Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)

Gustafsson, Nils; Bojarova, Jelena

doi:10.5194/npg-21-745-2014

Cited by 29 publications

(24 citation statements)

References 50 publications

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“…For a LAM, and similarly to Gustafsson et al s*() and Gustafsson and Bojarova (), we use the inverse bi‐Fourier transform S −1 , such as:

boldC = S^{- 1} C_{s} S^{- T} .

…”

Section: Data Assimilation Methodssupporting

confidence: 81%

“…This approach seems particularly interesting for applications at convective scale, as it allows avoidance of the linearization of strongly nonlinear processes such as those of the microphysical parametrizations, which is the main reason why running a 4D‐Var still is a difficult task at those resolutions. Such a 4DEnVar has been successfully tested in a LAM context by Gustafsson and Bojarova () and is applied operationally at Environment Canada (Caron et al s*()), however both systems produce analysis increments with rather coarse spatial resolutions over 30 km.…”

Section: Introductionmentioning

confidence: 99%

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A 3D ensemble variational data assimilation scheme for the limited‐area AROME model: Formulation and preliminary results

Montmerle

Michel

Arbogast

et al. 2018

Quart J Royal Meteoro Soc

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This paper presents the formulation and preliminary results of a 3D ensemble‐variational data assimilation algorithm (3DEnVar) for the AROME‐France model at 3.8 km horizontal resolution. This algorithm is a deterministic, variational data assimilation scheme that uses background‐error covariances sampled from an ensemble. Our ensemble is an ensemble of data assimilation (EDA) at convective scale, based on the same system with the same spatial resolutions. In ensemble schemes, localization of the covariances is necessary to filter sampling noise. Two different localization schemes have been implemented, one in spectral space and one in grid‐point space. We also evaluate hybrid formulations, where the background‐error covariances are a weighted linear combination of the sampled covariances with the climatological ones. Cycled experiments are performed over a five‐week time period with 3 h updates. The 3DEnVar scheme largely outperforms standard 3D‐Var in terms of forecast scores. The best experiment is the one with the grid‐point localization scheme. A diagnostic of objective localization can provide guidance about the horizontal and vertical localization lengths that give best performance. The hybrid configuration with 80% of ensemble covariances and 20% of climatological ones performs also significantly better than the 3D‐Var, but to a lesser extent than the best 3DEnVar configuration, although it has better balanced initial fields.

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“…For a LAM, and similarly to Gustafsson et al s*() and Gustafsson and Bojarova (), we use the inverse bi‐Fourier transform S −1 , such as:

boldC = S^{- 1} C_{s} S^{- T} .

…”

Section: Data Assimilation Methodssupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

A 3D ensemble variational data assimilation scheme for the limited‐area AROME model: Formulation and preliminary results

Montmerle

Michel

Arbogast

et al. 2018

Quart J Royal Meteoro Soc

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show abstract

“…However, the left-multiplied form will require the minimization process to be done in state vector space which is more computationally demanding. Another possible approach would be to introduce a static full-rank B and to apply a 3DVar-FGAT in order to add more directions in the incremental field which is not solely covered by the ensemble B as suggested in Gustafsson and Bojarova (2014). This will in fact add a hybrid taste in our EnVar scheme, which constitutes an interesting perspective for advanced study.…”

Section: D Case Results Synthetic Observations and Real Datamentioning

confidence: 99%

“…Since the scheme is formulated on variational base, the transition is straightforward. Adding a static full-rank B and applying a 3DVar-FGAT (Gustafsson and Bojarova, 2014) can also be beneficial to the posterior ensemble quality as more directions are permitted to grow in the incremental field. This path constitutes another promising direction to follow.…”

Section: Discussionmentioning

confidence: 99%

High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar

Yin¹,

Mémin

2017

Tellus A: Dynamic Meteorology and Oceanography

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In this paper, we explore a dynamical formulation allowing to assimilate high-resolution data in a large-scale fluid flow model. This large-scale formulation relies on a random modelling of the small-scale velocity component and allows to take into account the scale discrepancy between the dynamics and the observations. It introduces a subgrid stress tensor that naturally emerges from a modified Reynolds transport theorem adapted to this stochastic representation of the flow. This principle is used within a stochastic shallow water model coupled with an 4DEnVar assimilation technique to estimate both the flow initial conditions and the inhomogeneous time-varying subgrid parameters. The performance of this modelling has been assessed numerically with both synthetic and real-world data. Our strategy has shown to be very effective in providing a more relevant prior/posterior ensemble in terms of the dispersion compared to other tests using the standard shallow water equations with no subgrid parameterization or with simple eddy viscosity models. We also compared two localization techniques. The results indicate the localized covariance approach is more suitable to deal with the scale discrepancy-related errors.

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“…In fact, these new models must be able to make a quantum leap forward in forecast skill, by being able to efficiently utilize the latest developments in data assimilation (e.g., 4D-En-Var [45]), scale-aware physical parameterizations, and, as just mentioned, modern HPC technologies (e.g., GPU/MIC). From this perspective, several international and interagency activities have been organized to select best candidates for next-generation models.…”

Section: Overview Of Operational Time-integrationmentioning

confidence: 99%

Current and Emerging Time-Integration Strategies in Global Numerical Weather and Climate Prediction

Mengaldo

Wyszogrodzki

Diamantakis

et al. 2018

Arch Computat Methods Eng

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The continuous partial differential equations governing a given physical phenomenon, such as the Navier-Stokes equations describing the fluid motion, must be numerically discretized in space and time in order to obtain a solution otherwise not readily available in closed (i.e., analytic) form. While the overall numerical discretization plays an essential role in the algorithmic efficiency and physically-faithful representation of the solution, the time-integration strategy commonly is one of the main drivers in terms of cost-to-solution (e.g., time-or energy-to-solution), accuracy and numerical stability, thus constituting one of the key building blocks of the computational model. This is especially true in time-critical applications, including numerical weather prediction (NWP), climate simulations and engineering. This review provides a comprehensive overview of the existing and emerging time-integration (also referred to as time-stepping) practices used in the operational global NWP and climate industry, where global refers to weather and climate simulations performed on the entire globe. While there are many flavors of time-integration strategies, in this review we focus on the most widely adopted in NWP and climate centers and we emphasize the reasons why such numerical solutions were adopted. This allows us to make some considerations on future trends in the field such as the need to balance accuracy in time with substantially enhanced time-to-solution and associated implications on energy consumption and running costs. In addition, the potential for the co-design of time-stepping algorithms and underlying high performance computing hardware, a keystone to accelerate the computational performance of future NWP and climate services, is also discussed in the context of the demanding operational requirements of the weather and climate industry.

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Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)

Abstract: Abstract. A four-dimensional ensemble variational (4D-En

Cited by 29 publications

References 50 publications

A 3D ensemble variational data assimilation scheme for the limited‐area AROME model: Formulation and preliminary results

A 3D ensemble variational data assimilation scheme for the limited‐area AROME model: Formulation and preliminary results

High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar

Current and Emerging Time-Integration Strategies in Global Numerical Weather and Climate Prediction

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