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Lei Zhu, Qilin Wan, Xinyong Shen, Zhiyong Meng, Fuqing Zhang, Yonghui Weng, Jason Sippel, Yudong Gao, Yunji Zhang, and Jian Yue

ensemble forecasts initiated with the EnKF analyses and perturbations. Section 2 introduces the numerical modeling system, the EnKF technique, the processing of the observations to be assimilated, and the experiment setup. Section 3 presents EnKF analyses of Vicente in terms of track, minimum SLP, and the TC 3D structure. Section 4 shows the comparison among observations, experiments without data assimilation, and forecasts initialized with EnKF analyses. The results of sensitivity analyses using

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Stacey M. Hitchcock, Michael C. Coniglio, and Kent H. Knopfmeier

1. Introduction Convection-permitting numerical weather prediction (NWP) models have proven to be useful to forecasters tasked with alerting the public of the threat for severe weather (e.g., Kain et al. 2006 ; Clark et al. 2012 ). Part of the challenge of predicting convective weather in the short-term (0–9 h) using NWP models is the accurate analysis of ongoing storms in the initial conditions, for which the assimilation of radar data is essential (e.g., Dawson et al. 2012 ; Stratman et

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Sho Yokota, Hiromu Seko, Masaru Kunii, Hiroshi Yamauchi, and Hiroshi Niino

accurately the relationship between LMCs and the surrounding environment; such a sensitivity analysis can be accomplished by a “warn-on-forecast” approach ( Stensrud et al. 2009 , 2013 ; Cintineo and Stensrud 2013 ), that is, ensemble forecasts of storm features made by assimilating dense surface and radar observations around the tornadoes. Surface meteorological data directly capture the dynamic and thermodynamic characteristics of the planetary boundary layer, and these characteristics are closely

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Heiner Lange and Tijana Janjić

( Zhang et al. 2007 ; Selz and Craig 2015 ). It is planned to combine the present setup with the assimilation of observation sets with even higher resolutions, such as radial winds and reflectivity of convective systems from Doppler radar, and to verify their influences across the observation spaces [e.g., by using the techniques of Sommer and Weissmann (2014) ]. Forecasts with longer lead times than 3 hours should be performed to evaluate if the Mode-S EHS benefit is persistent. A rigorous survey

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Kozo Okamoto, Kazumasa Aonashi, Takuji Kubota, and Tomoko Tashima

have developed an assimilation technique for the reflectivity of space-based precipitation radars by using a regional CRM and data assimilation system that can explicitly handle cloud variables. The observations we mainly targeted were the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory . The DPR observability was enhanced relative to the PR with respect to better sensitivity, double frequencies, and higher vertical resolution. We

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Yudong Tian, Grey S. Nearing, Christa D. Peters-Lidard, Kenneth W. Harrison, and Ling Tang

1. Examples of conventional performance metrics.* The observations and forecasts are denoted as x and y , respectively. Among them, the “big three”—bias, MSE, and CC—are the most widely used in diverse disciplines, exemplified by the popular “Taylor diagram” ( Taylor 2001 ). These metrics do, however, have several limitations: Interdependence. Most of these conventional performance metrics are not independent; they have been demonstrated to relate to each other in complex ways. For example

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Jean-Jacques Morcrette, George Mozdzynski, and Martin Leutbecher

this radiation burden, radiation transfer is only computed every few model hours. For example, with full radiation computations performed every 2 h at all grid points, radiation transfer accounts for 27% of the run time of the “GME” forecast model ( Majewski et al. 2002 ). The recent introduction of the McRad package for radiation computations ( Morcrette et al. 2008 ) in the Integrated Forecasting System (IFS) has increased the cost of the radiation computations and required revisiting the use of

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Stephan Rasp and Sebastian Lerch

the mean value and standard deviation of the 2-m temperature forecasts. 3. Benchmark postprocessing techniques a. Ensemble model output statistics Within the general EMOS framework proposed by Gneiting et al. (2005) , the conditional distribution of the weather variable of interest, , given ensemble predictions , is modeled by a single parametric forecast distribution with parameters : The parameters vary over space and time, and depend on the ensemble predictions through suitable link

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Robin J. T. Weber, Alberto Carrassi, and Francisco J. Doblas-Reyes

caused by the displacement of the model state onto the observed values lying outside the model attractor. At the expense of larger initial errors, the objective of AI is to keep the initial state close to the model attractor and reduce the drift. The mean forecast error is less dependent on lead time and, as argued by Magnusson et al. (2013) , the use of standard a posteriori bias correction techniques is more robust. Anomaly initialization can reduce initialization shocks, but is unable to avoid

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Camille Marini, Iuliia Polkova, Armin Köhl, and Detlef Stammer

, which only relay on changes in external forcing ( Smith et al. 2007 ; Pohlmann et al. 2009 ; van Oldenborgh et al. 2012 ; Boer et al. 2013 ). The extra skill in the initialized forecasts is believed to come from climate modes of natural climate variability, which, when properly initialized, carry predictive skill on decadal time scales. Candidate modes of internal climate variability that could contribute to predictive skill encompass Pacific decadal variability and Atlantic multidecadal

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