共查询到18条相似文献，搜索用时 125 毫秒
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若不考虑特定的数据同化方法,数据同化通常可被分解为先验信息、观测算子、观测误差协方差和背景误差协方差等组成部分.本文基于经典的Lorenz模式,研究了数据同化各组成部分对初始条件误差和预报误差的影响,以期为设计不同尺度天气系统的数据同化方法提供理论基础.研究结果表明,预报误差经历三个典型阶段:0～5天为预报误差的缓慢增长期; 5～15天为预报误差的快速增长期,其中确定性预报和集合预报的误差增长速率具有显著差异; 15天后为预报误差的饱和期.数据同化可通过提供更加准确的初始条件,进而提升可预报性.相比于静态背景误差协方差(B),流依赖的背景误差协方差(Pf)可提供更精确的初始条件,因此当瞬时观测或频繁的时间平均观测被同化时,循环同化效果优于离线同化;但当时间平均观测频率低时则结果相反,这是因为循环同化在模式缺乏预报技巧时无法构造具有信息的先验估计,且流依赖的Pf相比于静态的B不能有效地从含信息量低的观测中提取出观测信息.瞬时观测相比于时间平均观测包含更多的信息,因此在时间频率低的观测系统中,瞬时观测应优先被考虑.此外,集合预报优于确定性预报,且... 相似文献
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《中国科学:地球科学》2021,(10)
陆地碳循环是地球生物化学循环的重要组成部分,与人类福祉和可持续发展息息相关,但其模拟和观测都具有高度不确定性.融合模型和观测数据以减少陆地碳循环估计的不确定性、提高其可预测性,已成为陆地碳循环研究前沿.文章综述了陆地碳循环模型与观测各自不确定性的来源和特征,介绍了数据同化和参数估计这两类模型-数据融合方法的数学原理,其实质都是在考虑模型和观测各自误差的基础上,实现模型和观测信息的最优融合.文章进一步分析了陆地碳循环模型-数据融合的挑战和研究热点,重点讨论了真实和虚假的模型"异参同效"及其可识别性,地面通量观测与遥感观测代表性误差的估计,敏感性分析得到的参数后验概率分布对于确定模型误差矩阵的潜在作用,对日光诱导叶绿素荧光等新型遥感观测的同化,并指出把多源观测整合到一个协调一致的碳数据同化系统中绝非易事,然而这方面的突破是发展新一代全球碳数据同化系统的前提.论文最后指出,应用陆地碳循环数据同化,产出更高分辨率、更长时间序列、更可靠和一致的陆地碳循环再分析数据产品,对于准确估计全球和区域碳循环、实现碳管理和碳中和具有重要意义. 相似文献
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利用便携式光谱辐射计,采用一定的观测角度获取水体表面的光谱,进而提取水表面下辐照度比R(0-)信息,分析R(0-)光谱特征与叶绿素a浓度之间的相互关系,结果表明太湖夏季水体叶绿素a浓度与R(0-)光谱曲线762 nm、727nm和496nm处的相关系数较大,分别达到了0.85、0.84、-0.80.通过单波段、波段比值模型分析,认为以R(0-)761、R(0-)762/R(0-)496、R(0-)727/R(0-)496为自变量的二次函数模型是利用水表面下辐照度比R(0-)估算太湖夏季水体中叶绿素a浓度的最佳模型,模型的决定系数R2分别达到了0.923、0.919、0.916,回归估计的标准误差S分别为0.012、0.013、0.013,F检验值分别为101.241、96.576、92.925.利用剩余10个样本对估算模型进行精度和误差检验,结果表明以R(0-)762/R(0-)496为自变量的二次函数模型好于另外两个,对太湖夏季水体叶绿素a浓度估算具有一定的实用性.此外,将光谱微分技术应用到R(0-)信息分析太湖夏季水体叶绿素a浓度,结果不能获得较高的预测精度. 相似文献
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NDCI法Ⅱ类水体叶绿素a浓度高光谱遥感数据估算 总被引：1，自引：0，他引：1
以太湖、巢湖为研究区,以Hyperion和HJ-1A卫星HSI高光谱数据以及实测水质浓度数据为实验数据,引入归一化叶绿素指数(NDCI),对Ⅱ类水体的高光谱叶绿素a浓度估算进行分析研究.首先对高光谱数据的光谱通道设置以及水体光谱特征进行分析,研究确定模型的最优波段.然后,将确定最优波段后的NDCI反射率因子作为变量与实测样本点数据进行回归分析,得到NDCI与叶绿素a浓度之间的回归关系,进行叶绿素a浓度的估算.与常用的比值法、一阶微分法和三波段法相比,NDCI的性能优于这3种方法,表明NDCI是一种计算简单、估算精度高、实用性强的Ⅱ类水体叶绿素a浓度估算方法. 相似文献
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本文基于YH4DVAR业务系统构建了集合资料同化试验平台,利用10个集合样本统计得到的流依赖背景误差能显著改进业务应用中背景误差方差的结构和大小.但是受样本数的限制,背景误差方差的集合估计值中引入了大量的随机取样噪声.为了降低噪声对估计值的影响,本文采用谱滤波方法,根据信号和噪声尺度的统计特征构造一个低通滤波器来滤除背景误差方差估计值中的大部分随机取样噪声.在2013年第九号台风"飞燕"的集合方差滤波试验中,10个样本的滤波结果优于30个样本的集合估计值.谱滤波方法的成功应用有效降低了集合资料同化系统对集合样本数的要求,将是集合资料同化系统未来业务化运行的一项不可或缺的关键技术. 相似文献
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富营养化模型是进行湖泊水环境质量预测和管理的重要工具,然而模型客观存在的误差一直是应用者关心的重要问题.数据同化作为连接观测数据与数值模型的重要方法,可以有效提高模型的准确性.集合卡尔曼滤波(En KF)是众多数据同化算法中应用最为广泛的一种,可进行非线性系统的数据同化,并能有效降低数据同化的计算量.本研究以太湖作为具体实例,选择Delft3D-BLOOM作为富营养化模型,在数值实验确定En KF集合数为100、观测误差方差为1%、模拟误差方差为10%的基础上分别进行模型状态变量同化以及状态变量与关键参数同步同化.结果显示,仅同化状态变量时,模型预测精度有所增加;同时同化状态变量和关键参数时,可显著提升模型在湖泊水环境质量预测中的精度.该研究为应用集合卡尔曼滤波以提高复杂的湖库富营养化模型模拟精度提供了有效的方法. 相似文献
