(2018) Geospatial web services pave new ways for server-based on-demand access and processing of

With the unprecedented increase of orbital sensor, in situ measurement, and simulation data there is a rich, yet not leveraged potential for obtaining insights from dissecting datasets and rejoining them with other datasets. Obviously, goal is to allow users to "ask any question, any time, on any size", thereby enabling them to "build their own product on the go".One of the most influential initiatives in EO is EarthServer which has demonstrated new directions for flexible, scalable EO services based on innovative NoSQL

This paper presents a method to extrapolate wind-speed data and to calculate wind-speed and dynamic pressure maps for the complex topography of a mountain rainforest area in the tropical Andes of southeastern Ecuador. The spatial differentiation of dynamic wind pressure in this area is claimed to be a major determinant of the altitude of the tree-line ecotone and to affect the tree line's physiognomy. The paper presents a hybrid method encompassing statistical data analysis using the Weibull distribution and a digital terrain analysis, taking topographical shelter effects into account. The method is used to derive mean and maximum wind-speed and dynamic pressure maps to reveal whether the tree-line ecotone is influenced by direct wind effects. On average, the tree-line ecotone on the eastern slopes shows a clear average depression of ~50 m. These slopes are affected by higher dynamic wind stress, so have a more disturbed canopy. These altered vegetation structures may be caused mainly by direct wind effects and to a smaller extent by indirect effects, such as high humidity. Zusammenfassung: Es wurde eine Methode entwickelt, um räumliche Windgeschwindigkeits-sowie Staudruckkarten für ein topographisch diverses Gebiet in den tropischen Anden Südecuadors zu generieren. Die räumliche Variabilität des dynamischen Staudrucks beeinflusst entscheidend die Höhenposition und Physiognomie des lokalen Waldgrenzökotons im Studiengebiet. Eine Hybridmethode aus statistischer Daten-und digitaler Geländemodellanalyse, um mittlere und maximale Windgeschwindigkeits-sowie Staudruckkarten zu generieren, wird präsentiert. Mithilfe der Weibull-Verteilung werden dabei die Winddaten statistisch beschrieben und Höhen-sowie Topographieeffekte werden bei der Interpolation mit berücksichtigt. Die generierten Karten werden anschließend für weitere Baumgrenzökotonuntersuchungen herangezogen. Hierbei wird hauptsächlich untersucht, ob die Herabsetzung des Baumgrenzökotons im Studiengebiet auf den Einfluss direkten Windstresses zurückzuführen ist. Im Durchschnitt ist das Baumgrenzökoton der östlichen Hänge im Studiengebiet um ~50 m herabgesetzt. Osthänge werden stärker durch dynamischen Windstress beeinflusst. Die daraus resultierenden Veränderungen der Vegetationsstruktur des Baumgrenzökotons sind größtenteils auf direkte Windeffekte und zu einem kleineren Teil auf indirekte Effekte, wie z.B einer erhöhten Feuchtigkeit, zurückzuführen.

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 lowlikelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science-policy interface to manage high impacts.

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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.

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