Making ECMWF open weather forecast data more accessible to users: maintenance challenges

ECMWF are moving towards an extensive open data policy, providing data to a broader user base extending beyond operational forecasters in Member and Co-operating states and commercial customers. Beginning in 2020, the first phase saw the opening of hundreds of web forecast charts (www.charts.ecmwf.int) and made archived data available under a Creative Commons (CC BY 4.0) open licence. This momentum continued in January 2022 with the introduction of an open subset of real-time medium-range forecast data, with ongoing updates incorporating new parameters and datasets. Notably, the latest update in February 2024 included increasing the resolution from 0.4 to 0.25 and included the new Artificial Intelligence/Integrated Forecasting System (AIFS) model data.This phased move towards free and open data aims to support creativity, innovation and reproducibility in scientific research and weather applications. However, this can not be achieved by only opening the real time data. The users need to be able to find and easily use the data and integrate it into their own research work or application workflows.To address this, additional efforts are underway to improve the data's FAIR (Findable, Accessible, Interoperable and Reusable) attributes. Key developments include the creation of open source Python libraries for data processing and visualisation, alongside the introduction of a set of Jupyter notebooks, each of which is reproducing one open weather forecast chart - from the downloading the data to the visualisation.However, the tools and data constantly change, and keeping up with these changes presents a significant challenge if not designed with maintenance in mind.This talk will provide an overview of the open forecast web charts and the use of Jupyter notebooks for their reproduction, followed by an exploration of the maintenance challenges and future plans.