Xclim: Xarray-based Climate Data Analytics

TL;DR: pyESDv1.0.1 is an open-source Python framework for empirical-statistical downscaling of climate information. It implements the entire downscaling cycle and includes well-established machine learning algorithms.

TL;DR: Climate services provide tailored information and knowledge about climate risks and opportunities to agricultural sector, enabling effective risk management and adaptation strategies.

TL;DR: Projected changes in extreme event indices for Alaska show large increases in temperature thresholds for the hottest and coldest days of the year, warm spell duration, and decreases in cold spell duration. Increases in yearly maximum 1-day and 5-day precipitation amounts and the number of consecutive wet days are also projected.

TL;DR: ICCLIM as mentioned in this paper is a flexible python software package to calculate climate indices and indicators, which is designed with performance and optimisation in mind, because the goal is to provide on-demand calculations for users.

TL;DR: This work presents cf-python, a Python software library that conforms to the model and can manipulate any CF-compliant dataset, and compares this CF data model with the ISO 19123 coverage model, the Open Geospatial Consortium CF netCDF standard, and the Unidata Common Data Model.