The Initialization of Numerical Models by a Dynamic-Initialization Technique

Hoke, James E.; Anthes, Richard A.

doi:10.1175/1520-0493(1976)104<1551:tionmb>2.0.co;2

Cited by 310 publications

(194 citation statements)

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“…The term 'indiscrimnate nudging' is thus not as commonly used as 'spectral nudging'. Salameh et al(2010) use the term 'indiscriminate nudging', Anthes (1974), Hoke and Anthes (1976) and Stauffer and Seaman (1990) refer to 'data assimilation', and other synonymous terminologies exist such as 'dynamical relaxation' (Davies and Turner, 1977) or 'grid' or 'analysis nudging'. In the following we will use 'indiscriminate nudging'.…”

Section: Introductionmentioning

confidence: 99%

Investigation of indiscriminate nudging and predictability in a nested quasi‐geostrophic model

Omrani

Drobinski

Dubos

2011

Quart J Royal Meteoro Soc

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In this work, we consider the effect of indiscriminate nudging time on an idealized high-resolution global model (GM) and limited-area model (LAM) simulations. The model used is a two-layer quasi-geostrophic model on the beta-plane.The effect of nudging is studied as a function of the predictability time, following a 'Big Brother' experimental approach: a high-resolution 'global' model is used to generate a 'reference run'. These fields are filtered afterwards to remove small scales and provide the coarse-resolution fields which are used to drive the high-resolution GM and the LAM. Comparison of the reference fields and the high-resolution runs over the same region allows the estimation of the ability of the high-resolution GM and LAM to regenerate the removed small scales. This fully nonlinear set-up mimics the configuration used for regional high-resolution atmospheric modelling.For the high-resolution GM, the results show that the behaviour of the nudged model depends primarily on the ratio of the nudging time to the predictability time. When the nudging time is very small compared to the predictability time, the model reproduces the large scale used to drive the model. On the other hand, if the nudging time is close to or larger than the predictability time, the nudging effect is weak and both large and small scales are poorly reproduced compared to the reference fields. The best result is obtained with a nudging time close to half the predictability time. This technique clearly improves the model capacity to reproduce the reference fields.For the high-resolution LAM, our results show that for a sufficiently small domain the simulation is largely controlled by the lateral boundary conditions (LBCs) and is quasi-insensitive to nudging. However, if the domain size exceeds a few Rossby radii, the high-resolution LAM becomes sensitive to initial conditions and the control exerted by LBCs becomes insufficient to prevent a divergence from the driving fields. Although the reconstructed fine scales are significantly damped, they are surprisingly well correlated to their reference values in a deterministic sense, not a statistical sense. Copyright c 2011 Royal Meteorological Society Key Words: downscaling; uncertainties; large/small-scale perturbations; potential vorticity

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Section: Introductionmentioning

confidence: 99%

Investigation of indiscriminate nudging and predictability in a nested quasi‐geostrophic model

Omrani

Drobinski

Dubos

2011

Quart J Royal Meteoro Soc

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“…(Kuo et al, 1993;Errico et al, 2000). 자료동화 기법은 연속수정법(successive correction method; Bergthórsson and Döös, 1955;Cressman, 1957;Barnes, 1964;Brandes, 1975), Nudging (Hoke and Anthes, 1976;Stauffer et al, 1991), Variational method (Grecu and Krajewski, 2001), Kalman Filtering (Caya et al, 2005;Chumchean et al, 2006) 등과 같이 많은 방법들이 개발되고 응용되어 왔으며 최 근 수문 분야에서도 그에 관한 연구가 활발하게 진행되 고 있다 (Houser et al, 1998;Entekhabi et al, 1999;Crow and Wood;2003) …”

Section: 서 론unclassified

Synthesis of Radar Measurements and Ground Measurements using the Successive Correction Method(SCM)

Kim¹,

Choi²,

Yoo³

2008

Journal of Korea Water Resources Association

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This study investigated the application of the successive correction method(SCM), a simple data assimilation method, for synthesizing the radar and rain gauge data. First, the number of iteration and influence radius for the SCM application were decided based on their sensitivity analysis. Also, for the evaluation of synthetic rainfall, the distributed rainfall field using the dense rainfall gauge network was assumed to be the true one. The synthetic rainfall field based on the SCM was also compared quantitatively with the one based on the co-Kriging frequently used nowadays. As the results, the SCM, a simple and economical data assimilation method, was found to secure the accuracy and statistical characteristics of the co-Kriging application.

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“…An early data assimilation approach called "nudging" (e.g., see Kistler, 1974 andHoke andAnthes, 1976) is very similar to the direct insertion of observations used in many synchronization studies. Nudging adds a forcing term to the prognostic equations, which relaxes the dynamics toward the observed value with a timescale specified by a tuning parameter.…”

Section: Introductionmentioning

confidence: 99%

Mathematical foundations of hybrid data assimilation from a synchronization perspective

Penny

2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The state-of-the-art data assimilation methods used today in operational weather prediction centers around the world can be classified as generalized one-way coupled impulsive synchronization. This classification permits the investigation of hybrid data assimilation methods, which combine dynamic error estimates of the system state with long time-averaged (climatological) error estimates, from a synchronization perspective. Illustrative results show how dynamically informed formulations of the coupling matrix (via an Ensemble Kalman Filter, EnKF) can lead to synchronization when observing networks are sparse and how hybrid methods can lead to synchronization when those dynamic formulations are inadequate (due to small ensemble sizes). A large-scale application with a global ocean general circulation model is also presented. Results indicate that the hybrid methods also have useful applications in generalized synchronization, in particular, for correcting systematic model errors. Data assimilation is a mathematical discipline that arose out of the development of numerical weather prediction (NWP), which required initialization of numerical models based on a set of relatively sparse observations. In recent years, popular solution approaches for this state estimation problem have evolved into two main categories: variational methods and ensemble-based Kalman filters. As each approach has its own unique benefits, researchers began creating hybrid combinations of these methods to take advantage of the strengths of both. We describe how data assimilation can be interpreted as a type of synchronization problem in which a modeled system is driven by observations of a natural system and extend this formalism to include the aforementioned hybrid data assimilation techniques.

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The Initialization of Numerical Models by a Dynamic-Initialization Technique

Cited by 310 publications

References 0 publications

Investigation of indiscriminate nudging and predictability in a nested quasi‐geostrophic model

Investigation of indiscriminate nudging and predictability in a nested quasi‐geostrophic model

Synthesis of Radar Measurements and Ground Measurements using the Successive Correction Method(SCM)

Mathematical foundations of hybrid data assimilation from a synchronization perspective

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