xarray: N-D labeled Arrays and Datasets in Python

Hoyer, Stephan; Hamman, Joseph

doi:10.5334/jors.148

Cited by 870 publications

(643 citation statements)

References 7 publications

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“…We also recognize high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX), provided by NCAR's Computational and Information Systems Laboratory and sponsored by the NSF. Analysis was done in Python using numpy, scipy, wrf-python [Ladwig, 2017], and xarray [Hoyer and Hamman, 2017]. Plots were made using matplotlib and cartopy [Met Office, 2010], with color map support from cmocean [Thyng et al, 2016].…”

Section: Acknowledgmentsmentioning

confidence: 99%

Why does Amazon precipitation decrease when tropical forests respond to increasing CO2?

Langenbrunner¹,

Pritchard²,

Kooperman³

et al. 2018

Preprint

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Key Points:• Increasing CO 2 over the Amazon causes a drier, warmer, and expanded boundary layer and reduces basin-wide rainfall within the first day • On synoptic timescales, enhanced lower free troposphere moisture is advected westward by the low-level jet, increasing Andean rainfall • A wetter Andes, dryer Amazon pattern is consistent across regional and global climate models and parameterized versus resolved convectionCorresponding author: Baird Langenbrunner, blangenb@uci.edu EarthArXiv PREPRINTEarthArXiv PREPRINT of confidential manuscript submitted to Earth's Future AbstractEarth system models predict a zonal dipole of precipitation change over tropical South America, with decreases over the Amazon and increases over the Andes. Much of this has been attributed to the physiological response of the rainforest to elevated CO 2 , which describes a basin-wide reduction in stomatal conductance and transpiration. While robust in Earth system model experiments, details of the underlying atmospheric mechanismspecifically how it evolves in the context of land-atmosphere interaction and the diurnal cycle-are unresolved. We investigate this using idealized model simulations and find that within 24 hours of a CO 2 increase, changes occur over the Amazon that engender synoptic timescale feedbacks. Decreased evapotranspiration from the rainforest throttles near-surface moisture, inducing a drier, warmer, and deeper boundary layer. Above this, enhanced turbulent diffusivity increases vapor in the lower free troposphere. Together, these processes reduce convective activity and cause immediate decreases in Amazon rainfall. Over the synoptic timescale, these changes leave behind lower tropospheric moisture, which is advected westward by the background jet and increases Andean precipitation. This produces a dipole of precipitation change consistent across global and regional models as well as parameterized and resolved convection, though details are sensitive to model topography and boundary layer formulation. The mechanism reported here stresses the importance of fast timescale processes affecting stability over a period of hours that can influence longer-term vegetation-climate interactions. These results help clarify the Amazon's physiological response to rising CO 2 and provide insight into possible causes of historical model biases and end-of-century uncertainty in this region.

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Section: Acknowledgmentsmentioning

confidence: 99%

Why does Amazon precipitation decrease when tropical forests respond to increasing CO2?

Langenbrunner¹,

Pritchard²,

Kooperman³

et al. 2018

Preprint

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“…The result of calls to calculate() and equilibrium() are xarray Dataset objects [15]. The xarray Dataset object makes handling labeled multi-dimensional arrays substantially simpler.…”

Section: Representation Of Resultsmentioning

confidence: 99%

“…• gcc, MinGW or Microsoft Visual C++ compiler and toolchain • matplotlib [17] • numpy ≥ 1.9 [18] • scipy [18] • sympy [19] • xarray [15] • pyparsing [20] • tinydb • [21] containing this subsystem is publicly available, and it allows us to test several pycalphad features simultaneously since the system contains singlesublattice solution phases, multi-sublattice ordered phases, phases with magnetic ordering and stoichiometric compounds. The pycalphad package can perform computations for any number of components; we restrict our example to a binary system only for the simplicity of visualization.…”

Section: Dependenciesmentioning

confidence: 99%

pycalphad: CALPHAD-based Computational Thermodynamics in Python

Otis

Liu

2017

JORS

104

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The pycalphad software package is a free and open-source Python library for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria using the CALPHAD method. It provides routines for reading thermodynamic databases and solving the multi-component, multi-phase Gibbs energy minimization problem. The pycalphad software project advances the state of thermodynamic modeling by providing a flexible yet powerful interface for manipulating CALPHAD data and models. The key feature of the software is that the thermodynamic models of individual phases and their associated databases can be programmatically manipulated and overridden at run-time without modifying any internal solver or calculation code. Because the models are internally decoupled from the equilibrium solver and the models themselves are represented symbolically, pycalphad is an ideal tool for CALPHAD database development and model prototyping.

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“…For handling hyperspectral data, we recommend the xarray 1 package (Hoyer and Hamman, 2017). It provides multidimensional arrays and datasets with metadata.…”

Section: Handling Hyperspectral Datamentioning

confidence: 99%

Practical Approach for Hyperspectral Image Processing in Python

Annala

Eskelinen

Hämäläinen

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray, Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor and a spectral index library.

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xarray: N-D labeled Arrays and Datasets in Python

Cited by 870 publications

References 7 publications

Why does Amazon precipitation decrease when tropical forests respond to increasing CO2?

Why does Amazon precipitation decrease when tropical forests respond to increasing CO2?

pycalphad: CALPHAD-based Computational Thermodynamics in Python

Practical Approach for Hyperspectral Image Processing in Python

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