CapsuleState-of-the-Art statistical postprocessing techniques for ensemble forecasts are reviewed, together with the challenges posed by a demand for timely, high-resolution and reliable probabilistic information. Possible research avenues are also discussed.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Markus DabernigUniversität Innsbruck Achim ZeileisUniversität Innsbruck AbstractResults of many atmospheric science applications are processed graphically using colors to encode certain parts of the information. Colors should (1) allow humans to process more information, (2) guide the viewer to the most important information, (3) represent the data appropriately without misleading distortion, and (4) be appealing. The second requirement necessitates tailoring the visualization and the use of color to the viewer for whom the graphics is intended. A standard way of deriving color palettes is via transitions trough a certain color space. Most of the common software packages still provide palettes derived in the RGB color model or "simple" transformations thereof as default. Confounding perceptual properties such as hue and brightness make RGB-based palettes more prone to misinterpretation. Additionally, they are often highly saturated, which makes looking at them for a longer period strenuous. Switching to a color model corresponding to the perceptual dimensions of human color vision avoids these problems. We show several practically relevant examples using such a model, the 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, which should help readers to switch over to the new concept. The switch will result in a greatly improved quality and readability of visualized atmospheric science data for research, teaching, and communication of results to society.

To post-process ensemble predictions for a particular location, statistical methods are often used, especially in complex terrain such as the Alps. When expanded to several stations, the post-processing has to be repeated at every station individually, thus losing information about spatial coherence and increasing computational cost. Therefore, the ensemble post-processing is modified and applied simultaneously at multiple locations. We transform observations and predictions to standardized anomalies. Seasonal and sitespecific characteristics are eliminated by subtracting a climatological mean and dividing by the climatological standard deviation from both observations and numerical forecasts. This method allows us to forecast even at locations where no observations are available. The skill of these forecasts is comparable to forecasts post-processed individually at every station and is even better on average.

Separate statistical models are typically fit for each forecasting lead time to postprocess numerical weather prediction (NWP) ensemble forecasts. Using standardized anomalies of both NWP values and observations eliminates most of the lead-time-specific characteristics so that several lead times can be forecast simultaneously. Standardized anomalies are formed by subtracting a climatological mean and dividing by the climatological standard deviation. Simultaneously postprocessing forecasts between +12 and +120 h increases forecast coherence between lead times, yields a temporal resolution as high as the observation interval (e.g., up to 10 min), and speeds up computation times while achieving a forecast skill comparable to the conventional method.

Abstract. Statistical Postprocessing of medium-range weather forecasts is an important component of modern forecasting systems. Since the beginning of modern data science, numerous new postprocessing methods have been proposed, complementing an already very diverse field. However, one of the questions that frequently arises when considering different methods in the framework of implementing operational postprocessing is the relative performance of the methods for a given specific task. It is particularly challenging to find or construct a common comprehensive dataset that can be used to perform such comparisons. Here, we introduce the first version of EUPPBench, a dataset of time-aligned forecasts and observations, with the aim to facilitate and standardize this process. This dataset is publicly available at https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark. We provide examples on how to download and use the data, propose a set of evaluation methods, and perform a first benchmark of several methods for the correction of 2-meter temperature forecasts.