GSI 3DVar-Based Ensemble–Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments

Wang, Xuguang; Parrish, David; Kleist, Daryl; Whitaker, Jeffrey S.

doi:10.1175/mwr-d-12-00141.1

Cited by 258 publications

(243 citation statements)

References 43 publications

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“…Lorenc, 2003;Harlim and Hunt, 2007;Tian et al, 2008Tian et al, , 2011Wang et al, 2010;Zhang et al, 2009;Cheng et al, 2010;Zhang and Zhang, 2012;Wang et al, 2013). According to the classification suggested by Andrew C. Lorenc (http:// www.wcrp-climate.org/WGNE/BlueBook/2013/individualarticles/01_Lorenc_Andrew_EnVar_nomenclature.pdf), the existing composite methods in the literature can be roughly divided into three groups, namely, the 'hybrid' (e.g.…”

Section: Introductionmentioning

confidence: 99%

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

Tian

Feng

2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T This paper presents a novel non-linear least squares enhanced proper orthogonal decomposition (POD)-based 4DVar algorithm (referred as NLS-4DVar) for the non-linear ensemble-based 4DVar. In the algorithm, the GaussÁNewton iterative method is employed to handle the non-quadratic non-linearity of the 4DVar cost function while the overall structure of the algorithm still resembles the original POD-4DVar algorithm. It is proved that the original POD-4DVar algorithm is a special case of the proposed NLS-4DVar algorithm under the assumption of the linear relationship between the model perturbations (MPs) and the simulated observation perturbations (OPs). Under the assumption it is also shown that the solution of POD-4DVar algorithm coincides with the solution of the proposed NLS-4DVar algorithm. On the contrary, if the linear relationship assumption is dropped, the solution of the POD-4DVar algorithm is only the first iteration of the proposed NLS-4DVar algorithm. As a result, our analysis provides an explanation for the degraded and inaccurate performance of the POD-4DVar algorithm when the underlying forecast model or (and) the observation operator is strongly non-linear. The potential merits and advantages of the proposed NLS-4DVar are demonstrated by a group of Observing System Simulation Experiments with Advanced Research WRF (ARW) using accumulated rainfall-observations.

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Section: Introductionmentioning

confidence: 99%

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

Tian

Feng

2015

Tellus A: Dynamic Meteorology and Oceanography

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“…In order to improve the stability of the data assimilation system, traditional hybrid methods attempt to modify the gain matrix K i via the constituent background error covariance matrix P b (Hamill and Snyder, 2000;Wang et al, 2007aWang et al, , 2007bWang et al, , 2008aWang et al, , 2008bWang et al, , and 2013. A simpler and more direct approach is to apply additional contractions to the evolution operator (Penny 2014;Penny et al, 2015;Hamrud et al, 2014;and Bonavita et al, 2015).…”

Section: Hybrid-gain Data Assimilationmentioning

confidence: 99%

“…The nonlinear error dynamics are encoded into the forecast error covariance matrix to enable coupling of a potentially sparsely observed driver system with a numerical model as the response system. In NWP, this forecast error covariance information is either estimated from a long time-averaged history of the system's forecast errors (i.e., a climatology) typically denoted as B, produced adaptively to estimate the instantaneous "errors of the day" (Kalnay, 2003) typically denoted as P b , or some combination of the two (Hamill and Snyder, 2000;Wang et al, 2007aWang et al, , 2007bWang et al, , 2008aWang et al, , 2008bWang et al, , 2010Wang et al, , 2013Kleist 2012;Penny, 2014;Penny et al, 2015;Hamrud et al, 2014;and Bonavita et al, 2015). Such methods that combine static and dynamic error representations are typically referred to as hybrid methods and have recently been reviewed by Asch et al (2017) and Bannister (2017).…”

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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“…Although 3DVAR is still the dominant scheme for regional models, a GSI-based ensemble-variational hybrid data assimilation has also been developed [Wang et al, 2013]. Once the GSI-based hybrid scheme becomes available, it is straightforward to update the MS-DA methodology presented for use here.…”

Section: Ms-da Implementationmentioning

confidence: 99%

Development of fine‐resolution analyses and expanded large‐scale forcing properties: 1. Methodology and evaluation

Feng

Liu

et al. 2015

JGR Atmospheres

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We produce fine-resolution, three-dimensional fields of meteorological and other variables for the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The Community Gridpoint Statistical Interpolation system is implemented in a multiscale data assimilation (MS-DA) framework that is used within the Weather Research and Forecasting model at a cloud-resolving resolution of 2 km. The MS-DA algorithm uses existing reanalysis products and constrains fine-scale atmospheric properties by assimilating high-resolution observations. A set of experiments show that the data assimilation analysis realistically reproduces the intensity, structure, and time evolution of clouds and precipitation associated with a mesoscale convective system. Evaluations also show that the large-scale forcing derived from the fine-resolution analysis has an overall accuracy comparable to the existing ARM operational product. For enhanced applications, the fine-resolution fields are used to characterize the contribution of subgrid variability to the large-scale forcing and to derive hydrometeor forcing, which are presented in companion papers.

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GSI 3DVar-Based Ensemble–Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments

Cited by 258 publications

References 43 publications

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

Mathematical foundations of hybrid data assimilation from a synchronization perspective

Development of fine‐resolution analyses and expanded large‐scale forcing properties: 1. Methodology and evaluation

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