@article{Buerger2019,
  author    = {B{\"u}rger, Gerd},
  title     = {A seamless filter for daily to seasonal forecasts, with applications to Iran and Brazil},
  series = {Quarterly Journal of the Royal Meteorological Society},
  volume    = {146},
  journal   = {Quarterly Journal of the Royal Meteorological Society},
  number    = {726},
  publisher = {WILEY-VCH},
  address   = {Weinheim},
  pages     = {14},
  year      = {2019},
  abstract  = {A digital filter is introduced which treats the problem of predictability versus time averaging in a continuous, seamless manner. This seamless filter (SF) is characterized by a unique smoothing rule that determines the strength of smoothing in dependence on lead time. The rule needs to be specified beforehand, either by expert knowledge or by user demand. As a result, skill curves are obtained that allow a predictability assessment across a whole range of time-scales, from daily to seasonal, in a uniform manner. The SF is applied to downscaled SEAS5 ensemble forecasts for two focus regions in or near the tropical belt, the river basins of the Karun in Iran and the Sao Francisco in Brazil. Both are characterized by strong seasonality and semi-aridity, so that predictability across various time-scales is in high demand. Among other things, it is found that from the start of the water year (autumn), areal precipitation is predictable with good skill for the Karun basin two and a half months ahead; for the Sao Francisco it is only one month, longer-term prediction skill is just above the critical level.},
  language  = {en}
}
@article{HansenMeyerFerrarietal.2017,
  author    = {Hansen, Bjoern Oest and Meyer, Etienne H. and Ferrari, Camilla and Vaid, Neha and Movahedi, Sara and Vandepoele, Klaas and Nikoloski, Zoran and Mutwil, Marek},
  title     = {Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana},
  series = {New phytologist : international journal of plant science},
  volume    = {217},
  journal   = {New phytologist : international journal of plant science},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0028-646X},
  doi       = {10.1111/nph.14921},
  pages     = {1521 -- 1534},
  year      = {2017},
  abstract  = {Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists.},
  language  = {en}
}