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Glen S. Romine, Craig S. Schwartz, Ryan D. Torn, and Morris L. Weisman

model weather prediction with 1–3 days of lead time. They used a variety of techniques to identify source regions of initial condition uncertainty that had the potential to lead to rapid forecast error growth and were suitable for targeted sampling. After observation collection, impact studies (e.g., Baker and Daley 2000 ; Langland and Baker 2004 ) assess changes in initial condition uncertainty and forecast error owing to the assimilation of particular observation sets (e.g., Ancell and Hakim

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Michail Diamantakis and Linus Magnusson

1. Introduction The semi-implicit, semi-Lagrangian (SISL) technique is a well-established method for solving the governing equations of global atmospheric NWP models. It is a very efficient technique as it offers unconditional stability without loss of accuracy (small dispersion errors) permitting integrations with long time steps ( Staniforth and Côté 1991 ). The ECMWF Integrated Forecast System (IFS) model employs a two-time-level SISL scheme ( Temperton et al. 2001 ) combined with a spectral

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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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