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基于反射光谱的太湖北部叶绿素a浓度定量估算 总被引：2，自引：0，他引：2
利用地物光谱仪研究了太湖水体的反射光谱特征与叶绿素a浓度之间的定量关系,结果表明太湖水体的叶绿素a浓度可以用720 nm附近的反射率估算,同时也可以用806 nm和571 nm两个波段的反射率比值来估算,前者建立的估算模型具有较好的通用性,而后者只能较好的估算<10μg/L的叶绿素a浓度;通过对光谱微分的分析,发现叶绿素a浓度与690 nm附近的一阶微分和702 nm附近的二阶微分相关性最好,但基于反射光谱一阶微分的叶绿素a浓度估算模型,并没有显著的提高太湖叶绿素a浓度的估测精度,二阶微分后的估测精度好于一阶微分,但其估测精度仍没有利用720 nm反射光谱的反演模型高．太湖水体的叶绿素a浓度可以利用720 nm附近的反射光谱有效地估算． 相似文献
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同化站点观测和SSM／I亮温改善冻土活动层状态变量的模拟精度 总被引：2，自引：0，他引：2
以考虑了土壤冻融过程的一维水-热-盐分耦合模型SHAW为冻土活动层数据同化系统的动力学约束框架,通过集合卡尔曼滤波算法同化土壤水分和温度的站点观测数据以及被动微波辐射计SSM／I19GHz亮温观测数据,以改善冻土活动层水热状态变量的估计精度,实现模型模拟和观测信息的融合。冬季活动层冻结,同化的关键变量为土壤温度;而夏季同化的关键变量为土壤水分。通过单点同化试验表明,该同化系统能显著改善土壤表层水分和温度的估计精度;同时,在同化过程中给定合理的模型误差协方差项,可将表层优化后的信息迅速传递给深层土壤,达到改善整个土壤廓线状态变量估计的目的。同化结果表明,相对于SHAW模拟结果,同化4cm土壤温度观测后,各层土壤温度RMSE平均减小0.96℃,而同化4cm土壤水分观测数据后,各层土壤水分RMSE平均减小0.020m^3·m^-3;同化SSM／I 19GHz亮温后,各层土壤温度RMSE平均减小0.76℃,各层土壤水分RMSE平均减小0.018m^3·m^-3。 相似文献
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本文采用高效集合卡尔曼滤波(EnKF)算法和背景场热层电离层理论模式NCAR-TIEGCM,开发了热层电离层数据同化系统.基于全球空地基GNSS电离层斜TEC观测、CHAMP和TIMED/GUVI热层参量观测构型设计了系列观测系统模拟实验,对热层参量进行估计.实验结果表明,(1)通过集合卡尔曼滤波算法同化电离层TEC观测能够较好地优化热层参量.(2)中性质量密度优化效果在整个同化阶段均有提升,提升百分比能达到40%.(3)积分氧氮比在同化阶段也能得到较好的优化,但在电子密度水平梯度变化剧烈区域效果较差.最后本文对中性质量密度进行了预报评估,结果表明,由于中性成分优化,在地磁平静条件下其预报时间尺度可长达24h. 相似文献
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太湖叶绿素a的时空分布特征及其与环境因子的相关关系 总被引：14，自引：3，他引：11
2012年3月至2013年2月逐月对太湖水体叶绿素a含量、主要环境因子及不同门类浮游植物密度进行测定,分析太湖叶绿素a含量和不同门类浮游植物密度的时空分布特征,探讨太湖叶绿素a含量和环境因子与不同门类浮游植物密度之间的相关关系并建立逐步回归方程.结果表明:太湖叶绿素a含量全年平均值为22.33±37.65 mg/m3,变幅为0.48~347.85 mg/m3;叶绿素a含量随季节变化明显,夏季最高、秋冬季次之、春季最低;在空间分布上,太湖北部和西北部最高,东部和南部最低.蓝藻门、隐藻门、硅藻门、绿藻门密度随时间呈峰型变化,均在10月份达到最大值,黄藻门、金藻门和裸藻门密度的变化趋势呈"V"型,在春、冬两季出现较大值;不同门类浮游植物密度基本在西北区出现最大值.全湖叶绿素a含量的显著影响因子有总有机碳、亚硝态氮、溶解氧、pH、水温和磷酸盐;lg(YChl.a)与lg(XTN)呈显著负相关,与lg(XTP)呈极显著正相关,与lg(XN/P)呈极显著负相关.太湖叶绿素a含量与蓝藻门、隐藻门、裸藻门与甲藻门密度有显著相关关系. 相似文献
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An ensemble Kalman filter (EnKF) is developed to identify a hydraulic conductivity distribution in a heterogeneous medium by assimilating solute concentration measurements of solute transport in the field with a steady‐state flow. A synthetic case with the mixed Neumann/Dirichlet boundary conditions is designed to investigate the capacity of the data assimilation methods to identify a conductivity distribution. The developed method is demonstrated in 2‐D transient solute transport with two different initial instant solute injection areas. The influences of the observation error and model error on the updated results are considered in this study. