Modular Ocean Model
fortran numerical-modelling ocean-circulation-models numerical-modeling ocean-circulation climate-modeling
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- Fortran
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climate-modeling
Here are 34 public repositories matching this topic...
We present the NASA Ames Legacy Mars Global Climate Model for public release. This model uses a modified version of the ARIES/GEOS dynamical core coupled with a set of Mars physics packages to simulate the martian climate. The physics packages include the treatment of surface properties, a ground temperature model, a planetary boundary layer sch…
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- Jupyter Notebook
Integrated Methane Inversion workflow repository.
python aws scientific-computing cloud-computing climate-change geos-chem methane atmospheric-chemistry atmospheric-modeling inversions atmospheric-composition greenhouse-gases climate-modeling inverse-modeling analytical-inversions
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- Jupyter Notebook
Implementation of the FV3GFS / SHiELD atmospheric model in Python
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- Python
[NeurIPS 24] Probablistic Emulation of a Global Climate Model with Spherical DYffusion
pytorch diffusion neurips pytorch-lightning climate-modeling large-ensembles ai-for-science neurips-2024 dyffusion
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- Python
NDE: Climate Modeling with Neural Diffusion Equation, ICDM'21
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- Python
Code repository associated with "Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification" (Getter, Bessac, Rudi, Feng).
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- Python
ConceptualClimateModels.jl is a Julia package for creating and analysing conceptual models of climate, such as energy balance models, glaciation cycle models, or climate tipping models.
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- Julia
IPSL-AID is a high-performance research framework for climate data downscaling based on diffusion models, designed for GPU clusters and HPC systems.
machine-learning deep-learning pytorch neural-networks atmospheric-science super-resolution climate-science downscaling diffusion-models climate-modeling
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- Python
A project on emulating an aerosol microphysics model, including physical constraints in the Deep Learning architecture.
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- Python
Climate Modeling with Neural Advection-Diffusion Equation, Springer KAIS
weather node climate noaa weather-forecasting climate-modeling advection-diffusion advection-diffusion-equation neural-advection-diffusion-equation
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- Python
Climate Modeling with Neural Diffusion Equation, ICDM'21
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- Python
A python script to access, visualize and extract time series of CRU long-term climate data at discrete locations
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- Python
Meteorological data from radar and satellite sources often contain noise due to air turbulence and device manipulation, leading to inaccuracies in predictions. Mitigating with ANN & LSTM
time-series netcdf lstm meteorology satellite-data artificial-neural-network climate-change rainfall-prediction weather-forecasting climate-modeling precipitation-forecasting
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- Jupyter Notebook
A Novel Clustered Support Vector Machine with Reduced Support Vectors for Big Data Classification
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- MATLAB
A powerful Jupyter-based toolkit for analyzing and visualizing Geos-Chem atmospheric chemistry model outputs. Supports both NetCDF and BPCH formats with interactive visualizations and custom colormaps.
visualization python jupyter-notebook data-visualization xarray netcdf open-science scientific-visualization scientific-computing earth-science data-analysis atmospheric-science climate-science cartopy geos-chem atmospheric-chemistry scientific-software environmental-science climate-modeling research-tools
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- Jupyter Notebook
Repo for creating machine learning emulators for simplified physics schemes in climate models.
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- Python
Quantifying accuracy drawbacks across various 1d and 0d methods of modelling large-scale Earth systems and interpreting results through the lens of spectroscopy, radiative transfer and thermodynamics.
atmospheric-modelling 1d-model radiative-transfer-models climate-modeling 0d-model radiative-equilibrium-models
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Configuration boilerplate for using PyTorch ML models in ICON.
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- Fortran
SPARC is a physics-constrained spatial machine-learning pipeline that trains an ensemble of geographically-weighted models, validates causal relationships via directed acyclic graphs (DAGs), and simulates "what-if" intervention scenarios with built-in uncertainty quantification. It is designed to be domain-agnostic.
machine-learning geospatial causal-inference directed-acyclic-graph climate-adaptation climate-modeling physics-informed-ml urban-heat climate-resilience forcesmip spatial-machine-learning
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- Python
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