Publications by Type: Journal Article

2007
Camargo, Suzana J., Andrew W. Robertson, Scott J. Gaffney, Padhraic Smyth, and Michael Ghil. “Cluster analysis of typhoon tracks. Part I: General properties.” Journal of Climate 20, no. 14 (2007): 3635–3653.
Camargo, Suzana J., Andrew W. Robertson, Scott J. Gaffney, Padhraic Smyth, and Michael Ghil. “Cluster analysis of typhoon tracks. Part II: Large-scale circulation and ENSO.” Journal of Climate 20, no. 14 (2007): 3654–3676.
Chin, T. M., M. J. Turmon, J. B. Jewell, and Michael Ghil. “An Ensemble-Based Smoother with Retrospectively Updated Weights for Highly Nonlinear Systems.” Monthly Weather Review 135 (2007): 186–202. Abstract

Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.

Ihler, Alexander T., Sergey Kirshner, Michael Ghil, Andrew W. Robertson, and Padhraic Smyth. “Graphical models for statistical inference and data assimilation.” Physica D: Nonlinear Phenomena 230, no. 1 (2007): 72–87.
Kravtsov, Sergey, William K. Dewar, Pavel S. Berloff, James C. McWilliams, and Michael Ghil. “A highly nonlinear coupled mode of decadal variability in a mid-latitude ocean–atmosphere model.” Dynamics of Atmospheres and Oceans 43, no. 3 (2007): 123–150.
Kondrashov, Dmitri, Y. Shprits, Michael Ghil, and R. Thorne. “A Kalman filter technique to estimate relativistic electron lifetimes in the outer radiation belt.” Journal of Geophysical Research: Space Physics 112, no. A10 (2007).
Feliks, Yizhak, Michael Ghil, and Eric Simonnet. “Low-frequency variability in the midlatitude baroclinic atmosphere induced by an oceanic thermal front.” Journal of the Atmospheric Sciences 64, no. 1 (2007): 97–116.
Kondrashov, Dmitri, Jie Shen, Richard Berk, Fabio D'Andrea, and Michael Ghil. “Predicting weather regime transitions in Northern Hemisphere datasets.” Climate Dynamics 29, no. 5 (2007): 535–551.
Gaffney, Scott J., Andrew W. Robertson, Padhraic Smyth, Suzana J. Camargo, and Michael Ghil. “Probabilistic clustering of extratropical cyclones using regression mixture models.” Climate Dynamics 29, no. 4 (2007): 423–440.
Shprits, Yuri, Dmitri Kondrashov, Yue Chen, Richard Thorne, Michael Ghil, Reiner Friedel, and Geoff Reeves. “Reanalysis of relativistic radiation belt electron fluxes using CRRES satellite data, a radial diffusion model, and a Kalman filter.” Journal of Geophysical Research: Space Physics 112, no. A12 (2007).
Kondrashov, Dmitri, and Michael Ghil. “Reply to T. Schneider's comment on "Spatio-temporal filling of missing points in geophysical data sets".” Nonlinear Processes in Geophysics 14, no. 1 (2007): 3–4.
Sushama, L., Michael Ghil, and K. Ide. “Spatio-temporal variability in a mid-latitude ocean basin subject to periodic wind forcing.” Atmosphere-ocean 45, no. 4 (2007): 227–250. Abstract

The mid-latitude ocean's response to time-dependent zonal wind-stress forcing is studied using a reduced-gravity, 1.5-layer, shallow-water model in two rectangular ocean basins of different sizes. The small basin is 1000 km $\times$ 2000 km and the larger one is 3000 km $\times$ 2010 km; the aspect ratio of the larger basin is quite similar to that of the North Atlantic between 20$\deg$N and 60$\deg$N. The parameter dependence of the model solutions and their spatio-temporal variability subject to time-independent wind stress forcing serve as the reference against which the results for time-dependent forcing are compared. For the time-dependent forcing case, three zonal-wind profiles that mimic the seasonal cycle are considered in this study: (1) a fixed-profile wind-stress forcing with periodically varying intensity; (2) a wind-stress profile with fixed intensity, but north–south migration of the mid-latitude westerly wind maximum; and (3) a north–south migrating profile with periodically varying intensity. Results of the small-basin simulations show the intrinsic variability found for time-independent forcing to persist when the intensity of the wind forcing varies periodically. It thus appears that the physics behind the upper ocean's variability is mainly controlled by internal dynamics, although the solutions’ spatial patterns are now more complex, due to the interaction between the external and internal modes of variability. The north–south migration of wind forcing, however, does inhibit the inertial recirculation; its suppression increases with the amplitude of north–south migration in the wind-stress forcing. Model solutions in the larger rectangular basin and at smaller viscosity exhibit more realistic recirculation gyres, with a small meridional-to-zonal aspect ratio, and an elongated eastward jet; the low-frequency variability of these solutions is dominated by periodicities of 14 and 6–7 years. Simulations performed in this setting with a wind-stress profile that involves seasonal variations of realistic amplitude in both the intensity and the position of the atmospheric jet show the seven-year periodicity in the oceanic circulation to be robust. The intrinsic variability is reinforced by the periodic variations in the jet's intensity and weakened by periodic variations in the meridional position; the two effects cancel, roughly speaking, thus preserving the overall characteristics of the seven-year mode.

