A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I: Application in the Lorenz system

Lili Lei, David R. Stauffer, Sue Ellen Haupt, George S. Young

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

A hybrid data assimilation approach combining nudging and the ensemble Kalman filter (EnKF) for dynamic analysis and numerical weather prediction is explored here using the non-linear Lorenz three-variable model system with the goal of a smooth, continuous and accurate data assimilation. The hybrid nudging-EnKF (HNEnKF) computes the hybrid nudging coefficients from the flow-dependent, time-varying error covariance matrix from the EnKF's ensemble forecasts. It extends the standard diagonal nudging terms to additional off-diagonal statistical correlation terms for greater inter-variable influence of the innovations in the model's predictive equations to assist in the data assimilation process. The HNEnKF promotes a better fit of an analysis to data compared to that achieved by either nudging or incremental analysis update (IAU). When model error is introduced, it produces similar or better root mean square errors compared to the EnKF while minimising the error spikes/discontinuities created by the intermittent EnKF. It provides a continuous data assimilation with better inter-variable consistency and improved temporal smoothness than that of the EnKF. Data assimilation experiments are also compared to the ensemble Kalman smoother (EnKS). The HNEnKF has similar or better temporal smoothness than that of the EnKS, and with much smaller central processing unit (CPU) time and data storage requirements.

Original languageEnglish (US)
Article number18484
JournalTellus, Series A: Dynamic Meteorology and Oceanography
Volume64
Issue number1
DOIs
StatePublished - Aug 23 2012

Fingerprint

Kalman filter
data assimilation
dynamic analysis
discontinuity
innovation
weather
matrix
prediction
experiment
analysis

All Science Journal Classification (ASJC) codes

  • Oceanography
  • Atmospheric Science

Cite this

@article{4de5b817b35a4ec0ad7d83b8b7a03a88,
title = "A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I: Application in the Lorenz system",
abstract = "A hybrid data assimilation approach combining nudging and the ensemble Kalman filter (EnKF) for dynamic analysis and numerical weather prediction is explored here using the non-linear Lorenz three-variable model system with the goal of a smooth, continuous and accurate data assimilation. The hybrid nudging-EnKF (HNEnKF) computes the hybrid nudging coefficients from the flow-dependent, time-varying error covariance matrix from the EnKF's ensemble forecasts. It extends the standard diagonal nudging terms to additional off-diagonal statistical correlation terms for greater inter-variable influence of the innovations in the model's predictive equations to assist in the data assimilation process. The HNEnKF promotes a better fit of an analysis to data compared to that achieved by either nudging or incremental analysis update (IAU). When model error is introduced, it produces similar or better root mean square errors compared to the EnKF while minimising the error spikes/discontinuities created by the intermittent EnKF. It provides a continuous data assimilation with better inter-variable consistency and improved temporal smoothness than that of the EnKF. Data assimilation experiments are also compared to the ensemble Kalman smoother (EnKS). The HNEnKF has similar or better temporal smoothness than that of the EnKS, and with much smaller central processing unit (CPU) time and data storage requirements.",
author = "Lili Lei and Stauffer, {David R.} and Haupt, {Sue Ellen} and Young, {George S.}",
year = "2012",
month = "8",
day = "23",
doi = "10.3402/tellusa.v64i0.18484",
language = "English (US)",
volume = "64",
journal = "Tellus, Series A: Dynamic Meteorology and Oceanography",
issn = "0280-6495",
publisher = "Co-Action Publishing",
number = "1",

}

A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I : Application in the Lorenz system. / Lei, Lili; Stauffer, David R.; Haupt, Sue Ellen; Young, George S.

In: Tellus, Series A: Dynamic Meteorology and Oceanography, Vol. 64, No. 1, 18484, 23.08.2012.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I

T2 - Application in the Lorenz system

AU - Lei, Lili

AU - Stauffer, David R.

AU - Haupt, Sue Ellen

AU - Young, George S.

PY - 2012/8/23

Y1 - 2012/8/23

N2 - A hybrid data assimilation approach combining nudging and the ensemble Kalman filter (EnKF) for dynamic analysis and numerical weather prediction is explored here using the non-linear Lorenz three-variable model system with the goal of a smooth, continuous and accurate data assimilation. The hybrid nudging-EnKF (HNEnKF) computes the hybrid nudging coefficients from the flow-dependent, time-varying error covariance matrix from the EnKF's ensemble forecasts. It extends the standard diagonal nudging terms to additional off-diagonal statistical correlation terms for greater inter-variable influence of the innovations in the model's predictive equations to assist in the data assimilation process. The HNEnKF promotes a better fit of an analysis to data compared to that achieved by either nudging or incremental analysis update (IAU). When model error is introduced, it produces similar or better root mean square errors compared to the EnKF while minimising the error spikes/discontinuities created by the intermittent EnKF. It provides a continuous data assimilation with better inter-variable consistency and improved temporal smoothness than that of the EnKF. Data assimilation experiments are also compared to the ensemble Kalman smoother (EnKS). The HNEnKF has similar or better temporal smoothness than that of the EnKS, and with much smaller central processing unit (CPU) time and data storage requirements.

AB - A hybrid data assimilation approach combining nudging and the ensemble Kalman filter (EnKF) for dynamic analysis and numerical weather prediction is explored here using the non-linear Lorenz three-variable model system with the goal of a smooth, continuous and accurate data assimilation. The hybrid nudging-EnKF (HNEnKF) computes the hybrid nudging coefficients from the flow-dependent, time-varying error covariance matrix from the EnKF's ensemble forecasts. It extends the standard diagonal nudging terms to additional off-diagonal statistical correlation terms for greater inter-variable influence of the innovations in the model's predictive equations to assist in the data assimilation process. The HNEnKF promotes a better fit of an analysis to data compared to that achieved by either nudging or incremental analysis update (IAU). When model error is introduced, it produces similar or better root mean square errors compared to the EnKF while minimising the error spikes/discontinuities created by the intermittent EnKF. It provides a continuous data assimilation with better inter-variable consistency and improved temporal smoothness than that of the EnKF. Data assimilation experiments are also compared to the ensemble Kalman smoother (EnKS). The HNEnKF has similar or better temporal smoothness than that of the EnKS, and with much smaller central processing unit (CPU) time and data storage requirements.

UR - http://www.scopus.com/inward/record.url?scp=84865142667&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84865142667&partnerID=8YFLogxK

U2 - 10.3402/tellusa.v64i0.18484

DO - 10.3402/tellusa.v64i0.18484

M3 - Article

AN - SCOPUS:84865142667

VL - 64

JO - Tellus, Series A: Dynamic Meteorology and Oceanography

JF - Tellus, Series A: Dynamic Meteorology and Oceanography

SN - 0280-6495

IS - 1

M1 - 18484

ER -