模式约束三维变分资料同化技术及其在台风数值预报中的应用研究
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摘要
数值天气预报作为初边值问题,初始场的改进对于提高模式的整体预报性能具有重要的作用。近年来资料同化技术得到了长足的发展,其中具有代表性的是3DVar(三维变分同化)和4DVar(四维变分同化)技术及其业务化应用。但是在变分资料同化技术的应用中依然存在一些问题,其中包括背景误差协方差的确定,4DVar中伴随模式的建立,区域模式同化中边界条件的影响等。在应用中的问题还包括如何减小4DVar的计算量,如何在3D-和4D-Var中滤除由于观测资料的引入引起的高频扰动。本研究的目的即是通过引入模式弱约束条件提出了模式约束3DVar(MC-3DVar:Model constrained 3DVar)技术并建立了相应的同化系统,该技术一方面能从物理机制上滤除在同化过程中由于观测信息的引入导致的高频扰动,从而使同化结果中模式变量间更协调,另一方面相对于同样采用模式作为约束条件的4DVar来说,大大节省了计算资源。
论文在提出MC-3DVar技术,并建立了MC-3DVar同化系统的基础上进行了同化AMSU-A,QuikSCAT,卫星云导风资料的试验,最后进行了采用多种资料进行循环同化的试验。
本文结构如下:
第一章:绪论
对资料同化技术的发展进行了全面回顾,并指出需要解决的一些问题,在此基础上提出本文研究的主要目的和内容。
第二章:模式约束三维变分同化技术的提出
通过在4DVar中引入模式变量的时间倾向作为约束条件,从而引入了模式的动力和热力过程作为同化的弱约束条件。在将同化的时间窗口缩短到一个时间步长后,提出了模式约束3DVar技术(MC-3DVar)。通过浅水波方程模式对该技术进行了理想试验,试验结果表明了该技术的有效性。
第三章:模式约束三维变分同化技术的应用研究
利用MM5-4DVar系统的切线、伴随模式,在MM5模式基础上建立了MC-3DVar同化系统。使用该同化系统对2002年11个热带气旋进行了23次同化AMSU-A反演温度的试验。试验结果表明采用该技术同化AMSU-A反演温度资料具有明显的正效应。同时对Vongfong个例进行了同化AMSU-A反演温度、QuikSCAT海面风及卫星云导风的试验,结果表明,通过多种资料能更明显地提高预报效果。
第四章:台风个例的多种资料循环同化研究
由于MC-3DVar技术计算量相对较少,因此可以在有限的计算资源下进行循环同化。另一方面,在台风初始场形成技术中,通常的Bogus技术构造的台风涡旋由于与模式不能很好协调,因此需要在模式中进行较长时间的调整。而采用MC-3DVar技术不仅能通过同化观测资料改善对台风结构的描述,同时通过模式动力和热力过程的约束能使得初始场与模式更为协调,因此该章进行了采用多种观测资料对台风进行循环同化调整的试验。结果表明通过循环同化明显改善了对台风结构的描述,提高了路径预报精度。
第五章:结论及讨论
对各章的内容进行系统的归纳和总结,并指出还存在的问题以及今后要进一步开展的工作。
Data assimilation technique plays a very important role in numerical weather prediction (NWP) that is a typical problem of initial and boundary conditions. In the past decads, the 3D- and 4D-Var data assimilation techniques were improved quikly not only in the theoretical researches but also in the NWPs of operational centers. However, there are still some problems which limit the wide use of Variational data assimilation techniques. The open questions include how to define the coVariance matrix of background error, how to handle the boundary conditions, how to reduce the computing cost of 4DVar, how to eliminate the high-frequency disturbance which are incurred by the insert of observation information and so on. In this dissertation, the efforts are focused on improving the Variational data assimilation technique and applying of this method to numerical forecast of tropical cyclone.
In the review of the development of the data assimilation technique in Chapter 1, it is pointed out that a disadvantage of 3-dimensional Variational data assimilation (3DVar) technique is its lack of complicated constraints such as the dynamics and physics in a numerical model, which are used in the 4-dimensional Variational data assimilation (4DVar) technique. On the other hand, however, using a numerical model and its adjoint in the 4DVar technique requires a large amount of computer resources, and thus limits its practical applicability.
In Chapter 2, a new 3DVar method is proposed by adding a numerical model constraint. This method minimizes the distance between observation and model Variables and time tendency of model Variables, so that the optimized initial conditions not only fit the observations but also satisfy the constraints of full dynamics and physics of the numerical model. The forward and adjoint models used in this method are as same as those in the 4DVar method but are only integrated one time step to calculate the time tendency. Because observations are only used at one time slice and meanwhile model constraints are applied, the method is called the model-constrained 3DVar (MC-3DVar).
A set of ideal experiments based on a shallow water equation model indicates that the model constraints used in MC-3DVar can spread the observation information spatially and balance the model Variables.
In Chapter 3, the MM5 (National Center for Atmospheric Research/Penn State Mesoscale Model 5) MC-3DVar system is established using the same forward and adjoint models of MM5 4DVar system. In the MC-3DVar system, the forward and adjoint models are only integrated one time step to calculate the time tendency and gradient.
Using the MC-3DVar system, AMSU-A retrieved air temperatures are assimilated into 32 tropical cyclone (TC) cases. The results show a significant decrease in track forecast errors. Meanwhile, one case study of assimilating AMSU-A temperature, QuikSCAT sea-level winds, and cloud drift winds gives dramatic track error decreases. The study shows that the assimilation of these data with MC-3DVar improves TC forecasts and more satellite data give better performances.
