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Lance O’Steen and David Werth

. Improvement in mesoscale simulations by parameter adjustment is often attempted in a subjective manner by observing the agreement with observations while changing the relevant user-input parameters that are judged by experience to have a strong impact on the solution (e.g., reducing the initial soil moisture if the existing solution is too wet). However, it is unlikely that the resulting simulation is actually optimized relative to the observations. A more systematic, objective, and robust procedure is

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James M. Kurdzo and Robert D. Palmer

-band coverage in areas not covered at low-levels by S-band systems. A need therefore exists to optimize such a multifrequency network, in order to maximize coverage while minimizing cost. Through a series of optimizations, a cost-benefit analysis can be completed, informing network designers as to the most cost-effective network possibilities (in the case of this paper, “benefit” refers to coverage). Other radar network designs may also gain from similar optimizations, including single-frequency networks

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Hiroshi Sumata, Frank Kauker, Michael Karcher, and Rüdiger Gerdes

.g., Massonnet et al. 2011 ), since the optimality of individual parameters depends on the other parameters (i.e., optimal parameters are interrelated; Chapman et al. 1994 ; Posselt and Vukicevic 2010 ; McLay and Liu 2014 ). The introduction of new observational data products, in addition, offers opportunities for further validation/examination of the optimal parameters. For these reasons modelers need efficient and automated parameter optimization algorithms. Studies exploring optimal parameters have

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Pascal Horton, Michel Jaboyedoff, and Charles Obled

evolutionary multiobjective optimization. Metaheuristics for Multiobjective Optimisation , X. Gandibleux et al., Eds., Lecture Notes in Economics and Mathematical Systems, Vol. 535, 3–37, doi: 10.1007/978-3-642-17144-4_1 . 10.1007/978-3-642-17144-4_1

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Qiang Wang, Youmin Tang, and Henk A. Dijkstra

perturbation (CNOP-P) method proposed by Mu et al. (2010) can be used to assess the largest effects of model parameter uncertainties. The CNOP-P method is a constraint optimization method, which can find the parameter perturbations that cause the largest uncertainties among the model results. Based on the CNOP-P approach, Yin et al. (2014) and Sun and Mu (2016) explored model parameter sensitivities. However, the strategy for identifying the sensitive parameters in Yin et al. (2014) depends on the

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Dinesh Manian, James M. Kaihatu, and Emily M. Zechman

management of resources and data, techniques that make use of existing datasets to optimize the application of such platforms toward a particular end use would be beneficial. There has been some related prior work on the sensitivity of bathymetric information on the results of predictive numerical models. Kaihatu and O’Reilly (2002) investigated the effect of updated bathymetric surveys of Scripps Canyon, California (CA) on the results of the Simulating Waves Nearshore (SWAN; Booij et al. 1999

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Moussa Waongo, Patrick Laux, Seydou B. Traoré, Moussa Sanon, and Harald Kunstmann

; Roudier et al. 2011 ; Biazin et al. 2012 ), where farmers have to cope with high rainfall variability. Different soil and water management techniques have been developed and promoted throughout SSA countries to optimize water consumption by plants ( Rockstrom et al. 2002 ; Kaboré and Reij 2004 ). However, with prolonged dry spells at the beginning of the rainy season, the risk of resowing and crop failures during the first stage of plant development is still a major concern in smallholder farming

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Xuefeng Zhang, Shaoqing Zhang, Zhengyu Liu, Xinrong Wu, and Guijun Han

tends to drift away from observed states and thus has limited forecast skill ( Smith et al. 2007 ). Regardless of whether the model error arises from dynamical core misfitting, physical scheme approximation, or model parameter errors, the model errors, in principle, could be corrected using data assimilation. Using observations to optimize uncertain parameters in climate models is an important and complex subject. Many efforts have been undertaken to include model parameters into data assimilation

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J. N. Stroh, Gleb Panteleev, Max Yaremchuk, Oceana Francis, and Richard Allard

* determined by optimizing sea ice drift include 27.5 kN m −2 ( Hibler and Walsh 1982 ), 15–20 kN m −2 ( Kreyscher et al. 1997 , 2000 ), and 30–45 kN m −2 ( Tremblay and Hakakian 2006 ). The latter work emphasizes the significant variability of their P * estimates, which may be attributed to spatial and temporal variation of Arctic sea ice and better represented using a nonelliptic form of the yield curve (e.g., Tremblay and Hakakian 2006 ). Recently, the impact of spatial nonuniformity of the ice

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Navid Tahvildari and James M. Kaihatu

, Rogers and Holland (2009) used a phase-averaged forward model and an inverse technique to deduce mud properties at Cassino Beach, Brazil. In this study we use a phase-resolving nonlinear wave model and a mud dissipation mechanism, in conjunction with the Levenberg–Marquardt numerical optimization method (e.g., Press et al. 1986 ) to deduce the mud characteristics from free surface data. One of the goals of this study, aside from creating and testing the inversion algorithm, is to see whether

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