数据同化：基于初始条件依赖的两种不同观点
Data Assimilation: Two Different Perspectives Based on the Initial-Condition Dependence
摘要：
数据同化（DA）是一种计算工具，它使用来自模型和实际测量的值来达到最佳可接受值。相反，这种技术依赖于卡尔曼增益的思想。我们指出，DA有两个不同的角度基于问题的类型。在本文中，我们研究了两种类型的问题：一个不依赖于初始条件，另一个是初始条件依赖。数据同化演示了两个例子：径流监测和预测在达卡市（初始条件独立）和对流在大气中（初始条件依赖）。我们表明，标准DA工程以及没有初始条件依赖的问题和分段DA是利用当问题具有初始条件依赖。在第一个例子中，我们利用标准的数据同化来获得比模型和观测值更真实的值。第二个例子是，我们设计了一种方法，在数据同化框架中以分段方式找到系统的动态，并注意到数据同化的动态（即使是由于嘈杂的初始条件）在未来合理的时间范围内与真实动态非常一致。
英文摘要：
Data Assimilation (DA) is a computational tool that uses value from the model and the real measurement to arrive to an optimally acceptable value. Rather, this technique relies on the idea of Kalman gain. We point out that DA has two different perspectives based on the type of problem. In this paper, we look into two problem types: one that does not rely on the initial condition, and the other that is initial condition dependent. Data Assimilation is demonstrated on two examples: runoff monitoring and forecasting in the city of Dhaka (initial condition independent) and convection in the atmosphere (initial condition dependent). We show that standard DA works well for problems with no initial condition dependence and piecewise DA is to be utilized when the problem has initial condition dependence. In the first example, we exploited standard Data Assimilation to arrive at values that are more realistic than the ones from the model and the observations. The second example is where we devised a method to find the dynamics of the system in a piecewise manner in Data Assimilation framework and noticed that the data assimilated dynamics (even due to noisy initial condition) is in good agreement with the true dynamics for a reasonable extent of time in future.
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