author = {Reto Stauffer and Matthias Dusch and Fabien Maussion and Georg J. Mayr},
  title = {Making Surface Wind Diagnostics Reproducible, Comparable and Scalable With {foehnix}},
  year = 2019,
  month = 10,
  journal = {Bulletin of the American Meteorological Society},
  institution = {American Meteorological Society},
  status = {submitted},
  abstract = {Given wind measurements and possibly additional information: how can one identify different wind regimes at least as well as human experts but with reproducibility, scalability and a degree of certainty assigned to the classification? While algorithms from the field of machine learning exist to perfor m such a classification task they have been difficult to use by non-specialists. The open-source software package foehnix available in two popular languages - Python and R - gives them tools to perform the whole classification workflow with ease in a few lines of code.  Extensive documentation and working examples add to the ease of use. The paper showcases oehnix identifying strong gusty winds that descend behind a mountain ran ge in California and can catastrophically fan wildfires. Such winds occur globally downstream of mountain s and straits and change temperature, humidity and air quality. They modify the local climate, affect agr iculture e.g. by killing rice plants, influence pollutants/aerosol mixing ratios, and provide ideal spots at which to generate electricity with wind farms or develop kitesurfing or windsurfing infrastructure. foehnix can also classify other wind regimes such as see breezes and valley winds and flexibly inc orporate available information. Applying it makes results from different regions comparable and handling of extensive data sets possible for users from many different fields.}