Lists (5)
Stars
A non-exhaustive list of open resources to help environmental scientists get started with machine learning
👋 Xplique is a Neural Networks Explainability Toolbox
CMIP variable to branded variable mapping
Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters
The Climate Variability Diagnostics Package (CVDP) developed by NCAR's Climate Analysis Section is an analysis tool that documents the major modes of climate variability in models and observations.
This page reviews and organizes emerging hybrid Earth System Models (ESMs), which combine machine learning and physics-based components.
This is a repository of my python notebooks that may be of general interest.
A conda-smithy repository for basemap.
Xarray extension for unstructured climate and global weather data analysis and visualization.
Comprehensive EOF analysis in Python with xarray: A versatile, multidimensional, and scalable tool for advanced climate data analysis
This repository contains the “beta version” of Datasheets for Earth Science Datasets released for feedback, comments, and questions from the broader Earth science community.
Hybrid ML + physics model of the Earth's atmosphere
CUPiD is a “one stop shop” that enables and integrates timeseries file generation, data standardization, diagnostics, and metrics from all CESM components.
machine learning for the atmospheric sciences - a CSU course
create masks of geospatial regions for arbitrary grids
year when a certain warming level was reached in CMIP5 and CMIP6 data
Open repository for the WCRP WGCM Infrastructure Panel (WIP)
Simple utility for retrieving and plotting ThreadEx station data
Earth-2 Model Intercomparison Project (MIP) is a python framework that enables climate researchers and scientists to inter-compare AI models for weather and climate.
Analysis framework and collection of process-oriented diagnostics for weather and climate simulations
Tools for manipulating and creating netCDF inputs for FMS managed models
Repository containing presentations made during the Copernicus Earth Observation Data Visualisation workshop series (16 May to 20 June 2023), including links to the resulting best practice guide.
Python code to assist in familiarizing meteorologists with machine learning
Python code to assist in familiarizing meteorologists with machine learning
The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.