- #WINDS RSS READER FULL#
- #WINDS RSS READER SERIES#
- #WINDS RSS READER DOWNLOAD#
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Stream accounts are free for up to 3 million feed updates and handle personalization (machine learning) for up to 100 users. For Winds the follow suggestions and the list of articles from the feeds you follow is powered by Stream. Stream is an API for building activity feeds.
#WINDS RSS READER FULL#
The full stack can be found on StackShare.io. Mobile application powered by React Native.Team support (share an activity feed with colleagues or friends to discover and collaborate).Playlist support (partially implemented).Help us improve Winds and/or vote on the Roadmap for 2.1 Here's a quick look at what the project has to offer: Beautiful UI Winds is packed full of awesome features behind a beautiful user interface and user experience. Have a popular RSS or Podcast and want to be featured? Reach out to We reply to every message.
#WINDS RSS READER DOWNLOAD#
To get started with Winds, please download the latest release Featured RSS & Podcasts Activity Feeds & Discovery in Winds are powered by Stream, the app leverages Algolia for search, AWS for hosting, MongoDB Atlas for a hosted database (DBaaS), and SendGrid for email. Contributions in form of pull requests are always appreciated. Use the free hosted version or run it on your own server and customize it as you see fit. Winds is a beautiful open-source RSS and Podcast app created using React & Redux on the frontend and Express.js on the backend. Winds - A Beautiful Open Source RSS & Podcast App Powered by GetStream.io Finally, as a demonstration of concept, we discuss how comparing feedback files of the different reanalyses can guide users to useful scales of variability.🛑 Notice: This repository is no longer maintained No further Issues or Pull Requests will be considered or approved. We also identified stations with homogeneity problems in the reported station values, demonstrating how reanalyses can be applied to support quality control for the observed station data. We discuss some typical examples where differences are found, e.g., where the mean wind distributions differ (probably related to either height or model topography differences) and where the correlations break down (because of unresolved local topography) which applies to a minority of stations. Still, the inter-annual variability connected to the North Atlantic Oscillation (NAO) found in the reanalysis surface wind anomalies is in accordance with the anomalies recorded by the stations. As expected from the lower spatial resolution and reduced amount of data assimilated into ERA-20C, the correlation of monthly means decreases somewhat relative to the other reanalyses (in our investigated period of 2007 to 2010). Generally, the correlation between the higher resolved COSMO-REA6 wind fields and station observations is highest, for both assimilated and non-assimilated (i.e., independent) observations. High correlations (larger than 0.9) can be found between stations and reanalysis monthly mean wind speeds all over Germany. We show that for the majority of the stations, the Weibull parameters of the daily mean wind speed frequency distribution match remarkably well with the ones derived from the reanalysis fields. We compare with a regional reanalysis (COSMO-REA6) and two global reanalyses, ERA-Interim and ERA-20C.
#WINDS RSS READER SERIES#
In this period, the station time series in Germany can be expected to be mostly homogeneous. Here we compare the statistical properties of wind speeds observed at 210 traditional meteorological stations over Germany with the reanalyses' near-surface fields, confining the analysis to the recent years (2007 to 2010). Inter-comparing reanalyses via employing independent observations can help to guide users to useful spatio-temporal scales. Reanalysis near-surface wind fields from multiple reanalyses are potentially an important information source for wind energy applications.
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