Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office

Clayton, Adam; Lorenc, Andrew C.; Barker, Dale

doi:10.1002/qj.2054

Cited by 329 publications

(352 citation statements)

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“…The use of ensembles allows the data assimilation to know about the day-to-day variability in the likely errors in the background forecast it uses. The current Met Office data-assimilation scheme makes limited use of the ensemble information (Clayton et al, 2012). In order to improve this link, one would envisage making a number of improvements to the ensemble initialization, which should make the perturbations more suited for use within data assimilation.…”

Section: Introductionmentioning

confidence: 99%

Tests of different flavours of EnKF on a simple model

Bowler

Flowerdew

Pring

2012

Quart J Royal Meteoro Soc

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The Met Office is currently developing an ensemble-based data-assimilation system for use in numerical weather prediction (NWP). In an effort to inform those developments, a simple model has been used to compare a number of different flavours of ensemble Kalman filter (EnKF). As in many previous studies, deterministic (square-root) filters are seen to perform better than stochastic (perturbed-observations) filters. Two previously proposed methods for mitigating the effects of perturbing the observations were tested. These modified stochastic filters improved performance, but were still worse than the deterministic filters. Very little difference was seen between the performance of the various deterministic filters tested.The typical approach to localization in the Local Ensemble Transform Kalman Filter (LETKF) is based solely on the distance between each observation and the model point being updated. It takes no account of the inter-observation distance and we argue that this is incorrect. However, the difference between covariance localization and observation localization is only relevant when the assimilation is not weak and hence it did not affect performance in these tests. If a hybrid covariance matrix is used to update the 'deterministic' forecast around which the ensemble is centred, then using the same covariance matrix when updating the perturbations is seen to be beneficial in a stochastic filter. This is not the case in the squareroot filter, due to restrictions imposed by its formulation. Tests with a nonlinear observation operator indicate better behaviour when observations are assimilated simultaneously rather than sequentially. An ensemble of variational minimizations has some advantages in this situation. These results, along with other considerations, have led the Met Office to develop a 4-dimensional ensemble-variational (4D-EnVar) system for data assimilation and ensemble generation.

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Section: Introductionmentioning

confidence: 99%

Tests of different flavours of EnKF on a simple model

Bowler

Flowerdew

Pring

2012

Quart J Royal Meteoro Soc

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“…Note that there are data assimilation schemes more advanced than 3DVAR, such as four-dimensional variational data assimilation (4DVAR) and 3/4DVAR-based ensemble-variational hybrid data assimilation [Lorenc, 2003;Clayton et al, 2013]. 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].…”

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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“…Therefore, variational estimation/DA is a method based on the optimal control theory, which can also be understood as a special case of the maximum a-posteriory probability (MAP) estimator [8]. This method is preferred for weather and ocean forecasting in major operational centers around the globe, particularly in the form of the incremental 4D-Var [7], and in the form of the ensemble 4D-Var [6]. Variational estimation is widely used in other scientific and engineering applications, such as aerospace engineering [2] and astrophysics [4], to mention a few.…”

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Hessian-based covariance approximations in variational data assimilation

Gejadze

Shutyaev

Dimet

2018

Russian Journal of Numerical Analysis and Mathematical Modelling

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Abstract:The problem of variational data assimilation (estimation) for a nonlinear model is considered in general operator formulation. Hessian-based methods are presented to compute the estimation error covariances. The importance of dynamic formulation and the role of the Hessian and its inverse are discussed. Keywords:Variational data assimilation, optimality system, Hessian, estimation error covariance. MSC 2010: 65K10, 86A22, 93B40Data assimilation (state or parameter estimation) for high-dimensional distributed parameter dynamical systems has become a powerful analysis tool in recent decades. It is a fusion process of incomplete and, possibly, indirect observations of state variables with a mathematical model governed by partial differential equations, complemented by a priori information. The applications include model initialization in meteorology and oceanography, air and water quality monitoring, 'calibration' of groundwater and reservoir models, discharge estimation and forecasting in river hydraulics and hydrology, flow estimation and control in aerospace engineering, process control in chemical and nuclear engineering, etc. In different applications these estimation problems are also referred as 'inverse problems', 'data assimilation' (DA), and 'calibration'. Variational methods were introduced in meteorology by Sasaki [27]. These methods consider the equations governing the flow as constraints and the problem is closed by using a variational principle, e.g., the minimization of the discrepancy between the model prediction and the observations. Optimal Control Approach ( [30]. Therefore, variational estimation/DA is a method based on the optimal control theory, which can also be understood as a special case of the maximum a-posteriory probability (MAP) estimator [8]. This method is preferred for weather and ocean forecasting in major operational centers around the globe, particularly in the form of the incremental 4D-Var [7], and in the form of the ensemble 4D-Var [6]. Variational estimation is widely used in other scientific and engineering applications, such as aerospace engineering [2] and astrophysics [4], to mention a few.Uncertainty quantification is an important topic closely associated with estimation and data assimilation. An overview of the original works by the authors on the Hessian-based advanced methodology for computing the estimation error covariances in the framework of variational estimation is presented in the coming sections. The main results are given in a general operator formulation. The accent is made on feasibility of the suggested methods for the uncertainty quantification in high-dimensions, where the statistical methods (Monte Carlo involving associated tricks, e.g., localization, importance sampling, etc.) may not produce a sensible outcome due to a very small sample being available. Some of the approaches could equally be useful for improving (in terms of acceleration, memory savings and robustness) the estimation/DA technique itself.

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Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office

Abstract: A particular problem encountered during development was significantly poorer tropical verification scores when measured against own analyses. In contrast, verification against independent (ECMWF) analyses gave scores that were much more consistent with those against observations.

Cited by 329 publications

References 46 publications

Tests of different flavours of EnKF on a simple model

Tests of different flavours of EnKF on a simple model

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

Hessian-based covariance approximations in variational data assimilation

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