@misc{73778,
  author = {D. Bouniol and A. Protat and J. Delanoë and J. Pelon and D.P. Donovan and J.-M. Piriou and F. Bouyssel and A.M. Tompkins and D. Wilson and Y. Morille and M. Haeffelin and E.J. O'Connor and Robin Hogan and A.J. Illingworth},
  title = {Evaluating cloud occurrence in the ECMWF integrated forecast system and three other operational forecasts models using long-term ground-based radar and lidar measurements.},
  abstract = {This paper assesses the ability of four operational weather forecast models (ECMWF, ARPEGE, RACMO and Met Office) to generate a cloud at the right location and time (i.e. the cloud occurrence) using a two year time series of observations collected by profiling ground-based active sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, NL, Chilbolton, UK, Palaiseau, F.). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. It is shown that all models tend to overestimate the occurrence of high-level clouds, while for lower levels the errors are contrasting. The data set is then divided into seasons to evaluate the potential of the models to generate different cloud situations. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as between the seasons of a given year. Overall, the model biases observed using the whole data set are still found at seasonal scale, but the models generally manage to capture the seasonal variation in cloud occurrence well. This study demonstrates the usefulness of long time series in order to monitor and guide changes to a given parametrisation.},
  year = {2007},
  journal = {ECMWF Technical Memoranda},
  number = {526},
  pages = {16},
  month = {12/2007},
  publisher = {ECMWF},
  url = {https://www.ecmwf.int/node/8331},
  doi = {10.21957/wv8w1sryh},
  language = {eng},
}