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Christoph K. Thomas and Alexander R. Smoot

to construct an inexpensive radiation shield that performs equally or better than commercially available models, and not to comprehensively evaluate the heat budget of the sensor. To account for any systematic biases in air temperature measurements between the sensors in the different radiation shields, a linear regression model was applied to all observations in the CAS. Model coefficients were determined using all nighttime data with wind speeds U ≥ 1 m s −1 when radiation errors and

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Vandoir Bourscheidt, Kenneth L. Cummins, Osmar Pinto Jr., and Kleber P. Naccarato

Vaisala) with different technology and sensitivity (lightning sensor models LPATS, IMPACT, LS7000, and LS8000, as shown in Table 1 ). All of these upgrades, associated with some gaps in the detection for different sensors, lead to variations in the network performance along the years. Special care must be taken to address these effects. Table . 1. BLDN network variations over the years (by amount and type of sensors). These variations are usually evaluated through the system detection efficiency

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B. Root, T-Y. Yu, M. Yeary, and M. B. Richman

subsections describe how the models and the datasets were created and evaluated. a. WEKA The Waikato Environment for Knowledge Analysis (WEKA) is a software “workbench” for experimenting with and learning different machine-learning techniques ( Witten and Frank 2005 ). WEKA is capable of performing many different data analysis tasks, which makes it ideal for comparing analysis techniques. For this work, version 3.5.8 was used. b. Multilayer perceptron The ANN model for this work is the “multilayer

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J. Mielikainen, B. Huang, H.-L. A. Huang, M. D. Goldberg, and A. Mehta

that a large fraction of performance is being lost to data transfer overhead. Much of this should be amortizable as more and more weather model kernels are adapted to run and reuse the model state data on the GPU without moving it back and forth from the CPU. Therefore, the GPU-based implementation of WDM6 provided a low-cost and effective solution for analyzing microphysics modules in WRF. Future work will be rewriting the other WRF modules for GPU execution. This will mean rewriting several

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Pengfei Xia, Shirong Ye, Caijun Xu, and Weiping Jiang

empirical model is the most important consideration, as PWV measurement depends on the performance of ZHD models. Over the last few decades, many empirical hydrostatic models have been developed to estimate ZHD. These models provide a convenient way to revise tropospheric hydrostatic delay and have been widely used in GNSS positioning and GNSS meteorology ( Schüler 2014 ; Böhm et al. 2015 ; Huang et al. 2020 ). Several studies have been conducted to evaluate the performance of different ZHD models

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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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Zepei Wu, Shuo Liu, Delong Zhao, Ling Yang, Zixin Xu, Zhipeng Yang, Dantong Liu, Tao Liu, Yan Ding, Wei Zhou, Hui He, Mengyu Huang, Ruijie Li, and Deping Ding

finally the model with the highest accuracy will be obtained. b. Experimental platform and evaluation methods The experimental running platform used in this paper is as follows: MATLAB R2016A (MathWorks Corporation, Massachusetts), Anaconda 3.5 (Anaconda, Austin, Texas). The hardware equipment and system environment are a remote Linux system server equipped with six Leadtek p4000 graphics cards. People often only consider accuracy values to evaluate model performance, but single parameters are

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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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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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