Bayesian Analysis of Extreme Precipitation Events and Forecasting Return Levels

2022/08/25 by Douglas E. Johnston, Johnston, Douglas E.
Earth and Planetary Sciences · Environmental Science · #Applications (stat.AP) #Climate variability and models #FOS: Computer and information sciences #Hydrology and Drought Analysis #Meteorological Phenomena and Simulations

paper · pdf · doi:10.48550/arxiv.2208.12316

openalex publication_date 2022/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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