权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Conservation laws and ensemble Kalman filter algorithms

守恒定律和集成卡尔曼滤波器算法

基本信息

批准号：
261092378
负责人：
Professorin Dr. Tijana Janjic Pfander
金额：
--
依托单位：
Mathematisches Institut für Maschinelles Lernen und Data Science
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/261092378?language=en
关键词：
Conservation laws ensemble Kalman filter

项目摘要

Maintaining physical conservation laws numerically has long been recognized as being important in the development of numerical weather prediction (NWP) models independent of their resolution. In the broader context of data assimilation, however, concerted efforts to maintain conservation laws numerically and to understand the significance of doing so have begun only recently. The numerical models of the atmosphere that resolve highly nonlinear dynamics and physics are very sensitive to proper initial and boundary conditions. Consequently, data assimilation for NWP models that resolve many scales of motion and for observations of higher temporal/spatial density/resolution requires re-evaluating and improving methodology that is currently inherited from less nonlinear applications.The principal objective of this project is to develop an ensemble-based data assimilation algorithm that replicates properties of nonlinear dynamical systems such as conservation of mass, angular momentum, energy and enstrophy. Two problems will be addressed. First, conservation of mass and preservation of positivity have been shown in recent work by the principal investigator and colleagues to be important constraints for data assimilation algorithms. These two constraints have been incorporated into a new algorithm, the quadratic programming ensemble Kalman filter and tested for linear dynamics. In this project, the algorithm will be extended, implemented and tested with the non-hydrostatic, convection permitting COSMO-DE model in an idealized setup for the assimilation of radar reflectivity. As the second problem that will be addressed, it will be examined how data assimilation algorithms such as the ensemble Kalman filter and the quadratic programming ensemble Kalman filter affect the conservation properties in idealized nonlinear 2d shallow water model experiments and whether and how these ensemble based algorithms can be modified to obtain solutions that conserve angular momentum, energy and enstrophy. The conservation of energy and enstrophy is expected to improve the nonlinear energy cascade in the system. The possible impact on the accuracy of prediction will be examined as well.

长期以来，在数值天气预报（NWP）模式的发展中，在数值上保持物理守恒定律一直被认为是独立于其分辨率的重要因素。然而，在更广泛的数据同化背景下，维持数值守恒定律和理解这样做的意义的一致努力直到最近才开始。解决高度非线性动力学和物理问题的大气数值模型对适当的初始条件和边界条件非常敏感。因此，对于解决许多运动尺度和更高时间/空间密度/分辨率观测的NWP模型的数据同化，需要重新评估和改进目前从较少非线性应用中继承的方法。该项目的主要目标是开发一种基于集成的数据同化算法，该算法可以复制非线性动力系统的特性，如质量守恒、角动量、能量和熵。解决两个问题。首先，在最近的研究中，主要研究者和同事们已经证明质量守恒和正性守恒是数据同化算法的重要约束。这两个约束已被纳入一个新的算法，二次规划集合卡尔曼滤波，并测试了线性动力学。在本项目中，该算法将在同化雷达反射率的理想设置中，使用非流体静力、对流允许cosmos - de模型进行扩展、实施和测试。作为将要解决的第二个问题，将研究数据同化算法（如集合卡尔曼滤波器和二次规划集合卡尔曼滤波器）如何影响理想非线性二维浅水模型实验中的守恒特性，以及是否以及如何修改这些基于集合的算法以获得守恒角动量，能量和熵的解。能量和熵的守恒有望改善系统中的非线性能量级联。对预测准确性可能产生的影响也将进行研究。