Home
Research
Enseignement




Pierre Ailliot
Université de Brest


Research interests
  • Environmental statistics
    • Time series
    • Stochastic weather generators, weather type model
    • Extreme values
    • Data assimilation
    • Wind, wave, rainfall
  • State-space models
    • HMM, Markov-switching autoregressive models, state-space models
    • Parametric estimation (EM and MCEM algorithms, state-augmentation)
    • Non-parametric estimation
    • EnKF, Particle filters

Publications

Scholar         HAL

Preprint

  •  Guillot, J., Ailliot, P., Frénod, E., Ruiz, J., & Tandeo, P. (2025). State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters.

International jounals

  • Obakrim, S., Ailliot, P., Monbet, V., & Raillard, N. (2024). EM algorithm for generalized Ridge regression with spatial covariates. Environmetrics, 35(6), e2871. 
  • Platzer, P., Ailliot, P., Chapron, B., & Tandeo, P. (2024). Could old tide gauges help estimate past atmospheric variability?. Climate of the Past, 20(10), 2267-2286.
  • Le Bras, P., Sévellec, F., Tandeo, P., Ruiz, J., & Ailliot, P. (2024). Selecting and weighting dynamical models using data-driven approaches. Nonlinear Processes in Geophysics, 31(3), 303-317.
  • Tandeo, P., Ailliot, P., & Sévellec, F. (2023). Data-driven reconstruction of partially observed dynamical systems. Nonlinear Processes in Geophysics, 30(2), 129-137. 
  • Obakrim, S., Ailliot, P., Monbet, V., & Raillard, N. (2023). Statistical modeling of the space–time relation between wind and significant wave height. Advances in Statistical Climatology, Meteorology and Oceanography, 9(1), 67-81. 
  • Boutigny, M., Ailliot, P., Chaubet, A., Naveau, P., & Saussol, B. (2023). A meta-Gaussian distribution for sub-hourly rainfall. Stochastic Environmental Research and Risk Assessment, 37(10), 3915-3927. 
  • Obakrim, S., Monbet, V., Raillard, N., & Ailliot, P. (2023). Learning the spatiotemporal relationship between wind and significant wave height using deep learning. Environmental Data Science, 2, e5.
  • Chau, T. T. T., Ailliot, P., Monbet, V., & Tandeo, P. (2023). Comparison of simulation-based algorithms for parameter estimation and state reconstruction in nonlinear state-space models. Discrete and Continuous Dynamical Systems-Series S, 16(2), 240-264. 
  • Guillot, J., Frénod, E., & Ailliot, P. (2023). Physics informed model error for data assimilation.  Discrete and Continuous Dynamical Systems-Series S, 16(2), 265-276 .
  • Michel, M., Obakrim, S., Raillard, N., Ailliot, P., & Monbet, V. (2022). Deep learning for statistical downscaling of sea states. Advances in Statistical Climatology, Meteorology and Oceanography, 8(1), 83-95. 
  • Koutroulis, E., Petrakis, G., Agou, V., Malisovas, A., Hristopulos, D., Partsinevelos, P., Ailliot P., Boutigny M. et al. (2022). Site selection and system sizing of desalination plants powered with renewable energy sources based on a web-GIS platform. International Journal of Energy Sector Management, 16(3), 469-492. 
  • Ruiz, J., Ailliot, P., Chau, T. T. T., Le Bras, P., Monbet, V., Sévellec, F., & Tandeo, P. (2022). Analog data assimilation for the selection of suitable general circulation models. Geoscientific Model Development Discussions, 2022, 1-30.
  • Chau, T. T. T., Ailliot, P., & Monbet, V. (2021). An algorithm for non-parametric estimation in state–space models. Computational Statistics & Data Analysis, 153, 107062.
  • Platzer, P., Yiou, P., Naveau, P., Tandeo, P., Filipot, J. F., Ailliot, P., & Zhen, Y. (2021). Using local dynamics to explain analog forecasting of chaotic systems. Journal of the Atmospheric Sciences, 78(7), 2117-2133.
  • Legrand, J., Ailliot, P., Naveau, P., & Raillard, N. (2023). Joint stochastic simulation of extreme coastal and offshore significant wave heights. The Annals of Applied Statistics, 17(4), 3363-3383.
  • Chau, T. T. T., Ailliot, P., & Monbet, V. (2020). An algorithm for non-parametric estimation in state–space models. Computational Statistics & Data Analysis, 153, 107062. preprint.pdf
  • Tandeo, P., Ailliot, P., Bocquet, M., Carrassi, A., Miyoshi, T., Pulido, M., & Zhen, Y. (2020). A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation. Monthly Weather Review, 148(10), 3973-3994. preprint.pdf
  • Ailliot, P., Boutigny, M., Koutroulis, E., Malisovas, A., & Monbet, V. (2020). Stochastic weather generator for the design and reliability evaluation of desalination systems with Renewable Energy Sources. Renewable Energy, Volume 158, October 2020, Pages 541-553. preprint.pdf
  • Ailliot, P., Delyon, B., Monbet, V., & Prevosto, M. (2019). Time‐change models for asymmetric processes. Scandinavian Journal of Statistics, 46(4), 1072-10, preprint.pdf 
