index.xml

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    <title>AMSIMP</title>
    <link>http://amsimp.github.io/</link>
    <description>Recent content on AMSIMP</description>
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      <title>About Us</title>
      <link>http://amsimp.github.io/about/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>http://amsimp.github.io/about/</guid>
      <description>Some information about AMSIMP
AMSIMP is an open-source solution which leverages machine learning to improve numerical weather prediction with Python. It was created in 2020. AMSIMP will always be 100% open source software, free for all to use and released under the terms of the GPL-3.0 license.
Corporate Structure The role of AMSIMP&amp;rsquo;s corporate structure is to ensure, through working with and serving the broader AMSIMP community, the long-term well-being of the project, both technically and as a community.</description>
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    <item>
      <title>AMSIMP Code of Conduct</title>
      <link>http://amsimp.github.io/code-of-conduct/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>http://amsimp.github.io/code-of-conduct/</guid>
      <description>Code of Conduct All participants of AMSIMP are expected to abide by our Code of Conduct, both online and during in-person events that are hosted and/or associated with AMSIMP.
The Pledge In the interest of fostering an open and welcoming environment, we pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.</description>
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    <item>
      <title>AMSIMP Roadmap</title>
      <link>http://amsimp.github.io/roadmap/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>http://amsimp.github.io/roadmap/</guid>
      <description>Overview The main aim of AMSIMP is to develop an open-source solution that leverages machine learning to improve numerical weather prediction. Currently, as far as we are aware, there are no machine learning models in use for numerical weather prediction. We’re applying machine learning to weather prediction and making it available for everyone: citizens in particular. The goal of this page is to highlight the short and long term aims and goals of AMSIMP.</description>
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    <item>
      <title>Community</title>
      <link>http://amsimp.github.io/community/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>http://amsimp.github.io/community/</guid>
      <description>AMSIMP is a community-driven open source project. The AMSIMP leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the AMSIMP Code of Conduct for guidance on how to interact with others in a way that makes the community thrive.
We offer several communication channels to learn, share your knowledge and connect with others within the AMSIMP community.
Participate online The following are ways to engage directly with the AMSIMP project and community.</description>
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    <item>
      <title>Contribute to AMSIMP</title>
      <link>http://amsimp.github.io/contribute/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>http://amsimp.github.io/contribute/</guid>
      <description>Thanks so much for being interested in contributing to AMSIMP!
To thrive, the AMSIMP project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to AMSIMP:
 Writing code Assisting with research related to writing code, whether that be implementing a new type of machine learning model or modifying the traditional physical model. Reviewing pull requests from other developers Developing educational materials (tutorials, presentations, etc.</description>
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    <item>
      <title>Installing AMSIMP</title>
      <link>http://amsimp.github.io/install/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>http://amsimp.github.io/install/</guid>
      <description>If you don&amp;rsquo;t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and some of the other necessary packages to successfully install and run the software.
AMSIMP can be installed with conda, or with pip. For more detailed instructions, consult our Python and AMSIMP installation guide below.
conda Before installation via this particular method, TensorFlow needs to be installed through the Python Package Index (PyPI) using the following command:</description>
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      <title>News</title>
      <link>http://amsimp.github.io/news/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>http://amsimp.github.io/news/</guid>
      <description>AMSIMP 0.6.0 Release 2 Jan, 2021 &amp;ndash; AMSIMP 0.6.0 is now available. This is a major overhaul with the vast majority of the functionality available in previous versions of the software has now become deprecated. Evidently, the software is still in an alpha stage of release, however in the future, the software should not exhibit such feature breaking releases. The current model architecture is based on the ConvLSTM layer, and has a spatial resolution of approximately 100 kilometres.</description>
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