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Tobias Selz, Lucas Fischer, and George C. Craig

atmosphere’s scale-dependence behavior appropriately, shortcomings in the numerics or parameterizations are likely. In the case of kinetic energy, the evaluation of scaling exponents has provided valuable insights into model performance ( Skamarock 2004 ; Hamilton et al. 2008 ; Bierdel et al. 2012 ; Fang and Kuo 2015 ). For water vapor, Schemann et al. (2013) investigated the scaling behaviors of a GCM, an NWP model, and a large-eddy simulation (LES) and the implication for cloud parameterizations

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Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

). Conventional observations such as surface stations and weather balloons are scarce at low latitudes, particularly over the vast tropical oceans. Consequently, the observing system is dominated by satellite data, which are heavily skewed toward measuring atmospheric mass variables rather than wind (e.g., Baker et al. 2014 ). However, data denial experiments for periods with a much enhanced radiosonde network during field campaigns over West Africa have shown a relatively small impact on model performance

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Stephan Rasp and Sebastian Lerch

boosting. However, initial tests indicated slightly worse predictive performance; we thus focus on maximum likelihood-based methods instead. 7 To account for the intertwined choice of scoring rules for model estimation and evaluation ( Gebetsberger et al. 2017 ), we have also evaluated the models using LogS. However, as the results are very similar to those reported here and computation of LogS for the raw ensemble and QRF forecasts is problematic ( Krüger et al. 2016 ), we focus on CRPS

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Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part I: Predictor Selection and Logistic Regression Model

Julian F. Quinting and Christian M. Grams

regression and the limitations of this approach. In section 4 we evaluate the performance of the models during Northern Hemisphere winter and demonstrate their applicability to an operational ECMWF ensemble forecast of a WCB event during January 2011. The study ends with concluding remarks and an outlook in section 5 . 2. Data a. Predictor dataset The predictor selection as well as the development and evaluation of the logistic regression models is based on ECMWF’s interim reanalysis data (ERA

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Kirstin Kober and George C. Craig

the perturbation method is applicable in any atmospheric model that allows for calculation of the relevant physical process information. The observational data used to evaluate the forecasts and the selected case studies in which the parameterization is tested will be introduced briefly as well as the analysis strategy for the suggested method. a. Physically based stochastic perturbations in the boundary layer We propose a concept of process-based model error representation in terms of a

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Joël Arnault, Thomas Rummler, Florian Baur, Sebastian Lerch, Sven Wagner, Benjamin Fersch, Zhenyu Zhang, Noah Kerandi, Christian Keil, and Harald Kunstmann

ENS is computed in a similar manner as ( section 2f ) ensures that both quantities are representative of the same environment. Parameter H ENS is finally evaluated in millimeters per day per kilometer, as E is computed as a water flux rate in millimeters per day [see section 2a , Eq. (2) ] and the grid spacing for the spatial derivate is provided in kilometers. 3. Results a. Model validation In this section the performance of the ensemble and ensemble subsets ( section 2b ) in reproducing

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Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

of the form with nonnegative weights , , and that sum to 1, and reflects the members’ performance during the training period. 4 Each of the component distributions, , , and , contains a point mass at zero and a density for positive accumulations. The point mass at zero specifies the probability of no precipitation and is estimated in a logistic regression model, where the cube root of the member forecast and a binary indicator of the member forecast being zero are used as predictor

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Mirjam Hirt, Stephan Rasp, Ulrich Blahak, and George C. Craig

that scales to γ = 1/2 km −1 for nondamped waves and to max ⁡ ( γ 0 , − m ) for damped waves. Mathematically, this can be expressed as (7) w ′ = w 0   f ⁡ ( z ) , (8) where f ⁡ ( z ) = { e − γ ⁡ ( z − z max ) , if z ≥ z max 1 z max z , if z < z max , (9) with γ = { γ 0 ω 2 ≤ N 2 ⁡ ( nondamped ) max ⁡ ( γ 0 , − m ) ω 2 ≥ N 2 ⁡ ( damped ) . Further details are described in appendix A . 3. Model simulations, observations, and simulation period To evaluate the impact of the PSP variants and the

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Kevin Bachmann, Christian Keil, George C. Craig, Martin Weissmann, and Christian A. Welzbacher

of the prevailing synoptic-scale weather regime in combination with orography? The outline of the article is as follows: section 2 describes the ensemble data assimilation and forecasting systems, the setup, and the observations. Section 3 briefly introduces measures and scores used to evaluate the experiments. Section 4 presents the results with a focus on predictable scales in NWP model configurations with different levels of realism. Concluding remarks and a comparison to previous

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Paolo Ghinassi, Georgios Fragkoulidis, and Volkmar Wirth

bust” for the majority of the operational forecast models, showing a huge drop in the medium-range forecast skill over Europe ( Rodwell et al. 2013 ). The authors associated this poor performance to the misrepresentation of moist convective processes over North America a few days earlier, and this error was subsequently communicated downstream embedded in a RWP. Data are retrieved from the ERA-Interim reanalyses ( Dee et al. 2011 ) with a horizontal resolution of 2° × 2° on 20 pressure levels

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