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Lauer, Melanie; Mech, Mario; Guan, Bin (2023): Global Atmospheric Rivers catalog for ERA5 reanalysis [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.957161

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Abstract:
The dataset contains hourly detected Atmospheric Rivers (ARs) for ERA5 reanalysis (1979 – 2021). For the detection of ARs, the global detection algorithm introduced by Guan and Waliser (2015) (first version v1), and refined by Guan et al. (2018) (second version v2) is applied. The algorithm considers different requirements: the intensity of integrated water vapor transport IVT, direction, and geometry (described in detail by Guan and Waliser (2015), Guan et al. (2018), and Lauer et al. (2023)). (1) IVT intensity: The IVT must exceed the 85th percentile of IVT for each grid cell and the lower limit of 100 kg m⁻¹ s⁻¹. (2) IVT direction: The IVT direction at individual grid cells have to be coherent, and the direction of object-mean IVT has to be within 45° of the AR shape orientation, with an appreciable poleward component. (3) Geometry: The length has to be larger than 2000 km, and the length-to-width ratio should be higher than two. In case an object exceeds the IVT percentile, but (2) and (3) are not fulfilled, the process is repeated for higher IVT thresholds (up to the 95th percentile with 2.5 steps). Thus, an object surrounded by an increased moisture content can be detected as an AR.
To apply the detection algorithm, the input variables - zonal and meridional components of the IVT (IVTx and IVTy) and the IVT percentiles - are first calculated using ERA5 reanalysis. When these variables have been inserted and the algorithm has been applied, an nc-file is output. This nc-file includes the AR shape, axis, geometric characteristics such as length and width, the coordinates of the AR's head, tail, and centroid, mean of zonal and meridional IVT, the direction of mean IVT, and the landfall location (if the AR made landfall).
Keyword(s):
atmospheric river; ERA5
Related to:
Guan, Bin; Waliser, Duane E (2015): Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies. Journal of Geophysical Research: Atmospheres, 120(24), 12514-12535, https://doi.org/10.1002/2015JD024257
Guan, Bin; Waliser, Duane E; Ralph, F Martin (2018): An Intercomparison between Reanalysis and Dropsonde Observations of the Total Water Vapor Transport in Individual Atmospheric Rivers. Journal of Hydrometeorology, 19(2), 321-337, https://doi.org/10.1175/JHM-D-17-0114.1
Lauer, Melanie; Rinke, Annette; Gorodetskaya, Irina V; Sprenger, Michael; Mech, Mario; Crewell, Susanne (in review): Influence of atmospheric rivers and associated weather systems on precipitation in the Arctic. https://doi.org/10.5194/egusphere-2023-261
Project(s):
Funding:
German Research Foundation (DFG), grant/award no. 268020496: TRR 172: ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1Date/time startDate/time start
2Date/time endDate/time end
3Binary ObjectBinary
4Binary Object (File Size)Binary (Size)Bytes
Size:
129 data points

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