ARTMIP-early start comparison of atmospheric river detection tools: how many atmospheric rivers hit northern California’s Russian River watershed?
Many atmospheric river detection tools (ARDTs) have now been developed. However, their relative performance is not well documented. This paper compares a diverse set of ARDTs by applying them to a single location where a unique 12-year-long time-series from an atmospheric river observatory at Bodega Bay, California is available. The study quantifies the sensitivity of the diagnosed number, duration, and intensity of ARs at this location to the choice of ARDT, and to the choice of reanalysis data set. The ARDTs compared here represent a range of methods that vary in their use of different variables, fixed vs. percentile-based thresholds, geometric shape requirements, Eulerian vs. Lagrangian approaches, and reanalyses. The ARDTs were evaluated first using the datasets documented in their initial publication, which found an average annual count of 19 ± 7. Applying the ARDTs to the same reanalysis dataset yields an average annual count of 19 ± 4. Applying a single ARDT to three reanalyses of varying grid sizes (0.5°, 1.0°–2.5°) showed little sensitivity to the choice of reanalysis. While the annual average AR event count varied by about a factor of two (10–25 per year) depending on the ARDT, average AR duration and maximum intensity varied by less than ± 10%, i.e., 24 ± 2 h duration; 458 ± 44 kg m− 1 s− 1 maximum IVT. ARDTs that use a much higher threshold for integrated vapor transport were compared separately, and yielded just 1–2 ARs annually on average. Generally, ARDTs that include either more stringent geometric criteria or higher thresholds identified the fewest AR events.
This research was supported by Grant Number W912HZ-15-2-0019 from the US Army Corps of Engineers. It was partially supported, with authors Alexander Gershunov and Tamara Shulgina, by Department of the Interior via the Bureau of Reclamation (USBR-R15AC00003, Seasonal and extended-range predictability of atmospheric rivers and their associated precipitation) and by the California Department of Water Resources (4600010378 UCOP2-11, Development of seasonal outlooks for Atmospheric Rivers). The Atmospheric River Observatory data were provided by NOAA’s Physical Sciences Division, Earth System Research Laboratory, from their website at http://www.esrl.noaa.gov/psd/. The authors would like to thank UC San Diego Qualcomm/Calit2 and Pacific Research Platform (ACI-1541349) for supporting the PRP/CONNECT pilot project with network data transfer and storage support for the Sellars et al. (2017b) dataset. The authors would also like to thank two anonymous reviewers for their comments that helped us to strengthen the paper.
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