4D-Var Data Assimilation

This project implements the 4D-Var (Four-Dimensional Variational Data Assimilation) technique to estimate the state of a dynamic system. The code is designed to work with the Lorenz '95 model, a simplified atmospheric model often used in data assimilation studies.

Table of Contents

• Installation
• Usage

Installation

To set up the environment, follow these steps:

1. Clone the repository:

   git clone https://github.com/Oussamamouhtal/DataAssimilation.git
   cd DataAssimilation

2. Create a virtual environment:

   python -m venv env

3. Activate the virtual environment:

   • On Windows:

     .\env\Scripts\activate

   • On macOS and Linux:

     source env/bin/activate

4. Install the required packages:

   pip install -r requirements.txt

Usage

To run the 4D-Var data assimilation process, you need to have the necessary model, operators, and solver files in place. The main function is fourDvar, implemented in fourdvar.py, which returns a list of values of the quadratic cost function. You can run logger.py to generate a plot showing the convergence of the Gauss-Newton process or plot just the second inner loop to benchmark different inner solvers.