technicalreports.bib

@techreport{lang2020,
  author = {Moritz N. Lang and Lisa Schlosser and Torsten Hothorn and Georg J. Mayr and Reto Stauffer and Achim Zeileis},
  title = {Circular Regression Trees and Forests with an Application to Probabilistic Wind Direction Forecasting},
  year = 2020,
  eprint = {2001.00412},
  archiveprefix = {arXiv},
  primaryclass = {stat.ME},
  abstract = {While circular data occur in a wide range of scientific fields, the methodology for distributional modeling and probabilistic forecasting of circular response variables is rather limited. Most of the existing methods are built on the framework of generalized linear and additive models, which are often challenging to optimize and to interpret. Therefore, we suggest circular regression trees and random forests as an intuitive alternative approach that is relatively easy to fit. Building on previous ideas for trees modeling circular means, we suggest a distributional approach for both trees and forests yielding probabilistic forecasts based on the von Mises distribution. The resulting tree-based models simplify the estimation process by using the available covariates for partitioning the data into sufficiently homogeneous subgroups so that a simple von Mises distribution without further covariates can be fitted to the circular response in each subgroup. These circular regression trees are straightforward to interpret, can capture nonlinear effects and interactions, and automatically select the relevant covariates that are associated with either location and/or scale changes in the von Mises distribution. Combining an ensemble of circular regression trees to a circular regression forest yields a local adaptive likelihood estimator for the von Mises distribution that can regularize and smooth the covariate effects. The new methods are evaluated in a case study on probabilistic wind direction forecasting at two Austrian airports, considering other common approaches as a benchmark.}
}
@techreport{stauffer2019,
  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},
  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.}
}