1. 1.Kondrashov, D., M. D. Chekroun, X. Yuan, and M. Ghil, 2018:
    Data-adaptive Harmonic Decomposition and Stochastic Modeling of Arctic Sea Ice,

In: Tsonis A. (eds) Advances in Nonlinear Geosciences. Springer, doi:10.1007/978-3-319-58895-7_10.

2.Ghil, M., A. Groth, D. Kondrashov, and A.W. Robertson, 2018:

Extratropical sub-seasonal–to–seasonal oscillations and multiple regimes: The dynamical systems view.,

In The Gap between Weather and Climate Forecasting: Sub-Seasonal to Seasonal Prediction.

A.W . Robertson and F. Vitart (eds), Elsevier.

  1. 2.Chekroun, M. D., and D. Kondrashov, 2017:

Data-adaptive harmonic spectra and multilayer Stuart-Landau models,

Chaos, 27, 093110: doi:10.1063/1.4989400, HAL preprint.

  1. 3.Kondrashov, D., M.D. Chekroun, and M. Ghil, 2015:
    Data-driven non-Markovian closure models,

Physica D, 297, 33-55, doi:10.1016/j.physd.2014.12.005.

  1. 4.Chekroun, M. D., D. Kondrashov and M. Ghil, 2011:

Predicting stochastic systems by noise sampling,
and application to the El Niño-Southern Oscillation

Proc. Nat. Acad. Sciences, 108 (29), 11766–11771, doi: 10.1073/pnas.1015753108.

  1. 5. Kravtsov S., D. Kondrashov, and M. Ghil, 2005:

Multi-level regression modeling of nonlinear processes: Derivation and applications to climatic variability.

J. Climate, 18, 4404–4424, doi: 10.1175/JCLI3544.1


  1. Matlab packages:

  2. Multilayer Stochastic Modeling (MSM)

  3. Past-Noise Forecasting (PNF)

  4. Data-adaptive Harmonic Decomposition (DAHD)   

  1. These tools demonstrate several data-driven nonlinear stochastic-dynamic methods for analysis, modeling and prediction of datasets from partially-observed systems.  

  2. 1. Empirical Model Reduction [Kondrashov et al, 2005, Kravtsov et al. 2009] within a general class of nonlinear Multilayer Stochastic Models (MSM) with memory effects and complex noise structure [Kondrashov et al. 2015, Ghil et al. 2018].

  3. 2. “Past-noise forecasting” [Chekroun, Kondrashov and Ghil et al. 2011].

  4. 3. Data-adaptive Harmonic Decomposition [Chekroun and Kondrashov, 2017; Kondrashov et al. 2018] for identification of coherent spatio-temporal modes in a shorty and noisy dataset. 

Supported by grants ONR-MURI N00014-16-1-2073, NSF OCE-1243175 and and OCE-1658357