title = {Hourly Probabilistic Snow Forecasts over Complex Terrain: A Hybrid Ensemble Postprocessing Approach},
  author = {Reto Stauffer and Georg J. Mayr and Jakob W. Messner and Achim Zeileis},
  journal = {Advances in Statistical Climatoloy, Meteorology and Oceanography},
  year = {2018},
  volume = {4},
  number = {1/2},
  pages = {65--86},
  doi = {10.5194/ascmo-4-65-2018},
  abstract = {Accurate and high-resolution snowfall and fresh snow forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. In this article a new hybrid statistical postprocessing method is proposed, which combines standardized anomaly model output statistics (SAMOS) with ensemble copula coupling (ECC) and a novel re-weighting scheme to produce spatially and temporally high-resolution probabilistic snow forecasts. Ensemble forecasts and hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) serve as input for the statistical postprocessing method, while measurements from two different networks provide the required observations.  This new approach is applied to a region with very complex topography in the eastern European Alps. The results demonstrate that the new hybrid method allows one not only to provide reliable high-resolution forecasts, but also to combine different data sources with different temporal resolutions to create hourly probabilistic and physically consistent predictions.}
  author = {Reto Stauffer and Nikolaus Umlauf and Jakob W. Messner and Georg J. Mayr and Achim Zeileis},
  title = {Ensemble Postprocessing of Daily Precipitation Sums over Complex Terrain Using Censored High-Resolution Standardized Anomalies},
  journal = {Monthly Weather Review},
  volume = {145},
  number = {3},
  pages = {955-969},
  year = {2017},
  doi = {10.1175/MWR-D-16-0260.1},
  abstract = {Probabilistic forecasts provided by numerical ensemble prediction systems have systematic errors and are typically underdispersive. This is especially true over complex topography with extensive terrain-induced small-scale effects, which cannot be resolved by the ensemble system. To alleviate these errors, statistical postprocessing methods are often applied to calibrate the forecasts. This article presents a new full-distributional spatial postprocessing method for daily precipitation sums based on the standardized anomaly model output statistics (SAMOS) approach. Observations and forecasts are transformed into standardized anomalies by subtracting the long-term climatological mean and dividing by the climatological standard deviation. This removes all site-specific characteristics from the data and makes it possible to fit one single regression model for all stations at once. As the model does not depend on the station locations, it directly allows the creation of probabilistic forecasts for any arbitrary location. SAMOS uses a left-censored power-transformed logistic response distribution to account for the large fraction of zero observations (dry days), the limitation to nonnegative values, and the positive skewness of the data. ECMWF reforecasts are used for model training and to correct the ECMWF ensemble forecasts with the big advantage that SAMOS does not require an extensive archive of past ensemble forecasts as only the most recent four reforecasts are needed, and it automatically adapts to changes in the ECMWF ensemble model. The application of the new method to the central Alps shows that the new method is able to depict the small-scale properties and returns accurate fully probabilistic spatial forecasts.}
  author = {Reto Stauffer and Georg J. Mayr and Jakob W. Messner and Nikolaus Umlauf and Achim Zeileis},
  title = {Spatio-Temporal Precipitation Climatology Over Complex Terrain Using a Censored Additive Regression Model},
  journal = {International Journal of Climatology},
  volume = {37},
  number = {7},
  publisher = {John Wiley \& Sons, Ltd},
  issn = {1097-0088},
  doi = {10.1002/joc.4913},
  pages = {3264--3275},
  keywords = {climatology, precipitation, complex terrain, GAMLSS, censoring, daily resolution},
  year = {2017},
  abstract = {Flexible spatio-temporal models are widely used to create reliable and accurate estimates for precipitation climatologies. Most models are based on square root transformed monthly or annual means, where a normal distribution seems to be appropriate. This assumption becomes invalid on a daily time scale as the observations involve large fractions of zero observations and are limited to non-negative values.We develop a novel spatio-temporal model to estimate the full climatological distribution of precipitation on a daily time scale over complex terrain using a left-censored normal distribution. The results demonstrate that the new method is able to account for the non-normal distribution and the large fraction of zero observations. The new climatology provides the full climatological distribution on a very high spatial and temporal resolution, and is competitive with, or even outperforms existing methods, even for arbitrary locations.}
  author = {Reto Stauffer and Georg J. Mayr and Markus Dabernig and Achim Zeileis},
  title = {Somewhere Over the Rainbow: How to Make Effective Use of Colors in Meteorological Visualizations},
  journal = {Bulletin of the American Meteorological Society},
  volume = {96},
  number = {2},
  pages = {203-216},
  year = {2015},
  doi = {10.1175/BAMS-D-13-00155.1},
  abstract = {Results of many atmospheric science applications are processed graphically. Visualizations are a powerful tool to display and communicate data. However, to create effective figures, a wide scope of challenges has to be considered. Therefore, this paper offers several guidelines with a focus on colors. Colors are often used to add additional information or to code information. Colors should (i) allow humans to process the information rapidly, (ii) guide the reader to the most important information, and (iii) represent the data appropriately without misleading distortion. The second and third requirements necessitate tailoring the visualization and the use of colors to the specific purpose of the graphic. A standard way of deriving color palettes is via transitions through a particular color space. Most of the common software packages still provide default palettes derived in the red–green–blue (RGB) color model or “simple” transformations thereof. Confounding perceptual properties such as hue and brightness make RGB-based palettes more prone to misinterpretation. Switching to a color model corresponding to the perceptual dimensions of human color vision avoids these problems. The authors show several practically relevant examples using one such model, the hue–chroma–luminance (HCL) color model, to explain how it works and what its advantages are. Moreover, the paper contains several tips on how to easily integrate this knowledge into software commonly used by the community. The guidelines and examples should help readers to switch over to the alternative HCL color model, which will result in a greatly improved quality and readability of visualized atmospheric science data for research, teaching, and communication of results to society. }
  author = {Susanne Drechsel and Georg J. Mayr and Jakob W. Messner and Reto Stauffer},
  title = {Wind Speeds at Heights Crucial for Wind Energy: Measurements and Verification of Forecasts},
  journal = {Journal of Applied Meteorology and Climatology},
  volume = {51},
  number = {9},
  pages = {1602-1617},
  year = {2012},
  doi = {10.1175/JAMC-D-11-0247.1},
  abstract = {Wind speed measurements from one year from meteorological towers and wind turbines at heights between 20 and 250 m for various European sites are analyzed and are compared with operational short-term forecasts of the global ECMWF model. The measurement sites encompass a variety of terrain: offshore, coastal, flat, hilly, and mountainous regions, with low and high vegetation and also urban influences. The strongly differing site characteristics modulate the relative contribution of synoptic-scale and smaller-scale forcing to local wind conditions and thus the performance of the NWP model. The goal of this study was to determine the best-verifying model wind among various standard wind outputs and interpolation methods as well as to reveal its skill relative to the different site characteristics. Highest skill is reached by wind from a neighboring model level, as well as by linearly interpolated wind from neighboring model levels, whereas the frequently applied 10-m wind logarithmically extrapolated to higher elevations yields the largest errors. The logarithmically extrapolated 100-m model wind reaches the best compromise between availability and low cost for data even when the vertical resolution of the model changes. It is a good choice as input for further statistical postprocessing. The amplitude of measured, height-dependent diurnal variations is underestimated by the model. At low levels, the model wind speed is smaller than observed during the day and is higher during the night. At higher elevations, the opposite is the case. }