Name: ECMWF Code for Earth
Type: Organization
Bio: ECMWF Code for Earth is a collaborative programme where each summer several developer teams work on innovative earth sciences-related software.
Twitter: ECMWFCode4Earth
Location: Online
Blog: https://codeforearth.ecmwf.int
ECMWF Code for Earth's Projects
ESoWC 2021: Comparing Atmospheric Composition Datasets
A project for matching flight ID with AMDAR data using selenium
Bias correction of air quality CAMS model predictions by using OpenAQ observations.
repository for 2023 Code for Earth project to develop a tool for exploration of CAMS atmospheric composition datasets
Realtime Streaming Anomaly-Detection on Massive Log Files
Archive of ECMWF and ESoWC support in the development of BlenderNC
Have a look at the challenges proposed for the 2019 edition of ECMWF's Summer of Weather Code.
ECMWF Summer of Weather Code 2020 challenges
Discover the ECMWF Summer of Weather Code 2021 challenges!
Discover the ECMWF Summer of Weather Code 2022 challenges.
Discover the ECMWF Code for Earth 2023 challenges
Discover the ECMWF Code for Earth 2024 challenges
Exploring ECMWF resources and datasets using natural language queries.
Python package to easy access to weather and climate data
Main repository for challenge 31/2022: Flood forecasting: the power of citizen science
Detect Anomaly in Air Quality Station (DAAQS)
ECMWF Code4Earth 2023 challenge #12: Compression of Geospatial Data with Varying Information Density
DeepR: Deep Reanalysis
This is the source code belonging to the Machine Learning and Artificial Intelligence challenge #25 of the ECMWF Summer of Weather Code 2020 programme. Goal of the challenge is to build an easy to use Conversational Virtual Assistant for ECMWF and C3S's users.
Compressing atmospheric data into its real information
Tool for forecast of fire radiative power.
Repo for codes and data necessary for the ESoWC Challenge #3
Challenge #13 - Tools and examples for visualising HPC performance data for the IFS
Challenge #11 - Creating Jupyter-based OpenIFS training material
Machine Learning for Pollution Monitoring