Low‐likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high‐impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low‐likelihood high‐impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium‐Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record‐shattering August 2020 California‐Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low‐likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low‐likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science‐policy interface to manage high impacts.

There is a growing demand to train Earth Observation (EO) data users in how to access and use existing and upcoming data. A promising tool for data-related training is computational notebooks, which are interactive web applications that combine text, code and computational output. Here, we present the Learning Tool for Python (LTPy), which is a training course (based on Jupyter notebooks) on atmospheric composition data. LTPy consists of more than 70 notebooks and has taught over 1000 EO data users so far, whose feedback is overall positive. We adapted five guiding principles from different fields (mainly scientific computing and Jupyter notebook research) to make the Jupyter notebooks more educational and reusable. The Jupyter notebooks developed (i) follow the literate programming paradigm by a text/code ratio of 3, (ii) use instructional design elements to improve navigation and user experience, (iii) modularize functions to follow best practices for scientific computing, (iv) leverage the wider Jupyter ecosystem to make content accessible and (v) aim for being reproducible. We see two areas for future developments: first, to collect feedback and evaluate whether the instructional design elements proposed meet their objective; and second, to develop tools that automatize the implementation of best practices.

Cloud-based services introduce a paradigm shift in how users access, process and analyse Big Earth data. A key challenge is to align the current state of how users access, process and analyse the data with trends and roadmaps large data organisations layout. In addition, due to the increased availability of open data, a more diverse user base wants to take advantage of Earth science data leading to new user requirements. We run a web-based survey among Big Earth data users to better understand the motivation to migrate to cloud-based services as well as the challenges and opportunities that might arise. Results show an overall interest in moving to cloud-based services but air an insufficient literacy in cloud systems and a lack of trust due to security concerns and opacity of emerging costs. These gaps demand efforts on three levels. First, cloud services shall be targeted at intermediate users instead of policy- and decision-makers and over-engineered systems with a high level of abstraction should be avoided. Second, more substantial capacity-building efforts are required to decrease the existing gap in cloud skills and uptake. Third, a cloud certification mechanism could help in building up overall trust in cloud-based services.

Learning tool for Python (LTPy) is a Python-based series of notebooks on different open satellite-and model-based data on atmospheric pollution and climate. LTPy features data from six different satellites, including the Copernicus satellites Sentinel-3 and Sentinel-5 as well as the polar orbiting meteorological satellite series Metop and five different model-based product types from the two Copernicus services on Atmosphere Monitoring (CAMS) and Emergency Management (CEMS).

The training course on 'Dust Aerosol Detection, Monitoring and Forecasting' is a Pythonbased training that provides a hands-on introduction to satellite-, ground-and model-based data used for dust monitoring and forecasting. The course is organised in three main chapters: (i) observations (satellite-and ground-based), (ii) forecast models and a (iii) practical case study. It features twelve different datasets derived from satellites, groundbased measurement networks and forecast models. The course material is developed in the form of well-described and modular Jupyter notebooks. In total, the course consists of 17 notebooks; 12 data workflows and five practical exercise notebooks.