Data Assimilation for a Coupled Ocean–Atmosphere Model. Part I: Sequential State Estimation

Sun, Chengbo; Zheng, Hao; Ghil, Michael; Neelin, J. David

doi:10.1175/1520-0493(2002)130<1073:dafaco>2.0.co;2

Cited by 18 publications

(20 citation statements)

References 61 publications

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“…However further investigation is needed in particular with respect to incorporating this information into hydrologic and Land Surface Models (LSMs, e.g., [5,6]). The assimilation of observed data in order to keep physical states of the models up-to-date, is a widely used technique in meteorology (e.g., [7][8][9]). Only in recent years its use in hydrology, for improving initial states and thus the simulation of flows, water budget, or snow pack, has significantly increased (e.g., [10][11][12][13][14]).…”

Section: Introductionmentioning

confidence: 99%

Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter

et al. 2013

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Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS) pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases.

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Section: Introductionmentioning

confidence: 99%

Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter

et al. 2013

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“…A brief description of this model is provided in appendix B; see Jin and Neelin (1993a,b), Sun et al (2002), and Kondrashov et al (2008) for further details on the original Jin-Neelin model with time-independent forcing and Jin et al (1994Jin et al ( , 1996 for the model formulation with seasonal forcing. Chekroun et al (2014) have called a further simplification of the JNG model the forced JN (fJN) model.…”

Section: The Jng Modelmentioning

confidence: 99%

“…The dynamical variables are the three velocity components (u, y, w) and the thermocline depth anomaly h. We describe here only the model's main features and key parameters. A more complete summary of the model derivation is given in appendix A of Sun et al (2002), and all the parameter values are listed in Table 1 of that paper.…”

Section: A the Oceanmentioning

confidence: 99%

Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models

et al. 2015

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The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin-Neelin-Ghil (JNG) hybrid seasonally forced coupled ocean-atmosphere model of El Niño-Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample.

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“…On the basis of intermediate coupled models, data assimilation studies have been carried out for better prediction and reanalysis of ENSO events. Assimilation methods adopted to the coupled models range from simple schemes of nudging (Chen et al 1995(Chen et al , 1998 or direct insertion (Chen et al 1999) to advanced ones such as the representer method (Bennett et al 1998(Bennett et al , 2000, the adjoint method (Lee et al 2000), a reduced-order Kalman filter (ROKF, Ballabrera-Poy et al 2001), or the extended Kalman filter (EKF, Sun et al 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Apparently the assumption of perfect models is questionable for simple coupled models as the ZC model. On the other hand, the weak constraint was employed by Bennett et al (1998) and Sun et al (2002); they assumed Gaussian system noise added to the model equation. This system model is reasonable compared with the strong constraint.…”

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confidence: 99%

Application of the Ensemble Kalman Filter and Smoother to a Coupled Atmosphere-ocean Model

Ueno

Higuchi

Kagimoto

et al. 2007

SOLA

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We report an application of the ensemble Kalman filter (EnKF) and smoother (EnKS) to an intermediate coupled atmosphere-ocean model of Zebiak and Cane, into which the sea surface height (SSH) anomaly observations by TOPEX/ POSEIDON (T/P) altimetry are assimilated. Smoothed estimates of the 54,403 dimensional state are obtained from 1,981 observational points with 2,048 ensemble members. While assimilated data are SSH anomalies alone, the estimated sea surface temperature (SST) anomalies reproduce primary temporal characteristics of the actual SST. The smoothed estimate of the zonal wind anomalies is also consistent with the observation except for the westerly anomalies in the western Pacific. IntroductionThe El Ni no-Southern Oscillation (ENSO) phenomenon is a dominant climate variation on interannual timescales, and it depends essentially upon coupled interactions of the dynamics of ocean and atmosphere (Neelin et al. 1998). ENSO is characterized by quasiperiodic interannual oscillation of tropical Pacific sea surface temperatures (SSTs) with a dominant period of approximately 4 years. Since ENSO affects not only global climate but ecosystems in and around the tropical Pacific and economies of several countries, successful prediction of ENSO is of great interest from scientific and social points of view (Latif et al. 1998).With a coupled atmosphere-ocean model, Zebiak and Cane (1987) (hereinafter referred to as ZC) have provided the first successful ENSO prediction. The ZC model is a nonlinear anomaly model of intermediate complexity and reproduces an ENSO-like quasiperiodic variation. On the basis of intermediate coupled models, data assimilation studies have been carried out for better prediction and reanalysis of ENSO events. Assimilation methods adopted to the coupled models range from simple schemes of nudging (Chen et al. 1995(Chen et al. , 1998 or direct insertion (Chen et al. 1999) to advanced ones such as the representer method (Bennett et al. 1998(Bennett et al. , 2000, the adjoint method (Lee et al. 2000), a reduced-order Kalman filter (ROKF, Ballabrera-Poy et al. 2001), or the extended Kalman filter (EKF, Sun et al. 2002).Advanced schemes are characterized by their clear assumptions based on the dynamical and statistical implications, which are in contrast to ad hoc assumptions of simple schemes (e.g., Fukumori 2001). However, the advanced schemes listed above could not treat the ZC coupled model without modification or approximation. When the above two variational methods (the representer method and the adjoint method) were applied, the atmospheric component was modified or simplified in order to derive the adjoint equations. The two sequential methods (ROKF and EKF) require a linear approximation of the nonlinear model. It means that the advanced schemes may degrade the model's ability to reproduce nonlinear physical processes.In addition, neglected system noise or Gaussian system noise employed by these works is another problem. If the model dynamics is assumed to be perfect (so-called "...

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Data Assimilation for a Coupled Ocean–Atmosphere Model. Part I: Sequential State Estimation

Cited by 18 publications

References 61 publications

Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter

Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter

Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models

Application of the Ensemble Kalman Filter and Smoother to a Coupled Atmosphere-ocean Model

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