Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.558 IF 1.558
  • IF 5-year value: 1.475 IF 5-year
  • CiteScore value: 2.8 CiteScore
  • SNIP value: 0.921 SNIP 0.921
  • IPP value: 1.56 IPP 1.56
  • SJR value: 0.571 SJR 0.571
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 55 Scimago H
    index 55
  • h5-index value: 22 h5-index 22
NPG | Articles | Volume 26, issue 3
Nonlin. Processes Geophys., 26, 227–250, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 26, 227–250, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 Aug 2019

Research article | 14 Aug 2019

Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data

Fei Lu et al.

Related authors

A prototype stochastic parameterization of regime behaviour in the stably stratified atmospheric boundary layer
Carsten Abraham, Amber M. Holdsworth, and Adam H. Monahan
Nonlin. Processes Geophys., 26, 401–427,,, 2019
Short summary
Combining a pollen and macrofossil synthesis with climate simulations for spatial reconstructions of European climate using Bayesian filtering
Nils Weitzel, Andreas Hense, and Christian Ohlwein
Clim. Past, 15, 1275–1301,,, 2019
Short summary
CSIB v1 (Canadian Sea-ice Biogeochemistry): a sea-ice biogeochemical model for the NEMO community ocean modelling framework
Hakase Hayashida, James R. Christian, Amber M. Holdsworth, Xianmin Hu, Adam H. Monahan, Eric Mortenson, Paul G. Myers, Olivier G. J. Riche, Tessa Sou, and Nadja S. Steiner
Geosci. Model Dev., 12, 1965–1990,,, 2019
Short summary
Effects of temporal averaging on short-term irradiance variability under mixed sky conditions
Gerald M. Lohmann and Adam H. Monahan
Atmos. Meas. Tech., 11, 3131–3144,,, 2018
Short summary
Idealized models of the joint probability distribution of wind speeds
Adam H. Monahan
Nonlin. Processes Geophys., 25, 335–353,,, 2018
Short summary

Related subject area

Subject: Predictability, Data Assimilation | Topic: Climate, Atmosphere, Ocean, Hydrology, Cryosphere, Biosphere
From research to applications – examples of operational ensemble post-processing in France using machine learning
Maxime Taillardat and Olivier Mestre
Nonlin. Processes Geophys., 27, 329–347,,, 2020
Short summary
Correcting for model changes in statistical postprocessing – an approach based on response theory
Jonathan Demaeyer and Stéphane Vannitsem
Nonlin. Processes Geophys., 27, 307–327,,, 2020
Short summary
Brief communication: Residence time of energy in the atmosphere
Carlos Osácar, Manuel Membrado, and Amalio Fernández-Pacheco
Nonlin. Processes Geophys., 27, 235–237,,, 2020
Short summary
Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales
Michiel Van Ginderachter, Daan Degrauwe, Stéphane Vannitsem, and Piet Termonia
Nonlin. Processes Geophys., 27, 187–207,,, 2020
Short summary
Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty
Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper
Nonlin. Processes Geophys., 27, 209–234,,, 2020
Short summary

Cited articles

Alberty, J., Carstensen, C., and Funken, S. A.: Remarks around 50 lines of Matlab: short finite element implementation, Numer. Algorithms, 20, 117–137, 1999. a
Andrieu, C., Doucet, A., and Holenstein, R.: Particle Markov chain Monte Carlo methods, J. R. Stat. Soc. B, 72, 269–342, 2010. a, b, c, d, e
Annan, J., Hargreaves, J., Edwards, N., and Marsh, R.: Parameter estimation in an intermediate complexity Earth System Model using an ensemble Kalman filter, Ocean Model., 8, 135–154, 2005. a
Apte, A., Hairer, M., Stuart, A., and Voss, J.: Sampling the Posterior: An Approach to Non-Gaussian Data Assimilation, Physica D, 230, 50–64, 2007. a
Bakka, H., Rue, H., Fuglstad, G. A., Riebler, A., Bolin, D., Illian, J., . and Lindgren, F.: Spatial modeling with R‐INLA: A review, Wiley Interdisciplinary Reviews: Computational Statistics, 10, e1443, 2018. a
Publications Copernicus
Short summary
ll-posedness of the inverse problem and sparse noisy data are two major challenges in the modeling of high-dimensional spatiotemporal processes. We present a Bayesian inference method with a strongly regularized posterior to overcome these challenges, enabling joint state-parameter estimation and quantifying uncertainty in the estimation. We demonstrate the method on a physically motivated nonlinear stochastic partial differential equation arising from paleoclimate construction.
ll-posedness of the inverse problem and sparse noisy data are two major challenges in the...