A framework for variational data assimilation with superparameterization
A framework for variational data assimilation with superparameterization
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DOI：
10.5194/npg-22-601-2015
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发表时间：
2015-01-01
影响因子：
2.2
通讯作者：
Lee, Y.
中科院分区：
文献类型：
--
作者：
Grooms, I.;Lee, Y.
超参数化（Superparameterization，SP）是一种多尺度的计算方法，其中大尺度大气或海洋模式耦合到嵌入到大尺度模式计算网格中的周期域上的小尺度动力学模拟阵列。SP已经成功地在全球大气和气候模式中得到了发展，并且是一种有前途的新应用方法，但是目前还没有可以与这些模式一起使用的实用数据同化框架。作者开发了一个3D-Var变分数据同化框架与SP使用，相对较低的成本和简单的3D-Var相比，合奏方法，使其成为一个自然适合相对昂贵的多尺度SP模式。为了在一个简单的模式中演示同化框架，作者开发了一个类似于双尺度Lorenz-'96模式的新的常微分方程系统。该系统具有一组表示为（Y-i）的变量，具有大尺度部分和小尺度部分，并且系统的SP近似是直接的。在新的同化框架下，SP模式能更准确地逼近真实系统的大尺度动力学。
Superparameterization (SP) is a multiscale computational approach wherein a large scale atmosphere or ocean model is coupled to an array of simulations of small scale dynamics on periodic domains embedded into the computational grid of the large scale model. SP has been successfully developed in global atmosphere and climate models, and is a promising approach for new applications, but there is currently no practical data assimilation framework that can be used with these models. The authors develop a 3D-Var variational data assimilation framework for use with SP; the relatively low cost and simplicity of 3D-Var in comparison with ensemble approaches makes it a natural fit for relatively expensive multiscale SP models. To demonstrate the assimilation framework in a simple model, the authors develop a new system of ordinary differential equations similar to the two-scale Lorenz-'96 model. The system has one set of variables denoted (Y-i), with large and small scale parts, and the SP approximation to the system is straightforward. With the new assimilation framework the SP model approximates the large scale dynamics of the true system accurately.