Applications of the Mean Recentering Scheme to Improve Typhoon Track Prediction: A Case Study of Typhoon Nanmadol (2011)

Chang, Chih Chien; Yang, Shun; Keppenne, Christian L.

doi:10.2151/jmsj.2014-604

Cited by 9 publications

(9 citation statements)

References 59 publications

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“…Differences in topography or in physical parameterizations could be a source of imbalance for the low-resolution ensemble runs, because the integrations starting from the recentered analyses will have to converge back to the attractor of the low-resolution forecast model. A local procedure of the same type is to relocate the initial condition of a tropical cyclone to, or close to, an observed position (Chang et al 2014a). The impact of recentering on balance properties such as small-scale variability, jumpiness, and precipitation spinup has been investigated by Lang et al (2015).…”

Section: Reduction Of Sources Of Imbalancementioning

confidence: 99%

Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation

Houtekamer

Zhang

2016

Monthly Weather Review

506

424

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This paper reviews the development of the ensemble Kalman filter (EnKF) for atmospheric data assimilation. Particular attention is devoted to recent advances and current challenges. The distinguishing properties of three well-established variations of the EnKF algorithm are first discussed. Given the limited size of the ensemble and the unavoidable existence of errors whose origin is unknown (i.e., system error), various approaches to localizing the impact of observations and to accounting for these errors have been proposed. However, challenges remain; for example, with regard to localization of multiscale phenomena (both in time and space). For the EnKF in general, but higher-resolution applications in particular, it is desirable to use a short assimilation window. This motivates a focus on approaches for maintaining balance during the EnKF update. Also discussed are limited-area EnKF systems, in particular with regard to the assimilation of radar data and applications to tracking severe storms and tropical cyclones. It seems that relatively less attention has been paid to optimizing EnKF assimilation of satellite radiance observations, the growing volume of which has been instrumental in improving global weather predictions. There is also a tendency at various centers to investigate and implement hybrid systems that take advantage of both the ensemble and the variational data assimilation approaches; this poses additional challenges and it is not clear how it will evolve. It is concluded that, despite more than 10 years of operational experience, there are still many unresolved issues that could benefit from further research. Contents Introduction...4490 Popular flavors of the EnKF algorithm...4491 General description...4491 Stochastic and deterministic filters...4492 The stochastic filter...4492 The deterministic filter...4492 Sequential or local filters...4493 Sequential ensemble Kalman filters...4493 The local ensemble transform Kalman filter...4494 Extended state vector...4494 Issues for the development of algorithms...4495 Use of small ensembles...4495 Monte Carlo methods...4495 Validation of reliability...4497 Use of group filters with no inbreeding...4498 Sampling error due to limited ensemble size: The rank problem...4498 Covariance localization...4499 Localization in the sequential filter...4499 Localization in the LETKF...4499 Issues with localization...4500 Summary...4501 Methods to increase ensemble spread...4501 Covariance inflation...4501 Additive inflation...4501 Multiplicative inflation...4502 Relaxation to prior ensemble information...4502 Issues with inflation...4503 Diffusion and truncation...4503 Error in physical parameterizations...4504 Physical tendency perturbations...4504 Multimodel, multiphysics, and multiparameter approaches...4505 Future directions...4505 Realism of error sources...4506 Balance and length of the assimilation window...4506 The need for balancing methods...4506 Time-filtering methods...4506 Toward shorter assimilation windows...4507 Reduction of sources of imbalance...4507 Regional data assimilation...4508 Boundary conditions and consistency across multiple domains...4509 Initialization of the starting ensemble...4510 Preprocessing steps for radar observations...4510 Use of radar observations for convective-scale analyses...4511 Use of radar observations for tropical cyclone analyses...4511 Other issues with respect to LAM data assimilation...4511 The assimilation of satellite observations...4512 Covariance localization...4512 Data density...4513 Bias-correction procedures...4513 Impact of covariance cycling...4514 Assumptions regarding observational error...4514 Recommendations regarding satellite observations...4515 Computational aspects...4515 Parameters with an impact on quality...4515 Overview of current parallel algorithms...4516 Evolution of computer architecture...4516 Practical issues...4517 Approaching the gray zone...4518 Summary...4518 Hybrids with variational and EnKF components...4519 Hybrid background error covariances...4519 E4DVar with the α control variable...4519 Not using linearized models with 4DEnVar...4520 The hybrid gain algorithm...4521 Open issues and recommendations...4521 Summary and discussion...4521 Stochastic or deterministic filters...4522 The nature of system error...4522 Going beyond the synoptic scales...4522 Satellite observations...4523 Hybrid systems...4523 Future of the EnKF...4523 APPENDIX A...4524 Types of Filter Divergence...4524 Classical filter divergence...4524 Catastrophic filter divergence...4524 APPENDIX B...4524 Systems Available for Download...4524 References...4525