In Chapter 4, an assimilation cycle is employed to improve the initial conditions using Various data at different time. A case study of typhoon Saomai (2006) from 20BST 8th to 08BST 9th Sep. is carried out in this chapter. The assimilated data include the Cloud Drift Wind, QuikSCAT sea level wind, Dropsonde and Bogus sea level pressure. The 12, 24, 36 and 48 h track forecast of Saomai is improved dramatically after assimilating of these data cycle, and the minimum 48 h track forecasting error is reached by combining the tropical cyclone vortex in the assimilated field and the background field of the output of global model.
Finally, the conclusions and discussions are given in Chapter 5.
论文在提出MC-3DVar技术,并建立了MC-3DVar同化系统的基础上进行了同化AMSU-A,QuikSCAT,卫星云导风资料的试验,最后进行了采用多种资料进行循环同化的试验。
本文结构如下:
第一章:绪论
对资料同化技术的发展进行了全面回顾,并指出需要解决的一些问题,在此基础上提出本文研究的主要目的和内容。
第二章:模式约束三维变分同化技术的提出
通过在4DVar中引入模式变量的时间倾向作为约束条件,从而引入了模式的动力和热力过程作为同化的弱约束条件。在将同化的时间窗口缩短到一个时间步长后,提出了模式约束3DVar技术(MC-3DVar)。通过浅水波方程模式对该技术进行了理想试验,试验结果表明了该技术的有效性。
第三章:模式约束三维变分同化技术的应用研究
利用MM5-4DVar系统的切线、伴随模式,在MM5模式基础上建立了MC-3DVar同化系统。使用该同化系统对2002年11个热带气旋进行了23次同化AMSU-A反演温度的试验。试验结果表明采用该技术同化AMSU-A反演温度资料具有明显的正效应。同时对Vongfong个例进行了同化AMSU-A反演温度、QuikSCAT海面风及卫星云导风的试验,结果表明,通过多种资料能更明显地提高预报效果。
第四章:台风个例的多种资料循环同化研究
由于MC-3DVar技术计算量相对较少,因此可以在有限的计算资源下进行循环同化。另一方面,在台风初始场形成技术中,通常的Bogus技术构造的台风涡旋由于与模式不能很好协调,因此需要在模式中进行较长时间的调整。而采用MC-3DVar技术不仅能通过同化观测资料改善对台风结构的描述,同时通过模式动力和热力过程的约束能使得初始场与模式更为协调,因此该章进行了采用多种观测资料对台风进行循环同化调整的试验。结果表明通过循环同化明显改善了对台风结构的描述,提高了路径预报精度。
第五章:结论及讨论
对各章的内容进行系统的归纳和总结,并指出还存在的问题以及今后要进一步开展的工作。
Data assimilation technique plays a very important role in numerical weather prediction (NWP) that is a typical problem of initial and boundary conditions. In the past decads, the 3D- and 4D-Var data assimilation techniques were improved quikly not only in the theoretical researches but also in the NWPs of operational centers. However, there are still some problems which limit the wide use of Variational data assimilation techniques. The open questions include how to define the coVariance matrix of background error, how to handle the boundary conditions, how to reduce the computing cost of 4DVar, how to eliminate the high-frequency disturbance which are incurred by the insert of observation information and so on. In this dissertation, the efforts are focused on improving the Variational data assimilation technique and applying of this method to numerical forecast of tropical cyclone.
In the review of the development of the data assimilation technique in Chapter 1, it is pointed out that a disadvantage of 3-dimensional Variational data assimilation (3DVar) technique is its lack of complicated constraints such as the dynamics and physics in a numerical model, which are used in the 4-dimensional Variational data assimilation (4DVar) technique. On the other hand, however, using a numerical model and its adjoint in the 4DVar technique requires a large amount of computer resources, and thus limits its practical applicability.
In Chapter 2, a new 3DVar method is proposed by adding a numerical model constraint. This method minimizes the distance between observation and model Variables and time tendency of model Variables, so that the optimized initial conditions not only fit the observations but also satisfy the constraints of full dynamics and physics of the numerical model. The forward and adjoint models used in this method are as same as those in the 4DVar method but are only integrated one time step to calculate the time tendency. Because observations are only used at one time slice and meanwhile model constraints are applied, the method is called the model-constrained 3DVar (MC-3DVar).
A set of ideal experiments based on a shallow water equation model indicates that the model constraints used in MC-3DVar can spread the observation information spatially and balance the model Variables.
In Chapter 3, the MM5 (National Center for Atmospheric Research/Penn State Mesoscale Model 5) MC-3DVar system is established using the same forward and adjoint models of MM5 4DVar system. In the MC-3DVar system, the forward and adjoint models are only integrated one time step to calculate the time tendency and gradient.
Using the MC-3DVar system, AMSU-A retrieved air temperatures are assimilated into 32 tropical cyclone (TC) cases. The results show a significant decrease in track forecast errors. Meanwhile, one case study of assimilating AMSU-A temperature, QuikSCAT sea-level winds, and cloud drift winds gives dramatic track error decreases. The study shows that the assimilation of these data with MC-3DVar improves TC forecasts and more satellite data give better performances.
In Chapter 4, an assimilation cycle is employed to improve the initial conditions using Various data at different time. A case study of typhoon Saomai (2006) from 20BST 8th to 08BST 9th Sep. is carried out in this chapter. The assimilated data include the Cloud Drift Wind, QuikSCAT sea level wind, Dropsonde and Bogus sea level pressure. The 12, 24, 36 and 48 h track forecast of Saomai is improved dramatically after assimilating of these data cycle, and the minimum 48 h track forecasting error is reached by combining the tropical cyclone vortex in the assimilated field and the background field of the output of global model.
Finally, the conclusions and discussions are given in Chapter 5.
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