Optimal Precursors Triggering the Kuroshio Extension State Transition Obtained by the Conditional Nonlinear Optimal Perturbation Approach

In this study, the initial perturbations that are the easiest to trigger the Kuroshio Extension(KE) transition connecting a basic weak jet state and a strong, fairly stable meandering state, are investigated using a reduced-gravity shallow water ocean model and the CNOP(Conditional Nonlinear Optimal Perturbation) approach. This kind of initial perturbation is called an optimal precursor(OPR). The spatial structures and evolutionary processes of the OPRs are analyzed in detail. The results show that most of the OPRs are in the form of negative sea surface height(SSH) anomalies mainly located in a narrow band region south of the KE jet, in basic agreement with altimetric observations. These negative SSH anomalies reduce the meridional SSH gradient within the KE, thus weakening the strength of the jet. The KE jet then becomes more convoluted, with a high-frequency and large-amplitude variability corresponding to a high eddy kinetic energy level; this gradually strengthens the KE jet through an inverse energy cascade. Eventually, the KE reaches a high-energy state characterized by two well defined and fairly stable anticyclonic meanders. Moreover, sensitivity experiments indicate that the spatial structures of the OPRs are not sensitive to the model parameters and to the optimization times used in the analysis.     

线性时变大系统的分散最优控制与镇定

本文提出一种关于线性时变大系统最优控制的二次型性能指标分解方法。据此研究线性时变大系统局部分散最优控制的逆问题．得到大系统关于某二次型性能指标存在最优状态反馈控制律的条件，同时它也是大系统局部分散镇定的一个新的充分条件．     

正弦干扰下时滞系统的状态预测观测器与前馈-反馈预测控制器设计

研究控制变量含有时滞的线性系统在外部正弦干扰下的最优减振预测控制问题。利用系统的控制向量和被控对象的预测输出向量 ,设计了 1种全维状态预测观测器。并将该状态观测器用于时滞控制系统的最优前馈 -反馈预测控制中。频域分析表明 ,应用该状态预测观测器可将闭环系统的时滞项移至系统闭环结构之外 ,从而其优化控制规律完全可以按无时滞系统进行设计。时域分析表明 ,设计的预测控制器对外部正弦干扰有较强的鲁棒性 ,得到的结果关于二次型平均性能指标是次优的     

福建春季降水量场预测模型研究

利用1960—2000年福建春季(2—4月)降水量场资料，分析了经验正交函数(EOF)展开得出的特征向量，以反映降水的空间分布特征。研究了前三种分布型的时间系数与前期北半球500hPa、100hPa月高度距平场和北太平洋海温距平场之间的关系，针对不同的分布型分别找出了不同的前期预测信号，以此为预测因子，构建出福建春季降水量场短期气候预测模型。利用该模型对2001、2002年福建春季降水量场进行试预测，取得了较满意的结果。     

基于IDW 的埕岛海域水下三角洲地形演变

为研究埕岛海域水下三角洲地形演变,利用1958—2014年间埕岛海域7个不同时期水深点的数据,采用 不同插值方法开展适用性比较,确定选用反距离加权法进行数据插值,生成研究海域范围内7期水深地形图。通过栅格计算对不同时期的水深地形进行研究,分析比较了各个时期的地形特征,总结出区域内地形演变规律。研究表明,埕岛海域内整体地形呈西南高东北低走势,3~12 m 等深线间水下岸坡地带地形最为复杂且变化剧烈,走河期淤积中心不断向东北方向推进,最大淤积厚度超过12 m,废弃后侵蚀中心逐步向岸移动,且后期侵蚀速率明显降低,形成了“陡-缓-陡”的地形特征。     

