This repository provides a python implementation of the BRaVDA solar wind data assimilation scheme described in:
Lang, M., & Owens, M. J. (2019). A variational approach to data assimilation in the solar wind. Space Weather, 17, 59–83. https://doi.org/10.1029/2018SW001857
It combines the output of a coronal model, such as WSA or MAS, with the in situ observations, typically near 1 AU. It returns the optimum reconstruction of the solar wind, acounting for errors in both models and observations.
BRaVDA is written in Python 3.9.13 and has a range of dependencies, which are listed in bravda_env.yml files. Because of these dependencies, the simplest way to work with BRaVDA in conda is to create its own environment. With the anaconda prompt, in the root directory of BRaVDA, this can be done as:
>>conda env create -f bravda_env.yml
>>conda activate bravda_env
Please contact either Matthew Lang or Mathew Owens.
If you use BRaVDA in a publication or presentation, please cite the software using the Zenodo reference with DOI:10.5281/zenodo.7892408
To cite this project, including the scientific basis and functionality of BRaVDA, please use:
Lang, M., & Owens, M. J. (2019). A variational approach to data assimilation in the solar wind. Space Weather, 17, 59–83. https://doi.org/10.1029/2018SW001857
and
Lang, M., Witherington, J., Turner, H., Owens, M. J., & Riley, P. (2021). Improving solar wind forecasting using data assimilation. Space Weather, 19, e2020SW002698. https://doi.org/10.1029/2020SW002698