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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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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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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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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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Ryan Lagerquist, David Turner, Imme Ebert-Uphoff, Jebb Stewart, and Venita Hagerty

training data for the U-net++ models. The rest of this paper is organized as follows. Section 2 describes the inner workings of a U-net++, section 3 describes the input data and methods used to train the U-net++ models, section 4 describes experiments to find the best U-net++ configuration (hyperparameters), sections 5 and 6 evaluate and interpret the selected U-net++ models, and section 7 concludes. 2. Background on U-net++ This section focuses mainly on traditional U-nets, extending 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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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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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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