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G. R. Halliwell Jr., A. Srinivasan, V. Kourafalou, H. Yang, D. Willey, M. Le Hénaff, and R. Atlas

-resolution coastal models nested within it that will all employ realistic high-frequency river runoff ( Schiller et al. 2011 ) for the purpose of evaluating coastal ocean observing systems. c. Evaluation of the T-SIS DA methodology Before conducting the OSSE system evaluation, the performance of the new T-SIS DA methodology is analyzed in comparison to two operational HYCOM Navy Coupled Ocean Data Assimilation (NCODA) ocean analysis products produced by the U.S. Navy using the operational HYCOM nowcast

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Timothy R. Keen and Scott M. Glenn

, short-term fluctuations and spatial variability become the dominant indicators of model performance. The passage of a tropical storm is an excellent example of this kind of flow event. The coastal currents produced by these storms are especially suitable for model evaluation in shallow water because both baroclinic and barotropic storm flows are strong enough to be identified for short periods, even when shelf- and basin-scale flows are present. To confidently use numerical models to study the

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Li Liu, Ruizhe Li, Guangwen Yang, Bin Wang, Lijuan Li, and Ye Pu

-of-the-art AGCM to reveal the differences in scalability and parallel efficiency. The remainder of this paper is organized as follows. Section 2 introduces the relevant background material and related work. Section 3 presents our parallelization strategies. Section 4 empirically evaluates these optimizations and analyzes the parallelization overhead. We summarize our conclusions in section 5 . 2. Description of model, high-performance computer, and existing parallelization In this section, we first

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Renske M. A. Timmermans, Martijn Schaap, Peter Builtjes, Hendrik Elbern, Richard Siddans, Stephen Tjemkes, and Robert Vautard

from the meteorological practice. Disturbances in a meteorological model will, in general, cause the runs to diverge from the nature run and lead to lower spatial correlation in time. Hence, in meteorological OSSEs, the performance of runs is often evaluated using the spatial correlation as a measure. Contrarily, chemistry transport models are stable systems because of the continuous input of emissions and the meteorology as driving forces. The tendency of the system to converge is illustrated with

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

( Siggia and Passarelli 2004 ). Parametric methods are compositionally expensive; therefore, it is important to evaluate the signal quality improvement one can expect by applying such methods before trying to implement them in real time. In this work we carry out an extensive evaluation of the performance of the parametric time domain method (PTDM). Similar to Boyer et al. (2003) we construct the estimator using maximum likelihood methodology. The performance of the proposed estimator is compared to

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H. E. Markus Meier and Torgny Faxén

ice have not been explored, because of the expected message passing overhead resulting from frequent redistribution of data between the processors. Further investigations to reduce the residual workload imbalance are not our primary concern, because it is not planned within SWECLIM to run the model on more than 128 processors. Instead, future work should concentrate on single node performance to improve cache and memory usage. Nevertheless, the evaluation shows that RCO is a fast, state

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Rod Frehlich

volume of the pulse. The performance of some common mean-frequencyestimators was presented by Frehlich and Yadlowsky(1994) as a function of the parameter q? for fixed ~and M. The performance of the estimators were described by an empirical model for the probability density function (PDF) of the estimates. The model selected was a Gaussian function of good estimates centered on the true mean frequency and a unitBrmAPRIL1995 NOTES AND CORRESPONDENCE

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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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