The 10,240‐member ensemble Kalman filtering with an intermediate AGCM

Miyoshi, Takemasa; Kondo, Keiichi; Imamura, Toshiyuki

doi:10.1002/2014gl060863

Cited by 92 publications

(82 citation statements)

References 19 publications

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“…However, the histogram for specific humidity (which was not assimilated directly) was far from Gaussian ( Fig. 5f ), which is consistent with the result of ensemble experiments with 10,240 members (Miyoshi et al 2014).…”

Section: Observation System Simulation Experimentssupporting

confidence: 86%

Comparison between Four-Dimensional LETKF and Ensemble-Based Variational Data Assimilation with Observation Localization

Yokota

Kunii

Aonashi

et al. 2016

SOLA

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In data assimilation for weather forecast, ensemble Kalman filter assumes linearity of the observation operator and Gaussianity of the probability distribution function (PDF) to explicitly solve the analysis. As a method avoiding errors based on these assumptions, we describe a four-dimensional ensemble-based variational method (4D-EnVAR) with observation localization. This formulation differs from that of the four-dimensional local ensemble transform Kalman filter (4D-LETKF) only in two points: (1) not assuming linearity of the observation operator and (2) calculating it globally. Using single-observation assimilation experiments and the observation system simulation experiments with a low-resolution atmospheric general circulation model, we demonstrate that 4D-EnVAR with observation localization has an advantage over 4D-LETKF because the observation operator is globally calculated in EnVAR.(Citation: Yokota, S., M. Kunii, K. Aonashi, and S. Origuchi, 2016: Comparison between four-dimensional LETKF and ensemble-based variational data assimilation with observation localization. SOLA, 12, 80−85,

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Section: Observation System Simulation Experimentssupporting

confidence: 86%

Comparison between Four-Dimensional LETKF and Ensemble-Based Variational Data Assimilation with Observation Localization

Yokota

Kunii

Aonashi

et al. 2016

SOLA

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“…The NCEP global hybrid‐4DEnVar system uses an 80‐member ensemble and

β_{c}^{2} = 0.125

β_{e}^{2} = 0.875

(Kleist, personal communication, 2017). In EnKF systems (without any static covariances) Houtekamer et al () have shown benefit from ensembles with up to 384 members, and research systems are testing much larger ensembles (Miyoshi et al ).…”

Section: Discussionmentioning

confidence: 99%

A comparison of hybrid variational data assimilation methods for global NWP

Lorenc

Jardak

2018

Quart J Royal Meteoro Soc

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Variational data assimilation methods are reviewed and compared in the Met Office global numerical weather prediction system. This supports hybrid background‐error covariances which are a weighted combination of modelled static “climatological” covariances with covariances calculated from a current ensemble of forecasts, in both three‐ and four‐dimensional methods. For the latter, we compare the use of linear and adjoint models (hybrid‐4DVar) with the direct use of ensemble forecast trajectories (hybrid‐4DEnVar). Earlier studies had shown that hybrid‐4DVar outperforms hybrid‐4DEnVar, and 4DVar outperforms 3DVar. Improvements in the processing of ensemble covariances and computer enhancements mean we are now able to explore these comparisons for the full range of hybrid weights. We find that, using our operational 44‐member ensemble, the static covariance is still beneficial in hybrid‐4DVar, so that it significantly outperforms hybrid‐4DEnVar. In schemes not using linear and adjoint models, the static covariance is less beneficial. It is shown that the time‐propagated static covariance is the main cause of the better performance of 4DVar; when using pure ensemble covariances, 4DVar and 4DEnVar show similar skill. These results are consistent with nonlinear dynamics theory about assimilation in the unstable subspace.

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“…However, at least two limitations are on the horizon for EnKFs. Perhaps counterintuitively, these limitations arise due to increased computational resources, and have already become challenges at the RIKEN Advanced Institute for Computational Science (AICS, e.g., Miyamoto et al, 2013;Miyoshi et al, 2014Miyoshi et al, , 2015. First, global models will be pushed to higher resolutions in which they begin to resolve highly nonlinear processes.…”

Section: Introductionmentioning

confidence: 99%

A local particle filter for high-dimensional geophysical systems

Penny

Miyoshi

2016

Nonlin. Processes Geophys.

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Abstract. A local particle filter (LPF) is introduced that outperforms traditional ensemble Kalman filters in highly nonlinear/non-Gaussian scenarios, both in accuracy and computational cost. The standard sampling importance resampling (SIR) particle filter is augmented with an observation-space localization approach, for which an independent analysis is computed locally at each grid point. The deterministic resampling approach of Kitagawa is adapted for application locally and combined with interpolation of the analysis weights to smooth the transition between neighboring points. Gaussian noise is applied with magnitude equal to the local analysis spread to prevent particle degeneracy while maintaining the estimate of the growing dynamical instabilities. The approach is validated against the local ensemble transform Kalman filter (LETKF) using the 40-variable Lorenz-96 (L96) model. The results show that (1) the accuracy of LPF surpasses LETKF as the forecast length increases (thus increasing the degree of nonlinearity), (2) the cost of LPF is significantly lower than LETKF as the ensemble size increases, and (3) LPF prevents filter divergence experienced by LETKF in cases with non-Gaussian observation error distributions.

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The 10,240‐member ensemble Kalman filtering with an intermediate AGCM

Cited by 92 publications

References 19 publications

Comparison between Four-Dimensional LETKF and Ensemble-Based Variational Data Assimilation with Observation Localization

Comparison between Four-Dimensional LETKF and Ensemble-Based Variational Data Assimilation with Observation Localization

A comparison of hybrid variational data assimilation methods for global NWP

A local particle filter for high-dimensional geophysical systems

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