Welcome to Anemoi’s documentation!

Warning

This documentation is work in progress.

Anemoi is a framework for developing machine learning weather forecasting models. It comprises of components or packages for preparing training datasets, conducting ML model training and a registry for datasets and trained models. Anemoi provides tools for operational inference, including interfacing to verification software. As a framework it seeks to handle many of the complexities that meteorological organisations will share, allowing them to easily train models from existing recipes but with their own data.

An Anemoi dataset is a thin wrapper around a zarr store that is optimised for training data-driven weather forecasting models. It is organised in such a way that I/O operations are minimised.

This documentation is divided into two main sections: how to use existing datasets and how to build new datasets.

• Overview

• Installing

Using training datasets

• Introduction

• Opening datasets

• Methods and attributes

• Subsetting datasets

• Combining datasets

• Selecting variables

• Selecting grid points

• Statistics

• Other operations

• Matching attributes

• Miscellaneous functions

• Configuration

Building training datasets

• Introduction

• Operations

• Sources

• Filters

• Gathering statistics

Command line tool

• Introduction

• create

• inspect

• compare

• copy

Anemoi packages

• anemoi-utils

• anemoi-datasets

• anemoi-models

• anemoi-training

• anemoi-inference

License

Anemoi is available under the open source Apache License.