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Section: Reduction Of Sources Of Imbalancementioning

confidence: 99%

Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation

Houtekamer

Zhang

2016

Monthly Weather Review

506

424

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“…1. In view of the previous studies (e.g., Keil and Craig 2007;Chang et al 2014), the best member of an ensemble prediction is more reliable and superior to some deterministic forecasts, or the ensemble-mean forecast. In this work, we selected the five most (best) and least (worst) representative ensemble members from a 100-member ensemble hindcast experiment through sequentially adopting three criteria.…”

Section: Introductionmentioning

confidence: 84%

The Role of Stochastic Model Error Perturbations in Predicting the 2011/12 Double-Dip La Niña

Feng

Zheng

Zhu

et al. 2015

SOLA

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The 2011/12 La Niña, referred to as a double-dip La Niña event with following a previous La Niña event, was not well predicted by most climate models when starting from early-mid 2011. Based on a developed El Niño-Southern Oscillation (ENSO) ensemble prediction system, this paper investigates the key predictors for the 2011/12 La Niña, to determine which conditions favor a double-dip La Niña event up to one year in advance. The key predictors were isolated in a 100-member ensemble hindcast experiment. Results show that continuous easterly surface winds and persistent subsurface cold conditions preceded the secondyear cooling in mid-2011. And a significant difference can be viewed between the best and the worst ensemble forecasts arose from the stochastic model-error perturbations. The detailed comparisons between the best and the worst ensemble forecasts further illustrate that the stochastic model-error perturbations play a significant role in improving the prediction skills of the best ensemble members during the 2011/12 12-month forecast process, through capturing the transition of sea surface temperature (SST) over the tropical Pacific (i.e., from a warm condition to a cold condition) in boreal spring.(Citation: Feng, L., F. Zheng, J. Zhu, and H. Liu, 2015: The role of stochastic model error perturbations in predicting the 2011/ 12 double-dip La Niña. SOLA, 11, 65−69,

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“…In addition, it is expected that the ensemble perturbations will grow upon the unstable-neutral subspace associated with the hybrid ensemble mean state during the subsequent forecast and cycling of the DA system. Yang et al (2012a) and Chang et al (2014) indicated that recentering an EnKF toward a more accurate mean state improves representation of the dynamical uncertainties that are used to form the background error covariance matrix, thus better maintaining the Gaussianity of the ensemble distribution. We should emphasize that the HGDA algorithm does not ''reuse'' observations and thus does not violate the basic assumption that background and observation errors are uncorrelated.…”

Section: A Hybrid Gain Algorithmmentioning

confidence: 99%

Hybrid Gain Data Assimilation Using Variational Corrections in the Subspace Orthogonal to the Ensemble

Chang

Penny

Yang

2020

Monthly Weather Review

Self Cite

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The viability of a parameterless hybrid data assimilation algorithm is investigated. As an alternative to the traditional hybrid covariance scheme, hybrid gain data assimilation (HGDA) was proposed to blend the gain matrix derived from the variational method and the ensemble-based Kalman filter (EnKF). A previously proposed HGDA algorithm uses a two-step process applying the EnKF with a variational update. The algorithm is modified here to limit the variational correction to the subspace orthogonal to the ensemble perturbation subspace without the use of a hybrid weighting parameter, as the optimization of such a parameter is nontrivial. The modified HGDA algorithm is investigated with a quasigeostrophic (QG) model. Results indicate that when the climatological background error covariance matrix B and the observation error covariance R are well estimated, state estimates from the parameterless HGDA are more accurate than the parameter-dependent HGDA. The parameterless HGDA not only has potential advantages over the standard HGDA as an online data assimilation algorithm but can also serve as a valuable diagnostic tool for tuning the B and R matrices. It is also found that in this QG model, the empirically best static B matrix for the stand-alone 3DVAR has high variance at larger spatial scales, which degrades the accuracy of the HGDA systems and may not be the best choice for hybrid methods in general. A comparison of defining the orthogonal subspace globally or locally demonstrates that global orthogonality is more advantageous for stabilizing the hybrid system and maintains large-scale balances.

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Applications of the Mean Recentering Scheme to Improve Typhoon Track Prediction: A Case Study of Typhoon Nanmadol (2011)

Cited by 9 publications

References 59 publications

Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation

Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation

The Role of Stochastic Model Error Perturbations in Predicting the 2011/12 Double-Dip La Niña

Hybrid Gain Data Assimilation Using Variational Corrections in the Subspace Orthogonal to the Ensemble

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