There are 6 repositories under atmospheric-modelling topic.
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GEOS-Chem "Science Codebase" repository. Contains GEOS-Chem science routines, run directory generation scripts, and interface code. This repository is used as a submodule within the GCClassic and GCHP wrappers, as well as in other modeling contexts (external ESMs).
QGIS toolkit 🧰 for pre- and post-processing 🔨, visualizing 🔍, and running simulations 💻 in the Weather Research and Forecasting (WRF) model 🌀
NSV13, a Ship-to-ship Combat SS13 Server
A python implementation of the ITU-R P. Recommendations for atmospheric attenuation modeling
LOWTRAN atmospheric absorption extinction, scatter and irradiance model--in Python and Matlab
Install WRF with required libraries
Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab
Python toolkit for GEOS-Chem. Contains basic plotting scripts, plus the suite of GEOS-Chem benchmarking utilities.
PyCHAM: CHemistry with Aerosol Microphysics in Python box model for Windows, Linux and Mac
Massive-Parallel Trajectory Calculations (MPTRAC) is a Lagrangian particle dispersion model for the analysis of atmospheric transport processes in the free troposphere and stratosphere.
Model of an idealized Moist Atmosphere: Intermediate-complexity General Circulation Model with full radiation
Field observation quick analysis toolkit
Basic tutorial for cartopy map plotting Python package
This is a repository for storing some demonstration for teaching computational geophysical fluid dynamics..
Plotting dispersion of dust particles emerging from chimney stacks using Gaussian Plume Dispersion Model
Numba-accelerated Pythonic implementation of MPDATA with examples in Python, Julia and Matlab
Python (and C++) interface to PartMC with Jupyter/Python, Julia and Matlab examples
A dynamical core for solving geophysical fluid equations on the sphere with conservative finite difference methods
Python package to fit single and binary photometric SEDs. Automatically pull archive photometry from the main surveys and use Bayesian inference to derive atmospheric parameters.