一种改进的频率匹配法在网格降水预报订正中的应用

为提高精细化网格降水的实际预报能力,评估了2021年汛期ECMWF(EC)、CMA-MESO、SXWRF和SCMOC降水预报产品在陕西的表现,讨论了卡尔曼动态频率匹配方法对不同模式的订正效果,然后针对该方法不足,基于最优TS评分阈值法和SCMOC在天气过程判定中占优信息对小量级降水进行了二次订正,最后利用分类降水过程建模和基于图像相似识别技术改进的卡尔曼动态频率匹配法对暴雨进行了订正研究。结果表明:SCMOC晴雨预报准确率和暴雨TS评分均最高,分别为81.60%和0.30,表现最好;卡尔曼动态频率匹配法可明显提高EC、CMA-MESO和SXWRF模式降水预报产品晴雨预报准确率,对暴雨预报的改善效果不稳定,对EC晴雨预报准确率和暴雨TS评分提升幅度均最大,分别为6.35%和6.99%,该订正方法更适合于EC模式;经晴雨消空二次订正后的EC模式晴雨和小雨预报准确率较一次订正后的EC模式均有提高,分别提高了0.51%和0.64%;分类降水过程建模订正可进一步提高EC暴雨TS评分,较未分类过程订正后的暴雨TS评分提高了1.05%,且暴雨其他评分指标也均变好;改进后的卡尔曼动态频率匹配法较改进前...     

基于改进三次样条插值的探空资料质控研究

由于L波段探空资料存在缺测及稀疏、零散的数据,造成信息量在时空分布上的不均匀性。考虑到参与插补计算的权重因子个数和插补半径对探空观测数据影响较大,在三次样条插值基础上将插补半径和参与计算权重因子个数作为约束条件引入算法中,以被插值点±40 gpm为插值半径,取有效因子个数≥3个,提出了改进的三次样条插补算法。通过交叉验证分时段对插补结果进行检验评估,气温AE在±0.1℃之间,RMSE值小于0.08;相对湿度AE在-1.0%～+0.7%之间,RMSE值小于0.3;风速数据AE在±0.6 m/s之间,RMSE值小于0.2,表明改进三次样条插补算法对探空资料的质控效果在时间和空间上一致性较好。在晴空条件下相对湿度的插补效果优于有降水天气,气温和风速的插补效果不受晴空条件和降水条件的影响。     

用曲波变换消除VSP数据处理中的空间假频与采样不规则

由于条件与经费的限制,在实际地震资料采集过程中,常常会遇到获得的地震资料较稀疏或不规则,导致空间假频、采样不规则的问题,影响地震资料的成像效果。可利用地震道空间插值来解决这些问题,提升地震数据处理结果品质。曲波变换的基函数是具有倾角、频率、时间三方面同时局部化的函数,它可以对空间信号做稀疏表达,适合地震资料的插值处理。本文采用该方法对理论模型数据和实际VSP数据作插值处理,解决不规则采样、空间假频问题。实际应用表明该方法精度高,插值准确,处理结果的振幅和同相轴连续性好。     

Optimal spatial allocation of irrigation water under uncertainty using the bilayer nested optimisation algorithm and geospatial technology

The optimal spatial allocation of irrigation water under uncertainty has become a serious concern because of irrigation water shortage and uncertain factors that affect irrigation water allocation. In this study, an optimal multi-objective model for irrigation water allocation under uncertainty is developed to maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation. The original and optimal plantation structure, irrigation mode and soil water content are acquired through geospatial technology. A bilayer nested optimisation (BLNO) algorithm is designed to produce multiple individuals of design vectors using an ant colony neural network algorithm for an outer optimisation. Meanwhile, a continuous adaptive ant colony (CAAC) algorithm is used for inner optimisation to calculate the interval values of the uncertain model. The crop distribution and irrigation mode are obtained to parameterise the planting area and the water demand of each crop and each block in the multi-objective model. This model is then solved and analysed. Compared to the optimal schemes obtained from an inexact two-stage fuzzy-stochastic programming and the CAAC, respectively, BLNO can effectively and efficiently solve the optimal spatial allocation of irrigation water under uncertainty. This method can spatially maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation using lower and upper bound maps whilst visually obtaining the exact crop type, reasonable irrigation method and precise water demand for each block and for the entire irrigated area.