AMSIMP is an open-source solution that leverages machine learning to improve numerical weather prediction. Read the paper. Due to data corruption on my end, the model no longer functions.

Features:

• Fast and accurate, AMSIMP's neural networks provide high quality weather forecasts and predictions.
• AMSIMP offers the pretrained operational AMSIMP Global Forecast Model (AMSIMP GFM) architecture. It is trained on a dataset from the past decade, ranging from the year 2009 to the year 2016. Over time, a future model will be trained on a larger dataset.
• AMSIMP offers near real-time numerical weather prediction through initialisation conditions provided by the Global Data Assimilation System.
• The core of AMSIMP is well-optimized Python code. A performance increase of 6.18 times can be expected in comparison against a physics-based model of a similar resolution.
• AMSIMP's high level and intuitive syntax makes it accessible for programmers and atmospheric scientists of any experience level.
• Distributed under the GNU General Public License v3.0, AMSIMP is developed publicly on GitHub.

This package is available on Anaconda Cloud, and can be installed using conda:

$ conda install -c amsimp amsimp  

For more information, please read the documentation on the website.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see this webpage.

AMSIMP appreciates help from a wide range of different backgrounds. Work such as high level documentation or website improvements are extremely valuable. Small improvements or fixes are always appreciated.