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Dmitri N. Moisseev, Cuong M. Nguyen, and V. Chandrasekar

pulse pair and PTDM can be significantly different. To minimize the difference one can introduce a second precipitation echo into the model. To detect cases where the assumption of a Gaussian-shaped precipitation spectrum fails, one can use a combination of a normalized trace: and the R 2 goodness of fit parameter based on the imaginary part of the autocovariance function (5) ( Nguyen et al. 2008 ): where 〈·〉 denotes a mean value. The goodness of fit parameters are evaluated at the

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Florian Le Guillou, Sammy Metref, Emmanuel Cosme, Clément Ubelmann, Maxime Ballarotta, Julien Le Sommer, and Jacques Verron

height (SSH) from realistic altimetric observations in the context of the future SWOT mission. The proposed mapping technique uses a 1.5-layer QG model forced by observations of SSH. The model is gradually nudged toward the observations while running forward and backward within a temporal window in an iterative process so as to overcome the problem of time distribution of the observations. The performance of this mapping technique, dubbed the BFN-QG, has been evaluated and compared with the standard

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Micheal Hicks, Belay Demoz, Kevin Vermeesch, and Dennis Atkinson

Ceilometer Proof of Concept Project was to show the capabilities of ceilometers beyond detecting sky conditions. This paper utilizes the dataset to evaluate the performance of an aerosol-based ML height detection algorithm, in anticipation of ceilometer backscatter data at AWSs being made available for user consumption in the near future. Hicks et al. (2015) used a consortium of automated aerosol-based detection methods [i.e., hybrid, hybrid-lowest (H-L), 1D Haar wavelet covariance transform (WCT), and

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Xin Zhang, Xiang-Yu Huang, Jianyu Liu, Jonathan Poterjoy, Yonghui Weng, Fuqing Zhang, and Hongli Wang

this paper. b. Computational performance and scientific impact A series of experiments are carried out to evaluate the computational performance and scientific impact of applying multi-incremental 4D-Var for model initialization. Two configurations with three inner loops are tested. One uses the full-resolution configuration in which the outer and inner loops use the same resolution (15, 15, and 15 km), and the other uses the multi-incremental configuration with a 15-km outer loop and three inner

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Maxime Ballarotta, Clément Ubelmann, Marine Rogé, Florent Fournier, Yannice Faugère, Gérald Dibarboure, Rosemary Morrow, and Nicolat Picot

specifically for this study, keeping one altimeter mission (here CryoSat-2 ) out to allow an independent assessment of the mapping performance. These maps are constructed from a LOI method ( Bretherton et al. 1976 ; Traon et al. 1998 ) based on the a priori statistical knowledge of the SSH fields (e.g., covariance model, correlation length scale, SSH variance, noise level). The LOI approach used for the DUACS-DT2018 processing is extensively described in section 2.2.6 of Pujol et al. (2016) as well as

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Matthieu Boquet, Philippe Royer, Jean-Pierre Cariou, Mehdi Machta, and Matthieu Valla

( Clifton et al. 2013 ) and for evaluating the performance of wind turbine generators ( Wagner 2010 ; Fleming et al. 2014 ). The success of these applications is largely dependent upon the data quality and availability of the CWDL, as poor measurement accuracy and low data availability will bias the results because critical atmospheric features may be missed and thus not be taken into account in the performed analysis. CWDL data availability in the context of wind energy applications has already been

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Peter C. Chu, Shihua Lu, and Yuchun Chen

and ψ o are the variables from model and observation, respectively. We may take the probability histogram of Δ ψ as the error distribution. b. Basic statistical parameters Three parameters: Bias, mean-square error (mse), and root-mean-square error (rmse), are commonly used for evaluation of the model performance. Here N is the total number of horizontal points, and [ ψ m ( z, t ), ψ o ( z, t )] and [σ m ( z, t ), σ o ( z, t )] are horizontal means and standard deviations of the

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Dinesh Manian, James M. Kaihatu, and Emily M. Zechman

section 2 , which compares different approaches to the sampling problem. Computer-generated random survey paths are evaluated for their ability to capture the areas critical to the model function, and a genetic algorithm is developed and applied to evolve the fittest possible path. The sampled bathymetry along this path is interpolated to the model grid using inverse distance-weighted triangular interpolation. The fitness is ascertained by evaluating the differences between the model results produced

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Detlef Stammer and Christian Dieterich

-resolving, state-of-the-art ocean general circulation model of the North Atlantic, we demonstrate the ability to recover the “true” velocity field from SSH data—measured instantaneously along the parallel tracks—and to evaluate associated errors. Moreover, the purpose of this note is also to determine the sensitivity of the system’s performance to the lateral separation δλ of the pair of parallel tracks and to identify a track separation that is optimal for studies of the mesoscale eddy field. 2. Methodology

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Linghui Cai, Shaoping Shang, Guomei Wei, Zhigang He, Yanshuang Xie, Ke Liu, Tao Zhou, Jinquan Chen, Feng Zhang, and Yan Li

distribution of the accuracy of HF radar-derived waves. In addition, some studies have evaluated waves deduced by radar based on in situ data and model simulation results. For example, Wyatt et al. (2003) undertook two experiments to test and demonstrate the performance of a radar system on the coasts of Norway and Spain, and they reported the results of qualitative and quantitative intercomparison of wave measurements and wave model products at selected points. Hisaki (2014) analyzed the consistency

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