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Cited by 96Meteorological Phenomena and SimulationsClimate variability and modelsHydrological Forecasting Using AI

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Improving Atmospheric River Forecasts With Machine Learning

Chapman, W. E., Subramanian, A. C., Delle Monache, L., Xie, S. P., Ralph, F. M.·2019·Geophysical Research Letters
DOI 10.1029/2019gl083662
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Abstract

This study tests the utility of convolutional neural networks as a postprocessing framework for improving the National Center for Environmental Prediction's Global Forecast System's integrated vapor transport forecast field in the Eastern Pacific and western United States. Integrated vapor transport is the characteristic field of atmospheric rivers, which provide over 65% of yearly precipitation at some western U.S. locations. The method reduces full‐field root‐mean‐square error (RMSE) at forecast leads from 3 hr to seven days (9–17% reduction), while increasing correlation between observations and predictions (0.5–12% increase). This represents an approximately one‐ to two‐day lead time improvement in RMSE. Decomposing RMSE shows that random error and conditional biases are predominantly reduced. Systematic error is reduced up to five‐day forecast lead, but accounts for a smaller portion of RMSE. This work demonstrates convolutional neural networks potential to improve forecast skill out to seven days for precipitation events affecting the western United States.

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Crossref · 10.1029/2019gl083662

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  • Coastal North America and the Eastern Pacific (180°W to 110°W, 10°N to 60°N) during the cold season (Jan-April and Oct-Dec 2018) is a region and period with frequent atmospheric rivers.

    “Similarly to previous work [10], Figure 44 reports RMSE skill and skill score averaged over coastal North America and the Eastern Pacific (from 180°W to 110°W longitude, and 10°N to 60°N latitude) during the cold season (Jan-April and Oct-Dec 2018), which corresponds to a region and a period with frequent atmospheric rivers.”

    From Learning skillful medium-range global weather forecasting (2023) · cites this paper as [10]

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Metadata

Authors
Chapman, W. E., Subramanian, A. C., Delle Monache, L., Xie, S. P., & Ralph, F. M.
Affiliations
  • Scripps Institution of Oceanography (US)
  • University of Colorado Boulder (US)
  • Scripps Institution of Oceanography University of California, San Diego La Jolla CA USA
Corresponding author
Chapman, W. E.
ORCID
Venue
Geophysical Research Letters
Publisher
American Geophysical Union (AGU)
Published
2019-09
Volume
46
Issue
17-18
Pages
10627-10635
ISSN
  • 0094-8276 (ISSN-L)
  • 1944-8007
DOI
10.1029/2019gl083662
OpenAlex
W2970999468
Cited by
96
References
3
Citations by year
2026: 10 · 2025: 9 · 2024: 14 · 2023: 17 · 2022: 17 · 2021: 17 · 2020: 9 · 2019: 3
FWCI
4.45
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 5 d)
Locations
Geophysical Research Letters (publishedVersion)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
Keywords
atmospheric rivers, integrated vapor transport, forecast post-processing, convolutional neural networks, Global Forecast System, precipitation forecasting, forecast error decomposition, systematic errors, conditional bias, eastern Pacific, American West

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