Soil quality both increases crop production and improves resilience to climate change

A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)

《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版

MATLAB for AI

Source code and full output of analysis in "Country-level social cost of carbon"

Automatically exported from code.google.com/p/miyoshi

JuliaClimate Notebooks

C implementation of 4DEnVar using the GSL

LPJmLmdi - model-data integration for the LPJmL dynamic global vegetation model (R package)

Welcome to the central open-source repository of LPJmL at PIK. You are free to download the code under the AGPLv3 license, see LICENSE file. Have fun. Please note that there is absolutely no suppor…

Machine learning forecast model for WxChallenge

ICTP Regional Climate Model

Processing data from micrometeorological Eddy-Covariance systems

Code to reproduce the analysis and figures in 'A constraint on historic growth in global photosynthesis due to increasing CO2' Keenan et al. 2021. Nature https://www.nature.com/articles/s41586-021-…

OLDLee的Python气象应用私教课

Source code for figure generation and analysis of the ENGAGE netzero scenario analysis

气象相关书籍合集（持续更新）

Data and code for Lesk et al. 2021

Scripts used in the analysis of Grant etal (2021)

Python (3.8+) version of CLIMADA

See https://github.com/CLIMADA-project/climada_python first

Data used deal with CMIP6 analysis

Notebooks of papers from the SantanderMetGroup

Raster-based Spatial Analytics for Python

Toward Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes, by Zhuo Wang, Yujing Jiang, Hui Wan, Jun Yan, Xuebin Zhang