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Zheng Guo, Haidong Pan, Wei Fan, and Xianqing Lv

according to Eq. (8) . Repeat the procedure with BFCs being optimized until certain criteria are met. The performance of SSI or CI in combination with the adjoint model is evaluated by the following statistics ( Zhong et al. 2010 ): the root-mean-square (RMS) error The relative average error E computed according to where x is the tidal amplitude or phase, or BFC in the twin experiments because the “true” BFC field is known; is the average over space; and subscripts “mod” and “obs” denote the

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Kenneth G. Hubbard and Jinsheng You

1. Introduction The spatial regression test (SRT) method has been found to be superior to the inverse distance weighting (IDW) method (You et al. 2004, manuscript submitted to J. Atmos. Oceanic Technol. , hereafter YHG) when applied to provide estimates for the maximum air temperature ( T max ) and the minimum air temperature ( T min ) in the Applied Climate Information System (ACIS). However, the sensitivity of the performance of both methods to the input parameters has not been evaluated

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Dongsik Chang, Fumin Zhang, and Catherine R. Edwards

flow for the time in the middle of the th subsurface phase, rather than for the time of the surfacing event. Hence, we set the time stamp of each flow estimate to be the middle of the preceding subsurface phase interval. Given a series of low-frequency flow estimates, we linearly interpolate low-frequency flow for a given time and reconstruct ocean currents by adding the low-frequency flow to ADCIRC tidal flow. b. Navigation performance using predictive ocean models The qualitative evaluation of

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Yuxin Zhao, Dequan Yang, Wei Li, Chang Liu, Xiong Deng, Rixu Hao, and Zhongjie He

performance. As discussed above, the amount of historical data affects the forecast performance. Accordingly, data from 1958 to 2014 (57 years of historical data in total) are used in the STEOF decomposition. We estimate the fitting coefficients and evaluate the forecast performance based on the 2015 data. We design experiments to explore the predictability of the forecast model with different fitting periods. Taking the 30-day forecast as an example, data from different periods are used for the fitting

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Chandrasekar Radhakrishnan and V. Chandrasekar

WRF Model resolution (1 km) for the evaluation using the ESMF regrid function. To evaluate the performance of the forecast, the critical success index (CSI), probability of detection (POD), equitable threat score (ETS), and mean absolute error (MAE) skill scores were used. Equations (1) – (4) show the mathematical formulation of these skill scores: (1) CSI = n 11 n 11 + n 10 + n 01 , (2) POD = n 11 n 11 + n 01 , (3) ETS = n 11 − C 1 n 11 + n 10 + n 01 − C 1 , C 1 = ⁡ ( n 11 + n 10 ) ⁡ ( n 11 + n

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Magnus Hieronymus, Jenny Hieronymus, and Fredrik Hieronymus

whereas the linear model has to be presented with transformed inputs to handle nonlinearity. The performance difference between these methods can therefore be seen as a measure of the nonlinearity of the problem. A detailed introduction to the different ANNs used, as well as to the data, is found in the data and methods section, which is followed by a results section and a conclusions section. 2. Data and methods a. Data Figure 1 shows the location of the nine tide gauge stations used in this

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Evan Ruzanski, V. Chandrasekar, and Yanting Wang

2002 ) simulation techniques where the mean field of the resulting posterior distribution is taken to be the nowcast and the standard deviation field is the measure of forecast uncertainty. Physical characteristics of precipitation patterns are modeled as parameters for which a range is preselected based on meteorological expertise. Although each level of the Bayesian hierarchical model can be parameterized, computational complexity of such models is high and a comprehensive evaluation of such

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John Lillibridge, Remko Scharroo, Saleh Abdalla, and Doug Vandemark

assesses the performance of these one- and two-dimensional wind speed models using ocean buoy data and then summarizes our results. 2. A physically based attenuation model at Ka band Atmospheric attenuation of the radar signal is more pronounced at Ka band than at Ku band. Consideration must be given to three components: attenuation due to oxygen molecules (dry atmosphere), water vapor molecules (wet atmosphere), and water droplets/rain (cloud liquid water). To compute Ka-band attenuation, we exploit

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Kimberly L. Elmore, Pamela L. Heinselman, and David J. Stensrud

assembled from the available observations, a regression model is fit and the prediction error of the model is calculated. The process of assembling a bootstrap replicate and then fitting the regression model to it is repeated 5000 times in order to provide good empirical confidence interval estimates for both the prediction error and the regression coefficients. The resulting bootstrap mean prediction error estimates offer a generally more accurate representation of expected model performance than

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Reza Marsooli, Philip M. Orton, George Mellor, Nickitas Georgas, and Alan F. Blumberg

horizontal section followed by a sloping beach with a constant slope of 1:40. The flume bed consisted of fine sand with a median grain diameter of 0.22 mm. A piston-type wave board generated random waves with a significant wave height of 1.41 m and a peak wave period T p of 5.41 s. The still water depth in the horizontal section of the flume was 4.1 m. We use the measured longitudinal profiles of wave height and water surface elevation to evaluate the performance of the coupled sECOM–MDO model. The

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