Western US Subseasonal Atmospheric River Prediction in the Global‐Nested GFDL SHiELD Model

Zavadoff, Breanna L.; Cheng, Kai‐Yuan; Lopez, Hosmay; Kim, Dongmin; West, Robert; Gao, Kun; Chen, Jan‐Huey; Harris, Lucas M.

doi:10.1029/2025JD043896

i

Western US Subseasonal Atmospheric River Prediction in the Global‐Nested GFDL SHiELD Model

2025
By Zavadoff, Breanna L. ; Cheng, Kai‐Yuan ; Lopez, Hosmay ; ...

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Journal Title:

Journal of Geophysical Research: Atmospheres
Personal Author:

Zavadoff, Breanna L. ; Cheng, Kai‐Yuan ; Lopez, Hosmay ; Kim, Dongmin ; West, Robert ; Gao, Kun ; Chen, Jan‐Huey ; Harris, Lucas M.
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies) ; CIMES (Cooperative Institute for Modeling the Earth System) ; GFDL (Geophysical Fluid Dynamics Laboratory)
Description:

Throughout the western US communities rely on atmospheric rivers (ARs) to fill reservoirs, accumulate snowpack, and mitigate drought while remaining wary of their potential to produce widespread flooding, debris flows, and property/infrastructure damage. This makes long‐range AR forecasts, specifically at subseasonal timescales, a valuable tool for both water resource and emergency management personnel. Ensemble forecasts of the system for high‐resolution prediction on Earth‐to‐Local Domains (SHiELD) model, configured with a CONUS nested grid, are evaluated for prediction skill relating to both weekly landfalling AR activity and AR‐related precipitation. Results indicate that SHiELD exhibits skillful prediction of landfalling AR activity and extreme daily AR‐related precipitation through week 2 with some skill even extending into week 3. Inspired by divergent outcomes regarding SHiELD's ability to predict two high‐impact ARs, successful week 2 forecasts of landfalling AR activity in SHiELD are found to be dependent, at least in part, on how favorably the large‐scale environment is preconditioned to the development and/or maintenance of AR activity during the preceding week. Our findings suggest SHiELD can skillfully predict AR activity and its related extreme precipitation at leads of 2–3 weeks, providing stakeholders ample time to monitor areas at risk for widespread flooding while also preparing them for potential impacts. Lastly, SHiELD is shown to under‐predict the frequency of AR activity over the southern half of the western US due to systematic mean state integrated vapor transport biases, a finding that highlights an avenue for further advancement of landfalling AR predictability in SHiELD.
Source:

Journal of Geophysical Research: Atmospheres, 130(17)
DOI:

https://doi.org/10.1029/2025JD043896
ISSN:

2169-897X ; 2169-8996
Format:

pdf
Publisher:

American Geophysical Union (AGU)
Document Type:

Journal Article
Funding:

Grant no. NA20OAR4320472
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

urn:sha-512:b6394ba8fdf481f4792730e343ec8bf488bab50a93b4771fa70878d0a4fecdd4423e71b4ac7eee4db2224e5ee2c19a8ee1c78bcac89c719455a387ab7903ea74
Download URL:

https://repository.library.noaa.gov/view/noaa/72207/noaa_72207_DS1.pdf
File Type:

[PDF - 8.92 MB ]

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