duncanwp

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

graphcast

mctx

Monte Carlo tree search in JAX

jupyterlab-variableInspector

Variable Inspector extension for Jupyterlab

conformal-prediction

Lightweight, useful implementation of conformal prediction on real data.

Quantus

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

Awesome-Scientific-Language-Models

A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

earth2mip

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.

xarray-tutorial

Xarray Tutorials

dinosaur

geovista

Cartographic rendering and mesh analytics powered by PyVista

neuralgcm

Hybrid ML + physics model of the Earth's atmosphere

geoxarray

Geolocation utilities for xarray

CEDS

Community Emissions Data System (CEDS)

tempestextremes

Extreme weather detection and characterization

neural-lam

Neural Weather Prediction for Limited Area Modeling

xcast

A High-Performance Data Science Toolkit for the Earth Sciences

pyaos.github.io

PyAOS community website.

cmip6

Examples of CMIP6 cloud data analysis with Pangeo

eurec4a-intake

Intake catalogue for EUREC4A field campaign datasets

aibedo

DARPA AIE (AI-Assisted Climate Tipping Point): AIBEDO (A hybrid AI framework to capture the effects of cloud properties on global circulation and regional climate patterns)

target_temp_detection

2022_ML_EES

Course repository for "Machine Learning for Earth and Environmental Sciences" (UNIL, FGSE, Fall 2022)

SCAMPy

A single column model of atmospheric boundary layer turbulence and clouds

gpg

Re-implementation of GP-GOMEA that attempts to be simpler to understand and use than the original.

ESMAC_diags

ESM aerosol-cloud diagnostics

pinacles

aerosol_optics_ml

Code for: "Emulating Aerosol Optics with Randomly Generated Neural Networks." by Geiss, A., Ma, P-L, Singh, B., and Hardin, J. C., (2022)

mam_x_validation

Cross validation inputs and scripts for the MAM box model

gtv_forecasting