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1. Introduction The so-called model bias occurs when systematic errors are introduced by model deficiencies including, as pointed out by Danforth et al. (2007) , approximations in numerical differentiation and integration, inaccurate forcing, and parameterizations used to represent subgrid-scale physical processes. Parameter values involved in the above practices yet to be determined empirically, however, can be estimated or optimized in tandem with the model state through assimilation
1. Introduction The so-called model bias occurs when systematic errors are introduced by model deficiencies including, as pointed out by Danforth et al. (2007) , approximations in numerical differentiation and integration, inaccurate forcing, and parameterizations used to represent subgrid-scale physical processes. Parameter values involved in the above practices yet to be determined empirically, however, can be estimated or optimized in tandem with the model state through assimilation
. 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
. 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
-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
-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
.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
.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
the Delta method. Additionally, the BS method has a high computational cost for large datasets ( Khosravi et al. 2011b ). The second category was recently developed based on optimization techniques, including the PI’s direct construction ( Khosravi et al. 2011a ). The basic technique in this group is the lower upper bound estimation (LUBE) method; variations of the original version of LUBE were developed by modifying its cost function (e.g., Quan et al. 2014 ; Zhang et al. 2015 ). LUBE
the Delta method. Additionally, the BS method has a high computational cost for large datasets ( Khosravi et al. 2011b ). The second category was recently developed based on optimization techniques, including the PI’s direct construction ( Khosravi et al. 2011a ). The basic technique in this group is the lower upper bound estimation (LUBE) method; variations of the original version of LUBE were developed by modifying its cost function (e.g., Quan et al. 2014 ; Zhang et al. 2015 ). LUBE
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
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
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
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
; 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
; 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
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
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
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
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
