论文标题
贝叶斯对极端降水事件和预测回报水平的分析
Bayesian Analysis of Extreme Precipitation Events and Forecasting Return Levels
论文作者
论文摘要
在这项研究中，我们检查了一种贝叶斯方法，以分析极端的每日降雨量和预测返回级别。在许多应用中，包括土木工程和公共基础设施的设计在内，估计未来极端事件的发生概率和分位数的概率很重要。与传统分析（使用点估计值来实现这一目标）相反，贝叶斯方法利用了从观测值得出的完整后部密度。贝叶斯的方法提供了定义明确的可信（置信度）间隔，改善预测以及捍卫严格的概率评估的能力。我们使用来自美国纽约州长岛的极端降水数据来说明贝叶斯的方法，并表明当前的回流水平或降水风险可能会被低估。
In this study, we examine a Bayesian approach to analyze extreme daily rainfall amounts and forecast return-levels. Estimating the probability of occurrence and quantiles of future extreme events is important in many applications, including civil engineering and the design of public infrastructure. In contrast to traditional analysis, which use point estimates to accomplish this goal, the Bayesian method utilizes the complete posterior density derived from the observations. The Bayesian approach offers the benefit of well defined credible (confidence) intervals, improved forecasting, and the ability to defend rigorous probabilistic assessments. We illustrate the Bayesian approach using extreme precipitation data from Long Island, NY, USA and show that current return levels, or precipitation risk, may be understated.
