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A regionally refined quarter-degree global atmospheric rivers database based on ERA5.

Bin GuanDuane E Waliser
Published in: Scientific data (2024)
Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle. The continually developing Tracking Atmospheric Rivers Globally as Elongated Targets (tARget) algorithm identifies AR objects at individual time steps based on thresholding integrated water vapor transport (IVT) and other requirements, and tracks each AR object in time and space. Building on previous versions of tARget, this paper discusses further refinements to the algorithm to better handle ARs in tropical and polar areas, as well as "zonal" ARs which the previous versions of the algorithm were not designed to capture. This further regionally refined algorithm is applied to the ERA5 reanalysis over 1940-2023 at 6 h intervals and a 0.25° × 0.25° horizontal resolution. The AR detection results are evaluated in terms of key AR characteristics. We anticipate this regionally refined global AR database will aid further understanding of ARs such as AR process studies, evaluation of AR simulations and predictions, and assessment of climate change impacts on ARs.
Keyphrases
  • climate change
  • machine learning
  • deep learning
  • particulate matter
  • neural network
  • gene expression
  • working memory
  • genome wide
  • molecular dynamics
  • air pollution
  • adverse drug
  • carbon dioxide
  • real time pcr