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Jason C. Knievel, David A. Ahijevych, and Kevin W. Manning

between the 10- and 4-km simulations was not possible, the latter did seem to produce a more realistic phase in the diurnal mode, perhaps partly because no cumulus parameterization was used. In this article we focused on the WRF model because of its growing prominence in the operational and research communities. However, our underlying point is more general: patterns such as the modes of rainfall frequency we examined are an underused standard for evaluating the performance of numerical weather

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Jeffrey D. Duda and David D. Turner

presently no published procedure in METplus for combining the raw outputs into useful metrics. Some metrics not previously reported in MODE literature are introduced herein and evaluated for their utility in assessing forecast performance. The HRRR model is a 3-km grid spacing forecast system that uses the Advanced Research version of the WRF (ARW) dynamical core. It is informally nested within the larger-domain 13-km Rapid Refresh model ( Benjamin et al. 2016 ), which provides the initial and lateral

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Markus Gross, Hui Wan, Philip J. Rasch, Peter M. Caldwell, David L. Williamson, Daniel Klocke, Christiane Jablonowski, Diana R. Thatcher, Nigel Wood, Mike Cullen, Bob Beare, Martin Willett, Florian Lemarié, Eric Blayo, Sylvie Malardel, Piet Termonia, Almut Gassmann, Peter H. Lauritzen, Hans Johansen, Colin M. Zarzycki, Koichi Sakaguchi, and Ruby Leung

state is evaluated in isolation. Splitting assumes that the processes are either evaluated based on the same state and, hence, do not see the impact of other processes on that state, or they are evaluated sequentially ( Donahue and Caldwell 2018 ). Both approaches are inaccurate reflections of reality. While splitting is useful and often unavoidable, it can lead to undesirable features in the numerical solutions. For example, process splitting can impact the model performance when processes compete

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Wayne M. Angevine, Lee Eddington, Kevin Durkee, Chris Fairall, Laura Bianco, and Jerome Brioude

), and P3 track (green) and Atlantis track (red) during cloud study on 16 May. The 2010 CalNex air quality and climate study involved many people, platforms, and instruments. In this paper, we use a subset of the data to evaluate meteorological model performance. Sites and data were chosen for relevance to the CalNex goals and to show important differences among the model configurations. Among these are data from wind profilers operated by the South Coast Air Quality Management District near Los

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Hongyi Li, Mark S. Wigmosta, Huan Wu, Maoyi Huang, Yinghai Ke, André M. Coleman, and L. Ruby Leung

green stars are the selected USGS streamflow stations where the model-predicted discharges are evaluated. While designing and developing this new model, it is our goal that it is applicable at a broad range of spatial resolutions (e.g., fine to coarse) and scales (e.g., local to global) with relatively consistent performance. The knowledge gained from applications at one resolution can thus be transferred to another resolution without much adjustment ( Guo et al. 2004 ; Wen et al. 2012 ). In this

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Huan Wu, Robert F. Adler, Yang Hong, Yudong Tian, and Fritz Policelli

flood event detection metrics (e.g., probability of detection) and delineation (e.g., flood evolution in the river network). Both studies concluded that a relatively more physically based hydrologic model may improve the GFMS performance ( Hong et al. 2007 ; Yilmaz et al. 2010 ). The Coupled Routing and Excess Storage (CREST) hydrologic model, later developed ( Wang et al. 2011 ) for this purpose, is the subject of the evaluation in this paper. The purpose of this paper is to evaluate the

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Dan Wu, Fuqing Zhang, Xiaomin Chen, Alexander Ryzhkov, Kun Zhao, Matthew R. Kumjian, Xingchao Chen, and Pak-Wai Chan

(HKRD, marked as gray triangle in Fig. 3a ) were used to make an intercomparison with the dataset of the GZRD and results show that these two datasets are generally consistent with each other (not shown). Thus, we only use the polarimetric observations from GZRD in the following analysis. The performance of the model simulations is evaluated against the observations averaged over the period from 1000 to 1100 UTC 1 August. The evaluation period for model simulation is selected when the modeled TC

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Fukang Yin, Guoli Wu, Jianping Wu, Jun Zhao, and Junqiang Song

) . Motivated by the above research, we focus our work solely on providing an optimized implementation and analysis. The novel aspect is the performance evaluation of SHT in the perspective of the properties of interpolation decompositions such as the rank, maximum absolute value, and number of nonzero entries of projection or interpolation matrix. The remainder of the paper is organized as follows. In section 2 , we introduce interpolative decomposition and describe a sparse matrix storage format and

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Kshitij Parajuli, Scott B. Jones, David G. Tarboton, Lawrence E. Hipps, Lin Zhao, Morteza Sadeghi, Mark L. Rockhold, Alfonso Torres-Rua, and Gerald N. Flerchinger

controlling the transfer of water and energy fluxes from land surfaces ( Dirmeyer et al. 2006 ; Dong and Crow 2018 ; Xia et al. 2014 ; Koster et al. 2009 ). Several past studies have focused on evaluating the effects of soil parameterization on model performance in simulating land surface processes ( Bi et al. 2016 ; Breuer et al. 2012 ; Chen et al. 2013 ; Gayler et al. 2013 , 2014 ; Gochis et al. 2010 ; Garrigues et al. 2015 ). Breuer et al. (2012) found that the use of higher

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Ricardo Fonseca, Marouane Temimi, Mohan Satyanarayana Thota, Narendra Reddy Nelli, Michael John Weston, Kentaroh Suzuki, Junya Uchida, Kondapalli Niranjan Kumar, Oliver Branch, Youssef Wehbe, Taha Al Hosari, Noor Al Shamsi, and Abdeltawab Shalaby

models and hydrological models (e.g., in Fu et al. 2005 ; Hattermann et al. 2017 ; Tabari and Willems 2018 ). An example of an intercomparison project of this nature is the Coordinated Regional Climate Downscaling Experiment (CORDEX; ), a project cosponsored by the World Climate Research Programme that aims to, among other things, evaluate and improve regional climate downscaling models and techniques. Zittis et al. (2014) evaluated the performance of 12 different

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