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A repository of graph-based neural weather prediction models for Limited Area Modeling.
lang: Python
stars: 272
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A Python package for identifiying, tracking and analysing of clouds and other meteorological phenomena in different types of gridded datasets.
lang: Python
stars: 137
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This package makes it easier to search for and download multiple months/years of historical weather data from the Environment and Climate Change Canada (ECCC) website.
lang: R
stars: 117
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A fast line-by-line code for high-resolution infrared molecular spectra.
lang: Python
stars: 250
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A 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model.
lang: Python
stars: 236
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A unique chemistry module that can be implemented in any atmosphere model used at NCAR.
lang: C++
stars: 9
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A machine learning-based Earth system models that is trained on a wide range of datasets, including reanalyses, forecast data and observations, to provide a robust and versatile model for the dynamics.
lang: Python
stars: 127
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An implementation of the FV3GFS / SHiELD atmospheric model developed by NOAA/GFDL using the NDSL middleware in Python, itself based on GT4Py and DaCe.
lang: Python
stars: 22
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Allows you to run any large weather model with a consumer grade GPU.
lang: Jupyter Notebook
stars: 251
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A library of algorithms for meteorological post-processing and verification.
lang: Python
stars: 123
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A neural network that forecasts precipitation up to 8 hours into the future at the high spatial resolution of 1 km² and at the temporal resolution of 2 minutes with a latency in the order of second.
lang: Python
stars: 300
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An open software platform to train and deploy AI atmospheric prediction model.
lang: Python
stars: 88
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A collaboration server to plan atmospheric research flights.
lang: Python
stars: 78
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A radiative transfer model for the millimeter and sub-millimeter spectral range.
lang: C++
stars: 94
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Data-driven approach for forecasting global weather using graph neural network.
lang: Python
stars: 278
last activity: