Introduction#
Earth2Studio is a Python package built to empower researchers, scientists, and enthusiasts in the fields of weather and climate science with the latest artificial intelligence models/capabilities. With an intuitive design and comprehensive feature set, this package serves as a robust toolkit for exploring this AI revolution in the weather and climate science domain.
Package Design#
Given that the goal of this package is to enable the use to extrapolate and build beyond what is implemented here, we have focused on providing the building blocks to enable this.
Core Design Principles
Modularity
Simple and Explicit Data
API Consistency
User Progression
Test Coverage and Stability
Performance
(in relative order of importance)
The design philosophy of Earth2Studio embodies a modular architecture where the inference workflow acts as a flexible adhesive, seamlessly binding together various specialized software components with well-defined interfaces. Each component within the package serves a distinct purpose in typical inference workflows.
By viewing the inference workflow as a dynamic connector, Earth2Studio facilitates effortless integration of these components, allowing researchers to easily swap out or augment functionalities to suit their specific needs. We recognize that many users will have their own custom workflow needs, thus encourage users to use the provided features as a starting point to build their own.
Significant importance is placed on the interface that enables the connection between the components and the workflow. These are simple python protocols that all variants of a particular component must share. This not only enables a consistent API but also the generalization of workflows.
Key Features#
While Earth2Studio contains a large collection of general utilities, functions and tooling the following six are considered the core. For more information on these features, see the dedicated documentation for each.
Built-in Workflows
Multiple built-in inference workflows to accelerate your development and research.
Prognostic Models
Support for the latest AI weather forecast models offered under a coherent interface.
Diagnostic Models
Diagnostic models for mapping to other quantities of interest.
Datasources
Datasources to connect on-prem and remote data stores to inference workflows.
IO
Simple, yet powerful IO utilities to export data for post-processing.
Statistical Operators
Statistical methods to fuse directly into your inference workflow for more complex uncertainty analysis.