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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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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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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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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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A. Birol Kara, Alan J. Wallcraft, and Harley E. Hurlburt

assessment of regional variations in performance. The model–data comparisons were made using both global ocean climatologies and climatologies calculated from long time series at particular locations. For a quantitative evaluation of the model performance, several statistical measures, such as mean error (ME), root-mean-square (rms) difference (RMS), correlation coefficient ( R ), and skill score (SS), were used. Using these measures, time series of monthly mean SST and MLD values from the NLOM were

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Steven R. Felker, Brian LaCasse, J. Scott Tyo, and Elizabeth A. Ritchie

performance. Using only spatial patterns, the system was able to correctly predict the post-ET outcomes of 60%–70% of storms between 12 and 24 h prior to ET. Spatiotemporal techniques allowed for peak performance ~82% using fields from 12 and 24 h before ET in combination. The full-physics NOGAPS model predictions with initialization times of 24 h prior to the ET time were assessed, and it was found that the general outcome of ET was correctly predicted approximately 84% of the time. However, the timing

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Guoqi Han and Yu Shi

, were fed into the model to obtain the predicted sea levels at Argentia. The predicted values were compared with actual water-level measurements to evaluate the model accuracy and stability ( Fig. 8 ). Even with the short-term training data, the NN results are better than those from the harmonic analysis method. The validation results in terms of RMSE and correlation coefficients are listed in Table 6 . The performance of the model with respect to the RMSE and correlation coefficients is acceptable

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S. Baidar, S. C. Tucker, M. Beaubien, and R. M. Hardesty

speed and direction retrieved by combining data from the two looks were also compared with the dropsonde measurements. In-flight instrument performance was evaluated using instrument performance model simulations. Simulations of the satelliteborne OAWL instruments were also performed and evaluated against the GrOAWL airborne performance to demonstrate the feasibility for space-based OAWL wind measurements. A follow-up paper will describe the ground-based study focused on verifying instrument

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Val R. Swail and Andrew T. Cox

1. Introduction The objective of this paper is to present an evaluation of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (NRA) surface marine wind fields ( Kalnay et al. 1996 ), in particular as the forcing of a third-generation ocean wave model adapted to the North Atlantic Ocean (NA) on a high-resolution grid. This evaluation is a part of a larger study to produce a high quality, homogeneous, long-term wind and wave

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