%0 Journal Article %T A Research of Four-Dimensional Variational Data Assimilation Based on Genetic Algorithm
基于遗传算法的四维变分资料同化技术的研究 %A HU Ya-Min %A DING Yi-Hui %A SHEN Tong-Li %A
胡娅敏 %A 丁一汇 %A 沈桐立 %J 大气科学 %D 2006 %I %X Four-dimensional(4D) variational data assimilation(VDA) has been recognized as one of the effective methods to improve numerical weather predication(NWP) initial field.But,the method still has some shortcomings,for example,1) the optimization method used in VDA should satisfy continual presumption,while the discontinuity of a forecast model,such as the parameterization physical process,will make the presupposition available no more;2) the cost function always presents multimodal distribution owning to its non-linear dynamic constraint,whereas the present descent algorithms of VDA only aim at the local optimization;3) furthermore,the adjoint VDA requires higher computing resource in operation.Therefore,it is necessary to seek for a new algorithm with weaker demand to mathematical properties of cost function and higher skill in searching out the global optimum or proximity,together with less computer time to match with the operational demand.In this context,the genetic algorithm(GA) is applied to 4DVDA.The raitional genetic coding,manipulation and parameters,together with related theoretical basis and detailed procedure are designed based upon the properties of the variational technique.The algorithm will be described as follows in detail: 1) real encoding of the parameters;2) population initialization with model solutions trajectory and the reciprocal of cost function as fitness equivalent;3) selection of the intermediate population with roulette wheel method;4) adoption of self-adaptive and mandatory crossover probability to produce better individuals towards greater fitness;5) application of the quasi-elitist strategy to directly reproduce parent individuals of higher fitness to next generation for better individual reserved;6) introduction of the steepest descent method,in order to exert its superiority of calculation velocity and precision,to make the mutation manipulation along with the descent direction;7) selection of genetic control parameters also playing an important role in GA,whose rationality depends on the solution convergence and quality after running times without number.Thus, it can be seen that in the process of assuring the population diversity,all of the operations make the population evolve towards greater fitness and accelerate the convergence velocity of GA global optimization.As an example,the output of a GA-based VDA model constructed under the constraint of 2-dimensional shallow water equations has been compared with that of a scheme with an adjoint VDA model.The results suggest that the convergence precision of GA-based scheme is in common with that of the adjoint equivalent to great degree.When the assimilation window is 6 hours,the convergence velocity of GA-based method is nearly in agreement with that of the adjoint scheme.If the window increases to 12 hours,the former is superior to the latter.Consequently, the GA-based VDA scheme shows a better performance than the adjoint counterpart with the assimilation time win %K genetic algorithm(GA) %K variational data assimilation(VDA) %K crossover %K mutation
遗传算法 %K 变分资料同化 %K 交叉 %K 变异 %U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=28A2F569B2458C17&jid=46874A5A102033D774D00D819E91CD68&aid=783AAFBE922EB3D9&yid=37904DC365DD7266&vid=340AC2BF8E7AB4FD&iid=0B39A22176CE99FB&sid=FBA00558C57D9C11&eid=80BD0A2EF8664214&journal_id=1006-9895&journal_name=大气科学&referenced_num=8&reference_num=19