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Jessica Keune
,
Christian Ohlwein
, and
Andreas Hense

Abstract

Ensemble weather forecasting has been operational for two decades now. However, the related uncertainty analysis in terms of probabilistic postprocessing still focuses on single variables, grid points, or stations. Inevitable dependencies in space and time and between variables are often ignored. To address this problem, two probabilistic postprocessing methods are presented, which are multivariate versions of Gaussian fit and kernel dressing, respectively. The multivariate case requires the estimation of a full rank, invertible covariance matrix. For this purpose, a Graphical Least Absolute Shrinkage and Selection Operators (GLASSO) estimator has been employed that is based on sparse undirected graphical models regularized by an L1 penalty term in order to parameterize the full rank inverse covariance. In all cases, the result is a multidimensional probability density. The forecasts used to test the approach are station forecasts of 2-m temperature and surface pressure from four main global ensemble prediction systems (EPS) with medium-range weather forecasts: the NCEP Global Ensemble Forecast System (GEFS), the Met Office Global and Regional Ensemble Prediction System (MOGREPS), the Canadian Meteorological Centre (CMC) Global Ensemble Prediction System (GEPS), and the ECMWF EPS. To evaluate the multivariate probabilistic postprocessing, especially the uncertainty estimates, common verification methods such as the analysis rank histogram and the continuous ranked probability score (CRPS) are applied. Furthermore, a multivariate extension of the CRPS, the energy score, allows for the verification of a complete medium-range forecast as well as for determining its predictability. It is shown that the predictability is similar for all of the examined ensemble prediction systems, whereas the GLASSO proved to be a useful tool for calibrating the commonly observed underdispersion of ensemble forecasts during the first few lead days by using information from the full covariance matrix.

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Clemens Simmer
,
Gerhard Adrian
,
Sarah Jones
,
Volkmar Wirth
,
Martin Göber
,
Cathy Hohenegger
,
Tijana Janjic´
,
Jan Keller
,
Christian Ohlwein
,
Axel Seifert
,
Silke Trömel
,
Thorsten Ulbrich
,
Kathrin Wapler
,
Martin Weissmann
,
Julia Keller
,
Matthieu Masbou
,
Stefanie Meilinger
,
Nicole Riß
,
Annika Schomburg
,
Arnd Vormann
, and
Christa Weingärtner

Abstract

In 2011, the German Federal Ministry of Transport, Building and Urban Development laid the foundation of the Hans-Ertel Centre for Weather Research [Hans-Ertel-Zentrum für Wetterforschung (HErZ)] in order to better connect fundamental meteorological research and teaching at German universities and atmospheric research centers with the needs of the German national weather service Deutscher Wetterdienst (DWD). The concept for HErZ was developed by DWD and its scientific advisory board with input from the entire German meteorological community. It foresees core research funding of about €2,000,000 yr−1 over a 12-yr period, during which time permanent research groups must be established and DWD subjects strengthened in the university curriculum. Five priority research areas were identified: atmospheric dynamics and predictability, data assimilation, model development, climate monitoring and diagnostics, and the optimal use of information from weather forecasting and climate monitoring for the benefit of society. Following an open call, five groups were selected for funding for the first 4-yr phase by an international review panel. A dual project leadership with one leader employed by the academic institute and the other by DWD ensures that research and teaching in HErZ is attuned to DWD needs and priorities, fosters a close collaboration with DWD, and facilitates the transfer of fundamental research into operations. In this article, we describe the rationale behind HErZ and the road to its establishment, present some scientific highlights from the initial five research groups, and discuss the merits and future development of this new concept to better link academic research with the needs and challenges of a national weather service.

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