2018 Workshop on developing Python frameworks for earth system sciences

ECMWF | Reading | 30-31 October 2018

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Workshop description

Python has become increasingly popular in processing environmental data, including the handling of weather forecast, climate and oceanographic data. In recent years the Python eco-system has greatly improved its handling of large multi-dimensional data with the development of pandas, xarray, dask and links to machine learning frameworks. Jupyter notebooks are now a popular interface for scientists to exchange their work in Python. The continuing challenge is to apply these technologies with new sources of big earth science data sets, such as the Copernicus Climate Data Store.  

After the success of last year's workshop, ECMWF is happy to invite developers of Python frameworks in the field of environment data to another two day workshop in 2018. The workshop assessed the current status of Python packages around the world through presentations of developments. Participants were able to join discussions on how interoperability between different packages can be achieved and how efforts between different developments can be harmonised.

Presentations and Recordings

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One year on … where are we?
Stephan Siemen (ECMWF)

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Python in the Copernicus Climate Change Service
Gionata Biavati (ECMWF)

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CFGRIB: easy and efficient GRIB file access in xarray
Alessandro Amici (B-Open)

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Metview's new Python interface
Iain Russell (ECMWF)

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Easy visualisation with Magics
Sylvie Lamy-Thepaut (ECMWF)

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Improving integration of XArray into MetPy
Ryan May (UCAR / Unidata)

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CDO's python bindings
Ralf Müller (MPI for Meteorology)

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The Open Data Cube (ODC) - A tool to increase the value and impact of global Earth observation satellite data
Dan Wicks (Satellite Applications Catapult)

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The WRF4G Python Framework for regional climate simulations with WRF model
Antonio S. Cofiño (University of Cantabria)

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Parallel Python Tools for Handling Big Climate Data
Sheri Mickelson (NCAR)

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The Pangeo ecosystem for data proximate analytics
Joseph Hamman (NCAR)

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Dask: Scaling Analytics in Python
Matthew Rocklin (Anaconda)

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Why Pangeo? What is it and why we need it geoscience
Theo McCaie (Informatics Lab, UK Met Office)

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GLOFRIM 2.0: coupling hydrologic and hydrodynamic models across scales for improved flood simulations
Dirk Eilander (Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam)

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Rainymotion and RainNet: optical flow and deep learning models for radar-based precipitation nowcasting
Georgy Ayzel (University of Potsdam)

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Python frameworks for the integration of a real-time data hub for meteorological and hydrological forecasting – benchmarks and design decisions
Alberto Sabater Morales, Jackie Leng (Kisters)

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Open radar science for fun and, yes, even profit
Scott Collis (Argonne National Laboratory)

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METplus - a Python-Wrapped Verification Capability Unifying the US Verification and Validation Community
Tara Jensen (NCAR & DTC)

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Heterogeneous computing with Python: why we need it?
Javier Vegas-Regidor (Barcelona Supercomputing Center)

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