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating solute concentration measurements. The larger area of the initial distribution and the more observed data obtained, the better the calculation results. When the standard deviation of the observation error varies from 1% to 30% of the solute concentration measurements, the simulated results by the data assimilation method do not change much, which indicates that assimilation results are not very sensitive to the standard deviation of the observation error in this study. When the inflation factor is more than 1.0 to enlarge the model error by increasing the forecast error covariance matrix, the updated results of the hydraulic conductivity by the data assimilation method are not good at all. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model. The evolutionary data assimilation (EDA) method uses multi-objective evolutionary strategy to continuously evolve ensemble of model states and parameter sets where it adaptively determines the model error and the penalty function for different assimilation time steps. The assimilation was determined by applying the penalty function to merge background information (i.e., model forecast) with perturbed observation data. The assimilation was based on updated estimates of the model state and its parameterizations, and was complemented by a continuous evolution of competitive solutions.The EDA was illustrated in an integrated assimilation approach to estimate model state using soil moisture, which in turn was incorporated into the soil and water assessment tool (SWAT) to assimilate streamflow. Soil moisture was independently assimilated to allow estimation of its model error, where the estimated model state was integrated into SWAT to determine background streamflow information before they are merged with perturbed observation data. Application of the EDA in Spencer Creek watershed in southern Ontario, Canada generates a time series of soil moisture and streamflow. Evaluation of soil moisture and streamflow assimilation results demonstrates the capability of the EDA to simultaneously estimate model state and parameterizations for real-time forecasting operations. The results show improvement in both streamflow and soil moisture estimates when compared to open-loop simulation, and a close matching between the background and the assimilation illustrates the forecasting performance of the EDA approach. 相似文献
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荧光光谱分析技术具有灵敏度高、检测快速等优点,三维荧光光谱提供的指纹荧光信息比普通荧光光谱更丰富,选择性更好,在多组分分离上更具优势.离散三维活体荧光光谱法通过提取水体蓝藻、绿藻、硅藻、甲藻和隐藻5个门类藻类荧光光谱的指纹特征,分类测量藻类叶绿素a浓度,经过加和得到总的水体叶绿素a浓度.将基于该方法研制的三维荧光光谱水体藻类原位测量仪用于太湖水体叶绿素a浓度的测定,并与YSI多参数水质检测仪、BBE藻类现场分析仪、分光光度法等测定结果对比,结果表明:该方法与分光光度法间无显著性差异,与分光光度法、BBE法间的相关性好,相关系数达0.96以上,精密度、准确率优于基于普通荧光法的原位测量仪,是一种快速有效的原位测量方法. 相似文献
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An effective method based on dynamic sampling for data assimilation in a global wave model 总被引：3，自引：2，他引：1