Deloncle, Axel, Richard Berk, Fabio D'Andrea, and Michael Ghil. “Weather regime prediction using statistical learning.” Journal of the Atmospheric Sciences 64, no. 5 (2007): 1619–1635.
2006
Kondrashov, Dmitri, S. Kravtsov, and M. Ghil. “Empirical Mode Reduction in a Model of Extratropical Low-Frequency Variability.” Journal of the Atmospheric Sciences 63, no. 7 (2006): 1859-1877. Publisher's Version
Kravtsov, S, P Berloff, William K. Dewar, M Ghil, and James C. McWilliams. “Dynamical origin of low-frequency variability in a highly nonlinear midlatitude coupled model.Journal of Climate 19, no. 24 (2006). Abstract

A novel mechanism of decadal midlatitude coupled variability, which crucially depends on the nonlinear dynamics of both the atmosphere and the ocean, is presented. The coupled model studied involves quasigeostrophic atmospheric and oceanic components, which communicate with each other via a constant-depth oceanic mixed layer. A series of coupled and uncoupled experiments show that the decadal coupled mode is active across parameter ranges that allow the bimodality of the atmospheric zonal flow to coexist with oceanic turbulence. The latter is most intense in the regions of inertial recirculation (IR). Bimodality is associated with the existence of two distinct anomalously persistent zonal-flow modes, which are characterized by different latitudes of the atmospheric jet stream. The IR reorganizations caused by transitions of the atmosphere from its high- to low-latitude state and vice versa create sea surface temperature anomalies that tend to induce transition to the opposite atmospheric state. The decadal–interdecadal time scale of the resulting oscillation is set by the IR adjustment; the latter depends most sensitively on the oceanic bottom drag. The period T of the nonlinear oscillation is 7–25 yr for the range of parameters explored, with the most realistic parameter values yielding T \approx 20 yr. Aside from this nonlinear oscillation, an interannual Rossby wave mode is present in all coupled experiments. This coupled mode depends neither on atmospheric bimodality, nor on ocean eddy dynamics; it is analogous to the mode found previously in a channel configuration. Its time scale in the model with a closed ocean basin is set by cross-basin wave propagation and equals 3–5 yr for a basin width comparable with the North Atlantic.

Kondrashov, Dmitri, S Kravtsov, and M Ghil. “Empirical mode reduction in a model of extratropical low-frequency variability.” Journal of the Atmospheric Sciences 63, no. 7 (2006): 1859–1877. Abstract

This paper constructs and analyzes a reduced nonlinear stochastic model of extratropical low-frequency variability. To do so, it applies multilevel quadratic regression to the output of a long simulation of a global baroclinic, quasigeostrophic, three-level (QG3) model with topography; the model's phase space has a dimension of O(104). The reduced model has 45 variables and captures well the non-Gaussian features of the QG3 model's probability density function (PDF). In particular, the reduced model's PDF shares with the QG3 model its four anomalously persistent flow patterns, which correspond to opposite phases of the Arctic Oscillation and the North Atlantic Oscillation, as well as the Markov chain of transitions between these regimes. In addition, multichannel singular spectrum analysis identifies intraseasonal oscillations with a period of 35–37 days and of 20 days in the data generated by both the QG3 model and its low-dimensional analog. An analytical and numerical study of the reduced model starts with the fixed points and oscillatory eigenmodes of the model's deterministic part and uses systematically an increasing noise parameter to connect these with the behavior of the full, stochastically forced model version. The results of this study point to the origin of the QG3 model's multiple regimes and intraseasonal oscillations and identify the connections between the two types of behavior.

Kao, Jim, Dawn Flicker, Kayo Ide, and Michael Ghil. “Estimating model parameters for an impact-produced shock-wave simulation: Optimal use of partial data with the extended Kalman filter.” Journal of Computational Physics 214, no. 2 (2006): 725–737.
Kravtsov, S., Andrew W. Robertson, and Michael Ghil. “Multiple regimes and low-frequency oscillations in the Northern Hemisphere's zonal-mean flow.” Journal of the Atmospheric Sciences 63, no. 3 (2006): 840–860.
Bellon, G., Michael Ghil, and H. Le Treut. “Scale separation for moisture-laden regions in the tropical atmosphere.” Geophysical Research Letters 33, no. 1 (2006).
Kondrashov, Dmitri, and Michael Ghil. “Spatio-temporal filling of missing points in geophysical data sets.” Nonlinear Processes in Geophysics 13, no. 2 (2006): 151–159. Abstract

The majority of data sets in the geosciences are obtained from observations and measurements of natural systems, rather than in the laboratory. These data sets are often full of gaps, due to to the conditions under which the measurements are made. Missing data give rise to various problems, for example in spectral estimation or in specifying boundary conditions for numerical models. Here we use Singular Spectrum Analysis (SSA) to fill the gaps in several types of data sets. For a univariate record, our procedure uses only temporal correlations in the data to fill in the missing points. For a multivariate record, multi-channel SSA (M-SSA) takes advantage of both spatial and temporal correlations. We iteratively produce estimates of missing data points, which are then used to compute a self-consistent lag-covariance matrix; cross-validation allows us to optimize the window width and number of dominant SSA or M-SSA modes to fill the gaps. The optimal parameters of our procedure depend on the distribution in time (and space) of the missing data, as well as on the variance distribution between oscillatory modes and noise. The algorithm is demonstrated on synthetic examples, as well as on data sets from oceanography, hydrology, atmospheric sciences, and space physics: global sea-surface temperature, flood-water records of the Nile River, the Southern Oscillation Index (SOI), and satellite observations of relativistic electrons.