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。
So the actual background error covariance is the key to success of data assimilation technique.
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。
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.
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 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.
近年来，卫星辐射资料在数值天气预报（NWP）系统中的直接同化研究取得了长足进展。
Recently, much progress has been made in the direct assimilation of satellite radiance measurements in numerical weather-prediction(NWP) system.
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。
Considering that the model is traditionally supposed to be exact and only initial data fields are amended in Adjoint Assimilation System (AAS).
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。
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.
然后，从采购和供应这两个方面，建立共同配送的模型，对调查资料系统分析并构筑物流共同化体系。
Then from two respects of purchasing and supplying this text sets up the model of joint distribution and construct to survey and analysis logistics system together.
另外，GPS测量可降水量本身可能存在系统误差，在将GPS资料同化入数值预报模式时应关注GPS资料本身的可靠性。
In addition, GPS observation itself maybe have system error, so it is needed to take into account GPS self-reliability when assimilating the GPS data into the numerical prediction model.
试验表明：在伴随模式同化系统中加入常规和非常规资料，可以改进初始场，从而改善预报场。
The results indicate that the adding of conventional and non-conventional observations into adjoint model system can improve the quality of initial field, thereof improve the result of forecasting.
试验表明：在伴随模式同化系统中加入常规和非常规资料，可以改进初始场，从而改善预报场。
The results indicate that the adding of conventional and non-conventional observations into adjoint model system can improve the quality of initial field, thereof improve the result of forecasting.
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