要么通过贝叶斯后验预测分布进行模拟。我们表明该模型非常适合数据，包括各种多日特征，表明对数据的自相关结构表示保真。使用来自美国西部的三个站点，我们开发了案例研究，在这些案例研究中，我们在降水的各个方面（例如干咒长度和降水强度）的趋势，仅根据将SEN的斜率用作非参数趋势的趋势量，在某些季节中仅发现有限的趋势证据。在将来的工作中，我们计划将方法应用于选定区域中GHCN站的完整集合，以系统地评估趋势的证据。
We develop a flexible spline-based Bayesian hidden Markov model stochastic weather generator to statistically model daily precipitation over time by season at individual locations. The model naturally accounts for missing data (considered missing at random), avoiding potential sensitivity from systematic missingness patterns or from using arbitrary cutoffs to deal with missingness when computing metrics on daily precipitation data. The fitted model can then be used for inference about trends in arbitrary measures of precipitation behavior, either by multiple imputation of the missing data followed by frequentist analysis or by simulation from the Bayesian posterior predictive distribution. We show that the model fits the data well, including a variety of multi-day characteristics, indicating fidelity to the autocorrelation structure of the data. Using three stations from the western United States, we develop case studies in which we assess trends in various aspects of precipitation (such as dry spell length and precipitation intensity), finding only limited evidence of trends in certain seasons based on the use of Sen's slope as a nonparametric measure of trend. In future work, we plan to apply the method to the complete set of GHCN stations in selected regions to systematically assess the evidence for trends.
