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biogeomon_2022_pangeo

BIOGEOMON 2022 open pre-conference workshop on "Spatio-temporal trend analysis of spatial climate data (temperature and rainfall) using Python"

https://github.com/landscapegeoinformatics/biogeomon_2022_pangeo

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BIOGEOMON 2022 open pre-conference workshop on "Spatio-temporal trend analysis of spatial climate data (temperature and rainfall) using Python"

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Readme License Citation

README.md

BIOGEOMON 2022 Python Pangeo Workshop by Landscape Geoinformatics

Spatio-temporal trend analysis of spatial climate data (temperature and rainfall) using Python

BIOGEOMON Pre-conference Workshop

There are wide range of global or regional level climate data available in a gridded format. Under the changing climate, we need to quantify the variability of temperature and rainfall patterns to understand the impact of climate change on ecosystems. In this workshop, we teach the participants how to handle NetCDF datasets, apply the Mann-Kendall (MK) test and calculate Sen's slope (SS) values on a gridded climate dataset.

Pangeo

We will be using Python packages from the Pangeo community, including Jupyter notebooks and the Xarray toolkit for working with labeled multi-dimensional arrays of data. In addition, we will demonstrate a few basic steps how to improve reproducibility and pro-actively apply FAIR principles when sharing and archiving data and code online for publishing via GitHub and Zenodo.

License and terms of usage

We hope that the materials provided here would be helpful for others. Thus, we share all the lesson materials openly, and also our source codes and lesson materials are openly available.

These materials and code snippets are licensed under the Creative Commons Attribution-ShareAlike 4.0 License CC-BY-SA-4.0

Spatio-temporal trend analysis of spatial climate data (temperature and rainfall) using Python (2021) Alexander Kmoch, Bruno Montibeller, Holger Virro, Evelyn Uuemaa, DOI

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Acknowledgments

Tartu Ülikooli ASTRA projekt PER ASPERA, Maateaduste ja ökoloogia doktorikool 2016-2020, Projekti kood: 2014–2020.4.01.16–0027

EAS

ETAG Mobilitas Pluss / MOBERC34 ETAG Mobilitas Pluss / MOBJD610

MOBERC34

Owner

  • Name: Landscape Geoinformatics Lab
  • Login: LandscapeGeoinformatics
  • Kind: organization
  • Location: Tartu, Estonia

We are the Landscape Geoinformatics working group at the Chair of Geoinformatics, Department of Geography, University of Tartu, Estonia

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Kmoch"
  given-names: "Alexander"
  orcid: https://orcid.org/0000-0003-4386-4450
- family-names: "Virro"
  given-names: "Holger"
  orcid: https://orcid.org/0000-0001-6110-5453
- family-names: "Montibeller"
  given-names: "Bruno"
  orcid: https://orcid.org/0000-0002-5250-8450
- family-names: "Uuemaa"
  given-names: "Evelyn"
  orcid: https://orcid.org/0000-0002-0782-6740
title: "Landscape Geoinformatics BIOGEOMON 2022 workshop materials"
version: 1.0.0
doi: 10.5281/zenodo.5876348
date-released: 2022-06-24
url: "https://github.com/LandscapeGeoinformatics/biogeomon_2022_pangeo"

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Dependencies

environment.yml conda
  • aiohttp
  • cartopy
  • dask
  • datashader
  • descartes
  • fiona
  • fsspec
  • gdal
  • geographiclib
  • geojson
  • geopandas
  • geopy
  • geos
  • h5netcdf
  • hdf4
  • hdf5
  • holoviews
  • hvplot
  • intake
  • intake-xarray
  • ipykernel
  • ipython
  • ipywidgets
  • jupyter
  • jupyterlab
  • mapclassify
  • matplotlib
  • netcdf4
  • notebook
  • numpy
  • pandas
  • pillow
  • proj
  • pymannkendall
  • pyproj
  • python 3.9.*
  • rasterio
  • requests
  • rioxarray
  • scipy
  • seaborn
  • shapely
  • xarray