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Kyoung-Ho Cho, Yan Li, Hui Wang, Kwang-Soon Park, Jin-Yong Choi, Kwang-Il Shin, and Jae-Il Kwon

high-frequency (HF) radar-derived surface currents ( Ullman et al. 2006 ; Abascal et al. 2012 ). It is significant that the performance of a SAR model requires assessment with observed drifter trajectories. Trajectory assessment has been studied in various ways: using a spaghetti diagram ( Toner et al. 2001 ; Nairn and Kawase 2001 ), statistical separation ( Thompson et al. 2003 ), and circle assessment ( Furnans et al. 2005 ). The use of a circle assessment is designed to evaluate how well a

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Jorge L. Salazar-Cerreño, V. Chandrasekar, Jorge M. Trabal, Paul Siquera, Rafael Medina, Eric Knapp, and David J. McLaughlin

model for wet radome with and without rivulet formation, and (c),(d) simulated results of transmission and reflection coefficients. 6. Summary This paper presents a new analytical model developed to evaluate the performance of a wet radome of dual-polarized radar. The model requires a rain DSD model or reflectivity radar data and the geometry of radar radome as input parameters. The model proposed can be used for any radome type. A rectangular tilted unit cell was considered to discretize any radome

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Mark D. Orzech, Fengyan Shi, Jayaram Veeramony, Samuel Bateman, Joseph Calantoni, and James T. Kirby

retreat. Below we summarize the development of the coupled wave–ice system and present results from a series of tests designed to evaluate general system performance. While the coupled system simulates the internal properties of ice, the tests described herein were primarily focused on how the motion of solid ice blocks was affected by interacting with the fluid and how the fluid pressure and fluxes were altered by the floating ice. An overview of the NHWAVE and LIGGGHTS models is presented in

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Xingming Liang and Alexander Ignatov

, and SD). Time series of global daily mean and SD statistics, along with double differences, are used to evaluate the BT and SST biases for cross-platform consistency. Dependencies on the main factors affecting the BT and SST biases—column water vapor content, view zenith angle, wind speed, Reynolds SST, air–sea temperature difference, and latitude—are examined. In addition to conventional statistics (mean and SD) that are indicative of the overall performance of the ACSPO product, robust

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Yong Chen, Yong Han, Paul van Delst, and Fuzhong Weng

1. Introduction The Community Radiative Transfer Model (CRTM) was developed at the Joint Center for Satellite Data Assimilation (JCSDA) ( Weng et al. 2005 ; Han et al. 2006 ; Chen et al. 2008 , 2010 , 2012 ) to simulate the radiances at the top of atmosphere and produce radiance gradients (or Jacobians) for satellite data assimilation and many other remote sensing applications. Since CRTM performs very fast and accurate computations, it has been implemented in the National Centers for

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Wen-Yu Huang, Bin Wang, Yong-Qiang Yu, and Li-Juan Li

the basic performance of the improved version through comparing the results from the standard version and the improved version with the same artificial island that occupies the region from 87.5° to 90°N. In Huang et al. (2014a , hereafter Part II ), we will show the impact on simulated Arctic circulation by a reduced island for the North Pole. Section 2 includes model description, experiment design for performance evaluations, and datasets used for initializing, forcing, and validation. It is

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Wen-Yu Huang, Bin Wang, Li-Juan Li, and Yong-Qiang Yu

evaluated the basic performance of LICOM2_imp ( Huang et al. 2014 ). With the same artificial island (88°–90°N) in the Arctic Ocean as in LICOM2, LICOM2_imp outperforms LICOM2 in simulating the sea surface temperature (SST), sea surface salinity (SSS), the Atlantic meridional overturning circulation (AMOC), and the Antarctic Circumpolar Current. This study is mainly devoted to evaluating the performance of LICOM2_imp in simulating the Arctic circulation. The organization of the paper is as follows. In

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X. Zhuge, X. Zou, F. Weng, and M. Sun

infrared radiance data assimilation, surface-sensitive infrared radiance observations over land are currently excluded from data assimilation systems mainly because of the large uncertainties in land surface emissivity (LSE) input into a fast radiative transfer model. Although the land skin temperature (LST) also has large uncertainties over areas with complicated land surface characteristics ( Zheng et al. 2012 ; Trigo et al. 2015 ; Zhuo et al. 2016 ), it is not strictly required, since it is often

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Elizabeth M. Douglass and Andrea C. Mask

1. Introduction and background With the advancement of ocean modeling and the proliferation of high-resolution models comes a need for standardized methods of evaluating model output and determining its accuracy. Metrics such as mean error, root-mean-square error, normalized bias, or normalized standard deviation present an objective test of whether a model is accurately representing observations (e.g., Kara and Hurlburt 2006 ; Stopa and Cheung 2014 ). These metrics measure the variability

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Chih-Chiang Wei

classification trees (C4.5 and RF) and regression trees (RF). To create categorical-type target labels, the dataset {R} is partitioned into several intervals using C4.5 and RF algorithms ( section 3b ). Step 3: Classify the datasets into training and testing subsets ( section 3b ). The training subset is used to build model structures and parameters, followed by the testing subset that evaluates the performance of the model to verify its generalizability. Step 4: Design the climate scenarios. Scenario 1 uses

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