Open AccessObservations of an Extreme Atmospheric River Storm With a Diverse Sensor Network
Author(s) -
Hatchett B. J.,
Cao Q.,
Dawson P. B.,
Ellis C. J.,
Hecht C. W.,
Kawzenuk B.,
Lancaster J. T.,
Osborne T. C.,
Wilson A. M.,
Anderson M. L.,
Dettinger M. D.,
Kalansky J. F.,
Kaplan M. L.,
Lettenmaier D. P.,
Oakley N. S.,
Ralph F. M.,
Reynolds D. W.,
White A. B.,
Sierks M.,
Sumargo E.
Publication year - 2020
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2020ea001129
Subject(s) - hydrometeorology , environmental science , context (archaeology) , extreme weather , climatology , storm , winter storm , snow , meteorology , precipitable water , precipitation , natural hazard , streamflow , climate change , drainage basin , geography , geology , oceanography , cartography , archaeology
Abstract Observational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.