Xarray: V0.8.0

Hoyer, Stephan; Fitzgerald, Clark; Hamman, Joseph; akleeman,; Kluyver, Thomas; Roos, Maximilian; Helmus, Jonathan; Markel, Vadim A.; Cable, Pete; Maussion, Fabien; Miles, Alistair; Kanmae, T.; Wolfram, Phillip J.; Sinclair, Scott E.; Bovy, Benoît; ebrevdo,; Guedes, Rafael; Abernathey, Ryan; Filipe,; Hill, Susan; Richards, Ned; Lee, Antony; Koldunov, Nikolay; Graham, M. J.; maciekswat,; Gerard, Jeffrey; Babuschkin, I.; Deil, C.; Welch, Erik; Hilboll, Andreas

doi:10.5281/zenodo.59499

Cited by 6 publications

(3 citation statements)

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“…We imported the previously described observables and stacked them into a multi-dimensional grid of 0.5 °× 0.5 °resolution using Xarray (Hoyer et al, 2016). In grid cells where no data for the geological or geophysical observable under consideration is available or where the resolution of the Frontiers in Earth Science frontiersin.org original data is not sufficient, we interpolate via inverse distance weighting (IDW) if the observable is of continuous type.…”

Section: Gridding Of the Datamentioning

confidence: 99%

A geothermal heat flow model of Africa based on random forest regression

2022

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Geothermal heat flow (GHF) data measured directly from boreholes are sparse. Purely physics-based models for geothermal heat flow prediction require various simplifications and are feasible only for few geophysical observables. Thus, data-driven multi-observable approaches need to be explored for continental-scale models. In this study, we generate a geothermal heat flow model over Africa using random forest regression, originally based on sixteen different geophysical and geological quantities. Due to an intrinsic importance ranking of the observables, the number of observables used for the final GHF model has been reduced to eleven (among them are Moho depth, Curie temperature depth, gravity anomalies, topography, and seismic wave velocities). The training of the random forest is based on direct heat flow measurements collected in the compilation of (Lucazeau et al., Geochem. Geophys. Geosyst. 2019, 20, 4001–4024). The final model reveals structures that are consistent with existing regional geothermal heat flow information. It is interpreted with respect to the tectonic setup of Africa, and the influence of the selection of training data and observables is discussed.

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Section: Gridding Of the Datamentioning

confidence: 99%

A geothermal heat flow model of Africa based on random forest regression

2022

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“…The High Resolution Downscaled Climate Data for Southeast Alaska is freely available online (Lader, 2020;Lader et al, 2020) analysis is publicly available online (Nash, 2023). Python 3, xarray, metpy, pandas, and matplotlib were used for the analysis and development of the figures (Caswell et al, 2022;Hoyer et al, 2016;Hoyer & Hamman, 2017;Hunter, 2007;May et al, 2017May et al, , 2022May et al, , 2023Pandas Development Team, 2022;Van Rossum & Drake, 2009).…”

Section: Appendix A: Z-score Testsmentioning

confidence: 99%

Atmospheric Rivers in Southeast Alaska: Meteorological Conditions Associated With Extreme Precipitation

Nash,

Rutz,

Jacobs

2024

JGR Atmospheres

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Extreme precipitation events associated with atmospheric rivers (ARs) trigger floods, landslides, and avalanches that threaten lives and livelihoods in Southeast Alaska. Six rural and indigenous communities (Hoonah, Klukwan, Skagway, Yakutat, Craig, and Kasaan) identified specific needs regarding these hazards and joined the Southeast Alaska Coastlines and People (CoPe) Kutí Hub to address the shared challenge of understanding and predicting these events. This study presents a climatology (1980–2019) of synoptic, mesoscale, and local meteorological characteristics of ARs and heavy precipitation across this region. High‐amplitude upper‐level patterns across the northeastern Pacific Ocean favor ARs reaching Southeast Alaska, where moisture is orographically lifted, resulting in heavy precipitation. In the six communities, ARs occur 8–15 days per month, yet only 6 AR days per year account for up to 68%–91% of precipitation extremes. Furthermore, 80%–96% of days with extreme precipitation have >75th percentile integrated water vapor transport (IVT), demonstrating the strong relationship between IVT and extreme precipitation. This study also highlights the relationship between IVT direction and complex coastal topography in determining precipitation extremes. For example, in Klukwan and Skagway, 80%–90% of extreme AR days have south‐southwesterly or south‐southeasterly IVT. Coastal communities like Yakutat experience higher IVT and precipitation overall, and although southeasterly IVT is more common, extreme precipitation events are most common with southwesterly IVT. Collaboration with the National Weather Service in Juneau, Alaska will lead to improved situational awareness, forecasts, and Impact Decision Support Services to communities, saving lives and property in a region vulnerable to the impacts of climate change.

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“…All processed data files for this study are provided via Mendeley Data Kuchar (2020), and all codes to reproduce our figures are provided via GitHub (Kuchar, 2021) (Jucker, 2018), cartopy (Met Office, 2010-2015, matplotlib (Hunter, 2007), numpy (Oliphant, 2006), pandas (McKinney, 2010, seaborn (Waskom et al, 2016), scipy (Virtanen et al, 2020), xarray (Hoyer et al, 2016), and Scientific color maps (Crameri, 2020).…”

Section: Data Availability Statementmentioning

confidence: 99%

Diverse Dynamical Response to Orographic Gravity Wave Drag Hotspots—A Zonal Mean Perspective

Šácha

Kuchař

Eichinger

et al. 2021

Geophysical Research Letters

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In the extratropical atmosphere, Rossby waves (RWs) and internal gravity waves (GWs) propagating from the troposphere mediate a coupling with the middle atmosphere by influencing the dynamics therein. In state‐of‐the‐art chemistry‐climate models (CCMs), RW effects are well resolved while the majority of GW effects have to be parameterized. Here, we analyze orographic GW (OGW) interaction with resolved dynamics in a comprehensive CCM on daily time scales. For this, we apply a recently developed method of strong OGW drag event composites for three pronounced northern hemisphere OGW hotspots. We show that strong OGW drag events are associated with anomalous resolved wave propagation in the stratosphere. Causal links are inferred from previously published work and are supported by the anomalies in zonal circulation and wave activity tendencies. The nature of these anomalies varies depending on the hotspot region, which underlines the parameterized OGW‐resolved dynamics interaction being a two‐way process.

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Xarray: V0.8.0

Cited by 6 publications

References 0 publications

A geothermal heat flow model of Africa based on random forest regression

A geothermal heat flow model of Africa based on random forest regression

Atmospheric Rivers in Southeast Alaska: Meteorological Conditions Associated With Extreme Precipitation

Diverse Dynamical Response to Orographic Gravity Wave Drag Hotspots—A Zonal Mean Perspective

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