The ensemble Kalman filter (EnKF) performs well because that the covariance of background error is varying along time. It provides a dynamic estimate of background error and represents the reasonable statistic characters of background error. However, high computational cost due to model ensemble in EnKF is employed. In this study, two methods referred as static and dynamic sampling methods are proposed to obtain a good performance and reduce the computation cost. Ensemble adjustment Kalman filter (EAKF) method is used in a global surface wave model to examine the performance of EnKF. The 24-h interval difference of simulated significant wave height (SWH) within 1 year is used to compose the static samples for ensemble errors, and these errors are used to construct the ensemble states at each time the observations are available. And then, the same method of updating the model states in the EAKF is applied for the ensemble states constructed by a static sampling method. The dynamic sampling method employs a similar method to construct the ensemble states, but the period of the simulated SWH is changing with time. Here, 7 days before and after the observation time is used as this period. To examine the performance of three schemes, EAKF, static, or dynamic sampling method, observations from satellite Jason-2 in 2014 are assimilated into a global wave model, and observations from satellite Saral are used for validation. The results indicate that the EAKF performs best, while the static sampling method is relatively worse. The dynamic sampling method improves an assimilation effect dramatically compared to the static sampling method, and its overall performance is closed to the EAKF. In low latitudes, the dynamic sampling method has a slight advantage over the EAKF. In the dynamic or static sampling methods, only one wave model is required to run and their computational cost is reduced sharply. According to the performance of these three methods, the dynamic sampling method can treated as an effective alternative of EnKF, which could reduce the computational cost and provide a good performance of data assimilation. 相似文献
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Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter 总被引：4，自引：0，他引：4
Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system. 相似文献
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基于突变理论的太湖蓝藻水华危险性分区评价 总被引：7，自引：2，他引：5
蓝藻水华暴发是湖泊生态系统中营养物质长期累积的结果,是系统营养经长期演化后的极端状态.突变理论评价方法无需确定指标权重,减少了人为主观因素,并且计算方便.本文基于突变理论,采取蓝藻水华暴发的表征因子(叶绿素浓度)和导致蓝藻水华暴发的环境因子(总氮和总磷)作为潜在危险性评价指标,蓝藻水华的面积、范围以及暴发频次作为历史危险性评价指标建立多准则蓝藻水华暴发风险评价指标体系,并结合太湖九个分区进行蓝藻水华暴发危险性分区及全湖评价.研究结果表明:竺山湖和西部沿岸为极重危险性湖区;梅梁湾为重度危险性湖区;南部沿岸、贡湖和大太湖为中度危险性湖区;箭湖东茭咀、东太湖和胥湖蓝藻水华暴发危险性较小,为轻微危险性湖区.整体上看,太湖蓝藻水华暴发危险性程度由轻到重基本上沿东南-西北方向变化,与营养盐浓度由低到高分布趋势相一致.根据评价结果,可以明确太湖各区遭遇蓝藻水华暴发危险性的大小,为蓝藻水华风险管理和应急处理提供科学依据. 相似文献