atmospheric river
Recently Published Documents


TOTAL DOCUMENTS

183
(FIVE YEARS 117)

H-INDEX

28
(FIVE YEARS 7)

Author(s):  
Josué Gehring ◽  
Étienne Vignon ◽  
Anne‐Claire Billault‐Roux ◽  
Alfonso Ferrone ◽  
Alain Protat ◽  
...  
Keyword(s):  

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 29
Author(s):  
Zhuang Zhang ◽  
X. San Liang

The heavy precipitation in Northern California—brought about by a landfalling atmospheric river (AR) on 25–27 February 2019—is investigated for an understanding of the underlying dynamical processes. By the peaks in hourly accumulation, this rainstorm can be divided into two stages (Stage I and Stage II). Using a recently developed multiscale analysis methodology, i.e., multiscale window transform (MWT), and the MWT-based theory of canonical transfer, the original fields are reconstructed onto three scale windows, namely, the background flow, synoptic-scale and mesoscale windows, and the interactions among them are henceforth investigated. In both stages, the development of the precipitation is attributed to a vigorous buoyancy conversion and latent heating, and besides, the instability of the background flow. In Stage I, the instability is baroclinic, while in Stage II, it is barotropic. Interestingly, in Stage I, the mesoscale kinetic energy is transferred to the background flow where it is stored, and is released back in Stage II to the mesoscale window again, triggering intense precipitation.


2021 ◽  
pp. 1-66

Abstract Successive atmospheric river (AR) events—known as AR families—can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impacts that often stem from AR families, the large-scale environments and mechanisms associated with these compound events remain poorly understood. In this work, a new reanalysis-based 39-year catalog of 248 AR family events affecting California between 1981 and 2019 is introduced. Nearly all (94%) of the inter-annual variability in AR frequency is driven by AR family versus single events. Using K-means clustering on the 500-hPa geopotential height field, six distinct clusters of large-scale patterns associated with AR families are identified. Two clusters are of particular interest due to their strong relationship with phases of the El Niño/Southern Oscillation (ENSO). One of these clusters is characterized by a strong ridge in the Bering Sea and Rossby wave propagation, most frequently occurs during La Niña and neutral ENSO years and is associated with the highest cluster-average precipitation across California. The other cluster, characterized by a zonal elongation of lower geopotential heights across the Pacific basin and an extended North Pacific Jet, most frequently occurs during El Niño years and is associated with lower cluster-average precipitation across California but a longer duration. In contrast, single AR events do not show obvious clustering of spatial patterns. This difference suggests that the potential predictability of AR families may be enhanced relative to single AR events, especially on sub-seasonal to seasonal timescales.


Author(s):  
Amin Dezfuli ◽  
Michael G. Bosilovich ◽  
Donifan Barahona

2021 ◽  
Author(s):  
Corinne Bowers ◽  
Katherine A. Serafin ◽  
Jack W. Baker

Abstract. Atmospheric rivers (ARs) are a class of meteorologic phenomena that cause significant precipitation and flooding on the US West Coast. This work presents a new Performance-based Atmospheric River Risk Analysis (PARRA) framework that adapts existing concepts from probabilistic risk analysis and performance-based engineering for application in the context of AR-driven fluvial flooding. The PARRA framework is a chain of physically based models that link the atmospheric forcings, hydrologic impacts, and economic consequences of AR-driven fluvial flood risk together at consistent “pinch point” variables. Organizing around these pinch points makes the framework modular, in that models between pinch points can be updated without affecting the rest of the model chain, and it produces a probabilistic result that quantifies the uncertainty in the underlying system states. The PARRA framework can produce results beyond analyses of individual scenario events and can look towards prospective assessment of events or system changes that have not been seen in the historic record. The utility of the PARRA framework is demonstrated through a series of analyses in Sonoma County, California. Evaluation of a February 2019 case study AR event shows that the individual component models produce simulated distributions that capture the observed precipitation, streamflow, inundation, and damage. The component models are then run in sequence to generate a first-of-its-kind AR flood loss exceedance curve for Sonoma County. The prospective capabilities of the PARRA framework are presented through the evaluation of a hypothetical mitigation action. It was found elevating 150 homes, selected based on their proximity to the Russian River, was able to reduce the average annual loss by half. The loss results from the mitigated building portfolio are compared against the original case. While expected benefits were minimal for the smallest events, the larger, more damaging ARs were expected to see loss reductions of approximately $50 million per event. These results indicate the potential of the PARRA framework for examining other changes to flood risk at the community level, including future changes to the hazard, through climate change; exposure, through development; and/or vulnerability, through flood mitigation investments.


2021 ◽  
Author(s):  
Amin Dezfuli ◽  
Michael G. Bosilovich ◽  
Donifan Barahona

Weather ◽  
2021 ◽  
Author(s):  
David Smart ◽  
Edward Graham
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document