Land data assimilation is a new means for multi-source geospatial data integration.
陆面数据同化是一种集成多源地理空间数据的新思路。
So the actual background error covariance is the key to success of data assimilation technique.
本文主要目的是建立具有实际运行能力的集合卡尔曼滤波资料同化系统。
Data assimilation has been successfully applied in atmospheric, oceanic, and land surface models.
数据同化方法在大气、海洋和陆面模式中得到了成功的应用。
In this paper, the variational data assimilation of two kinds of physical model including "on-off"...
本文研究了两类含有“开关”的物理过程的变分资料同化问题。
Ideal experiments are also conducted to verify the effect of the regularization method on variational data assimilation.
并通过数值试验进一步肯定正则化理论在资料同化中的作用。
The purpose of this paper is to explore the performances of different model error scheme in soil moisture data assimilation.
本文主要目的是探讨不同模式误差方案在土壤湿度同化中的性能。
Data assimilation can give a better initial field, therefore the short-range storm surge forecast has an obvious amelioration.
资料同化能够提供更为合理的预报初始场，对风暴潮的短期预报有较明显的改进。
As applied in weather prediction like probability forecast, ensemble prediction has been used for "targeted observation" and data assimilation.
集合预报的应用，在天气预报上主要是概率预报，另外在“目标观测”、资料同化等方面也有广泛应用。
Through the assimilation cycle, initialization not only improves the following fore - cast, but also advances the whole data assimilation and prediction quality.
初值化不仅对随后的一次预报有明显的改进，而且通过同化循环，提高了整个资料同化和预报的质量。
The four-dimensional data assimilation is to integrate the current and past data into a forecast model equation for providing time continuity and dynamic coupling.
应用伴随方法求解以数值预报方程作为约束条件的四维变分资料同化方案 ，关键问题是如何构造伴随模式。
Furthermore, the conventional observations are done in the MM5 model system, and a four-dimensional variational data assimilation test is made based on observed data.
最后对MM5伴随模式系统进行了梯度检验，并利用实际资料进行四维变分资料同化试验。
The principle and methods of monitoring the soil moisture based on remote sensing information are reviewed. Especially much attention was paid on the data assimilation method.
总结了当前国内外基于遥感信息监测土壤水分的原理和方法，特别是对数据同化法进行了着重阐述。
To carry out the data assimilation of sea temperature observations the adjoint method is applied, which can provide an accurate initial temperature field for numerical prediction.
以一维水温模型为例，利用伴随算子法进行海洋观测数据同化，以便给水温的数值预报提供一个较准确的初始场。
The Ensemble Kalman Filter (EnKF) is a powerful data assimilation method and has proven its efficiency for strongly non-linear dynamical systems but is demanding in computing power.
方法是目前在强非线性系统中应用最广泛，效果最明显的集合资料同化方法。
The ordinary optimization algorithm can not solve the multi-extreme value problem in data assimilation, so an improvement to steepest descent algorithm is proposed to solve the problem.
对于变分同化中经常遇到的多极值问题，一般的优化算法无法解决。
Ensemble data assimilation is at the intersection of ensemble forecasting methodologies and relatively independently developed data assimilation based on the theory of statistical estimation.
集合资料同化是依赖于统计理论，集合预报方法和资料同化方法的有机结合。
Background error covariance is an important part of variational data assimilation system, which is used to spread the observation information to other grid points and vertical levels of the model.
背景误差协方差是变分资料同化系统中的一个重要组成部分，能将观测信息从观测点传播到周围的模式格点和垂直层上。
The key problem of four-dimensional variational data assimilation method, which solves the constraining numerical predict equations through accompanied model, is how to establish an accompanied model.
应用伴随方法求解以数值预报方程作为约束条件的四维变分资料同化方案，关键问题是如何构造伴随模式。
The process of adding another appliance into a collective — appropriately termed "assimilation" — will wipe out any existing data grids on the assimilated appliance.
将另一个设备添加到集合的流程—适当地定义为术语“吸收”—将擦除被吸收的设备上的所有现有数据网格。
To complete the assimilation, the members of the collective will evenly redistribute the data grids and create replicas across the updated collective.
要完成吸收过程，集合成员将均匀地重新分布数据网格并在整个已更新的集合中创建副本。
Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting.
水文和水文气象数据的实时变分同化进入业务水文预报。
This result is used to discuss the problems concerning assimilation of different temperature data, which is of significance for the improvement of the skill of data-assimilation.
进一步应用这个结论讨论了不同来源温度资料的同化问题，对资料同化技术的提高有一定的指导意义。
Considering that the model is traditionally supposed to be exact and only initial data fields are amended in Adjoint Assimilation System (AAS).
鉴于传统的四维资料伴随模式同化系统都是假设模式完全正确仅对初始场进行修正。
Of course, in order to introduce the superiority than other methods, the WOA data is compared with assimilation data, at last the assimilation data is tested credibility.
文中为了说明数据同化的优越性，利用WOA数据与其进行比较，体现了同化数据对真实场描述的可靠性。
Because of the improvements in physical process parameter, grid nesting and 4-d assimilation of data and so on, its performance was improved greatly and its application scope was expanded.
由于在物理过程参数化、网格嵌套、资料四维同化等方面也有许多改进，大大改善了模式性能，扩展了其应用范围。
In this paper the variational adjoint method is applied to the assimilation of the observed data into the sectional distribution of sea temperature to optimize the initial field.
以二维断面海温分布模型为例，利用海温实际观测数据，将变分伴随方法应用于断面海温初始场的优化。
The South China Sea ocean Database (SCSODB) collects and integrates multi-disciplinary ocean observational data, remote sensing data, model simulation data, assimilation data and various products.
南海海洋科学数据库数据资源包括多学科海洋观测数据，遥感数据，海洋模型模拟数据，同化数据以及各类数据产品。
The South China Sea ocean Database (SCSODB) collects and integrates multi-disciplinary ocean observational data, remote sensing data, model simulation data, assimilation data and various products.
南海海洋科学数据库数据资源包括多学科海洋观测数据，遥感数据，海洋模型模拟数据，同化数据以及各类数据产品。
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