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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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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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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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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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Eugenio Gorgucci, V. Chandrasekar, and Luca Baldini

%, demonstrating the excellent performance of the SC attenuation-correction procedure. The self-consistent algorithm imposes the internal consistency of the specific attenuation estimates, with respect to variability in DSD as well as variability in raindrop shape model. This section evaluates the sensitivity of the procedure to variability in the drop shape model. While at this point the variability of shape model within a storm is not clearly known, the attenuation-correction process comes up with a best

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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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J. M. Baker, D. C. Reicosky, and D. G. Baker

. BAKERUniversity of Minnesota, St. Paul, Minnesota(Manuscript received 30 October 1987, in final form 21 March 1988) Many models in a variety of disciplines require air temperature throu~,hout the day as an input, yet 6fien theonly data available are daily extreme. Several methods for estimating the diurnal change in temperature fromdaily extreme have been reported. This paper compares the performance of three such algorithms (a sine wave,a sine-exponential, and a linear model) at all times of the year

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Rob K. Newsom and Robert M. Banta

velocity field. In evaluating J obs the calculation is greatly simplified if the error covariance matrix is diagonal. In this case J obs is given by where Δ m = u m  ·  r m − u obs rm . (9) The observed radial velocity is u obs rm , σ m is the corresponding measurement error, and r m is the unit vector from the lidar to the m th observation. The velocity field generated by the forward model is denoted by u m , where the overbar implies interpolation to the

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Eugenio Gorgucci, Gianfranco Scarchilli, V. Chandrasekar, and V. N. Bringi

( Z h , Z dr ), R β ( K dp ), and R β ( K dp , Z dr ) as a function of β. It can be seen from Fig. 4 that the bias due to variation in β is negligible compared to that shown in Fig. 1 . The model presented in this paper makes a linear approximation to the shape–size relationship. However, as pointed out earlier in the introduction some commonly used models for shape–size relations are nonlinear. The following analysis evaluates the performance of R β algorithms under this context

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Luwen Chen, Yijun Zhang, Weitao Lu, Dong Zheng, Yang Zhang, Shaodong Chen, and Zhihui Huang

1. Introduction Lightning location systems (LLSs) have been widely applied in many countries and regions as pivotal equipment for lightning detection. The detection efficiency and location accuracy are considered to be the most important performance indices for LLSs. One of the directly effective methods for objectively evaluating performance of LLS is to compare the reliable observation of lightning ground truth with the corresponding LLS records. Triggered lightning observation experiments

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