Journal article
Bayesian Hierarchical Models for the Frequency of Winter Heavy Precipitation Events Over the Central United States: The Role of Atmospheric Rivers
Water resources research, Vol.56(11), e2020WR028256
11/2020
DOI: 10.1029/2020WR028256
Abstract
Over the central United States, a large fraction of heavy precipitation and flood events have been tied to atmospheric rivers (ARs) and to two large‐scale atmospheric modes, the Pacific‐North American teleconnection pattern (PNA) and the Arctic Oscillation (AO). Here, we build on these insights to model the frequency of heavy precipitation events at 88 locations across the central United States. We use a Bayesian hierarchical modeling framework to develop and compare different models with varying degrees of complexity and nonstationary parameters that are conditioned on ARs and the prevalent climate. We show that Bayesian hierarchical models with a prior layer that allows spatially correlated parameters result in improved prediction skills over the traditional regression‐based modeling frameworks. While ARs, PNA, and AO have statistically significant relationships with the frequency of heavy precipitation events over large areas of the central United States, we find that ARs can significantly improve upon the other two covariates in the statistical modeling of extremes over the region.
Key Points
Over the central United States, the frequency of winter heavy precipitation has stronger relationship with AR frequency than with climate indices
Prior layers that allow spatially correlated parameters improve prediction skills over traditional regression‐based modeling frameworks
ARs improve upon the standard climate indices, such as PNA and AO, as covariate in the statistical modeling of extremes over the region
Details
- Title: Subtitle
- Bayesian Hierarchical Models for the Frequency of Winter Heavy Precipitation Events Over the Central United States: The Role of Atmospheric Rivers
- Creators
- Munir Ahmad Nayak - Indian Institute of Technology IndoreMary Kathryn Cowles - University of IowaGabriele Villarini - University of IowaBurhan Ul Wafa - Indian Institute of Technology Indore
- Resource Type
- Journal article
- Publication Details
- Water resources research, Vol.56(11), e2020WR028256
- DOI
- 10.1029/2020WR028256
- ISSN
- 0043-1397
- eISSN
- 1944-7973
- Number of pages
- 18
- Grant note
- USACE NASA (NNX14AK78H)
- Language
- English
- Date published
- 11/2020
- Academic Unit
- Civil and Environmental Engineering; Biostatistics; Statistics and Actuarial Science; IIHR--Hydroscience and Engineering
- Record Identifier
- 9984066349402771
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