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The ECMWF Summer of Weather Code (ESoWC) is an innovation programme run by ECMWF and Copernicus with the aim to drive innovation and open-source developments in the Earth science community.
Since 2018, each summer, up to ten developer teams work together with experienced mentors from ECMWF and Copernicus on innovative projects related to software development, web development or applied data science.
ESoWC 2019 Teams during the final ESoWC Day
@ ECMWF Reading Headquarters
HOW DOES IT WORK?
Four steps from Application Period to the Final ESoWC Day
01 March - 15 April
Browse through the ESoWC 2022 challenges on GitHub. Ask questions and together with ECMWF mentors, you can tailor your submission. You have time until 15 April 2022 to submit your proposal.
Announcement of selected proposals
The ESoWC 2022 teams will be announced on 29 April 2022. You can follow ESoWC on Twitterto get all updates.
02 May - 31 August
The 4-month long coding period starts on 2 May 2022 and lasts until 31 August 2022. During this time, the selected teams team up with experienced mentors and experts in weather, climate, atmosphere and cloud computing.
The Final ESoWC Day
The ESoWC Day is a celebratory closure of the programme. All ESoWC teams will be invited to present their project results.
This project is about using CrowdWater data and to translate this data into something that can be used in flood forecasting models. CrowdWater is an interesting initiative in which people send geo-referenced pictures of streams or rivers, along with the corresponding variations of water level.
The difficulty of this project lies in the variability of the CW data and in the difference of scale between the CW data and the GloFAS (or EFAS) data. We have indeed a very coarse representation of rivers in GloFAS, while in CW data we have more information about the smaller rivers.
The project aims to utilize CW data to improve model forecast.
Estimating and correcting the biases of climate models, as well as assessing associated uncertainties, are crucial for many climate impact-studies and other applications. In our project we aim to develop a software package – building upon the ISIMIP3b-code – that allows users to apply bias correction, using different methods, in a variety of situations.
We aim to:
1) develop an easy to use, flexible software package that users can employ in different computing environments,
2) extend the ISIMIP approach with several small improvements,
3) implement a systematic evaluation framework for bias correction to support the estimation of uncertainty.
The Wildfire Explorer will allow users to create plots of wildfire emission and activity data on-demand.
This application will consist of a GUI where the user can select the geographical domain of interest, the date period of a specific event, a reference period for comparison (optional), the variable considered and the plot type.
The processing of the data will be automated and optimised using a PostGIS database. The GUI will be built from a Jupyter notebook with interactive widgets and the data will be processed from the CAMS Global Fire Assimilation System (GFAS).
ECMWF as an organization provides a variety of applications but there is no central dashboard to get a global overview of the information from the user’s favorite app.
The aim of this project is to move forward the existing user dashboard prototype closer to operations by building on the already existing functionalities.
The current state of the project offers a central dashboard to add different widgets to it.
The plan is to move forward with integration from the individual apps using a simple discoverable widget-api comparable to GetCapabilities for OGC Web Services, to add the widgets from the individual applications as well.
Magics is ECMWF's meteorological plotting software that supports plotting contours, wind fields, observations, satellite images, symbols, text, axis and graphs.
The project aims to utilize the power, flexibility and extensibility of the python library matplotlib (https://github.com/matplotlib/matplotlib) to improve the drivers for ECMWF's magics-python library (https://github.com/ecmwf/magics-python).
These improvements would allow the users to create more customizable and interactive plots. This project also aims to continue the development of ECMWF's magpye (https://github.com/ecmwf/magpye), which provides a more pythonic and user-friendly API to magics.
The final aim is to create resources such as tutorials and documentation for magics and magpye.