deep4downscaling

Description

deep4downscaling is a Python library designed for developing deep learning models for statistical downscaling. The library focuses on two main objectives:

Ease of Research: deep4downscaling provides a suite of standard techniques essential for the statistical downscaling field, such as:

• Data Preprocessing
• Data transformations
• Standardization and Normalization

By offering these foundational methods, researchers can focus on innovative tasks such as novel architecture development, rather than re-implementing standard routines from scratch.

Established Deep Learning Models: deep4downscaling also supplies established deep learning models that can be used to downscale global climate model outputs. Beyond the models themselves, the library provides:

• Tools to compute and generate projections (ensuring compatibility with standard climate data formats like NetCDF).
• Scripts for proper post-processing (e.g., bias correction, domain mapping).

In addition to these main goals, deep4downscaling includes:

Comprehensive Evaluation Metrics: A dedicated collection of evaluation metrics widely recognized in the downscaling community, enabling researchers to thoroughly assess model performance.

eXplainable Artificial Intelligence (XAI) Techniques Tailored for Downscaling: XAI techniques adapted for statistical downscaling models. This ensures that generated projections are transparent and trustworthy—a critical feature for decision-makers and other end-users who rely on climate modeling outputs.

By combining core data transformations, established deep learning models, advanced evaluation metrics, and explainable AI, deep4downscaling aims to empower the research community to develop and validate cutting-edge downscaling solutions with greater efficiency and confidence.

Table of Contents

• Installation
• Usage
• Documentation
• Contributing

Installation

1. Clone the Repository

git clone https://github.com/SantanderMetGroup/deep4downscaling/
cd deep4downscaling

2. Create a conda environment from the provided .yml

conda env create -f requirements/deep4downscaling-gpu.yml

If you prefer a CPU-only setup, use the requirements/deep4downscaling-cpu.yml file instead.

Usage

We provide a set of Jupyter notebooks in the notebooks directory that demonstrate the basic functionality of the deep4downscaling library. These notebooks cover topics such as:

• Data preprocessing and transformations
• Model training and evaluation
• Using the built-in metrics and explainable AI features

As new features are developed and added to deep4downscaling, additional example notebooks will be included to help you stay up-to-date with the latest capabilities.

Documentation

While deep4downscaling does not currently offer a formal documentation website, all library functions include comprehensive docstrings describing their purpose, parameters, and return values. This ensures that the code is self-explanatory for developers who want to use or extend the library.

For further guidance on how to use deep4downscaling, please refer to:

• The notebooks in notebooks, which provide example workflows.
• The docstrings in the source code, which offer detailed explanations of functions and classes.

Should you have any questions or need clarifications, feel free to open an issue or contribute to improving the documentation.

Contributing

We welcome contributions of all kinds to deep4downscaling—from reporting bugs and suggesting improvements to submitting pull requests for new features or fixes. Here’s how you can get involved:

1. Report Bugs:
   If you find an issue, please open a new GitHub issue with details on how to reproduce it.

2. Suggest Enhancements or New Features:
   We value user feedback! Feel free to create an issue describing your idea or improvement.

3. Submit Pull Requests:

   • Fork the repository and create a new branch for your changes.
   • Make your changes or add your new feature.
   • Open a pull request (PR) to the main branch of deep4downscaling.