  • Lguensat, R., Tandeo, P., Ailliot, P., Pulido, M., & Fablet, R. (2017). The analog data assimilation. Monthly Weather Review, 145(10), 4093-4107, paper.pdf 
  • Monbet, V., & Ailliot, P. (2017). Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature. Computational Statistics & Data Analysis, 108, 40-51. preprint.pdf
  • Bessac J., Ailliot P., Cattiaux J., and Monbet V. (2016).Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic.Advances in Statistical Climatology, Meteorology and Oceanography, 2, pp 1-16, paper.pdf 
  • Ailliot P., Allard D., Monbet V., Naveau P. (2015). Stochastic weather generators: an overview of weather type models. Journal de la Société Française de Statistique, 156(1), pp 101-113, paper.pdf
  • Ailliot P., Bessac J., Monbet V., Pène F. (2015). Non-homogeneous hidden Markov-switching models for wind time series. Journal of Statistical Planning and Inference. 160, pp 75–88, preprint.pdf.
  • Kpogo-Nuwoklo K.A., Ailliot P., Olagnon M., Guédé Z., Arnault S. (2015). Improving sea wave spectrum estimation using the temporal structure of wave systems. Coastal Engineering, 96, pp 81-91, preprint.pdf
  • Ailliot P., Pène F. (2015). Consistency of the maximum likelihood estimate for Non-homogeneous Markov-switching models. ESAIM: PS, 19, pp 268-292, preprint.pdf
  • Saulquin B., Fablet R., Ailliot P., Mercier G., Doxaran D., Fanton d'Andon O.  (2015). Characterization of time-varying regimes in remote sensing time series: application to the forecasting of satellite-derived suspended matter concentrations. IEEE JSTARS, 8(1).
  • Bessac J., Ailliot P., Monbet V. (2015). Gaussian linear state-space model for wind fields in the North-East Atlantic. Environmetrics, 26(1) pp 29–38, preprint.pdf
  • Raillard N., Prevosto M., Ailliot P. (2015). Modeling process asymmetries with Laplace moving average.  Computational Statistics & Data Analysis, 81, pp 24–37, preprint.pdf
  • Wright C. J., Scott, R. B., Ailliot P., Furnival D. (2014). Lee wave generation rates in the deep ocean. Geophysical Research Letter, 41(7),  pp. 2434–2440.
  • Raillard N., Ailliot P., Yao J.F. (2014) Modelling extreme values of processes observed at irregular time step. Application to significant wave height. The Annals of Applied Statistics, 8(1), pp. 622-647, preprint pdf
  • Ailliot P., Maisondieu C., Monbet V. (2013), Dynamical partitioning of directional ocean wave spectra. Probabilistic Engineering Mechanics, 33, pp. 95-102, preprint pdf
  • Wright C. J., Scott R. B., Furnival D., Ailliot P., Vermet F. (2013), Global Observations of Ocean-Bottom Subinertial Current Dissipation. Journal of Physical Oceanography, 43, pp. 402-417, preprint pdf
  • Ailliot P., Monbet V., (2012), Markov-switching autoregressive models for wind time series. Environmental Modelling & Software, 30, pp 92-101, preprint pdf
  • Tandeo P., Ailliot P., Autret E. (2011), Linear Gaussian State-Space Model with Irregular Sampling - Application to Sea Surface Temperature. Stochastic Environmental Research & Risk Assessment 25, 793-804, preprint pdf
  • Ailliot P., Thompson C., Thomson P. (2011), Mixed methods for fitting the GEV distribution. Water Resources Research 47, W0551, doi:10.1029/2010WR009417, preprint.pdf
  • Ailliot P., Baxevani A., Cuzol A., Monbet V., Raillard N. (2011), Space-time models for moving fields. Application to significant wave height. Environmetrics,  22(3), pp. 354–369, preprint pdf
  • Ailliot P., Frenod E., Monbet V. (2010), Modeling the coastal ocean over a time period of several weeks. Journal of Differential Equations, 248, pp. 639-659, preprint pdf
  • Tandeo P., Autret E., Piollé J.F., Tournadre J., and Ailliot P. (2009). A multivariate regression approach to adjust AATSR Sea Surface Temperature to in-situ measurements. IEEE geoscience and remote sensing letters, 6(1),  pp. 8-12, preprint.pdf
  • Ailliot P., Thompson C., Thomson P. (2009), Space time modeling of precipitation using a hidden Markov model and censored Gaussian distributions,  Journal of the Royal Statistical Society, Series C (Applied Statistics), 58(3), pp. 405-426, preprint pdf
  • Monbet V., Ailliot P., Marteau P.F. (2008), L1-convergence of smoothing densities in non parametric state space models, Statistical Inference for Stochastic Processes, 11(3), pp. 311-325, preprint pdf
  • Monbet V., Ailliot P., Prevosto M. (2007), Survey of stochastic models for wind and sea-state time series, Probabilistic Engineering Mechanics, 22(2), pp.113-126. preprint pdf
  • Ailliot P., Monbet V., Prevosto M. (2006), An autoregressive model with time-varying coefficients for wind fields,  Environmetrics. 17(2), pp.107-117. abstract, preprint.pdf
  • Ailliot P., Frenod E., Monbet V. (2006). Long term object drift forecast in the ocean with tide and wind, Multiscale Modeling and Simulation, 5(2), pp 514–531. preprint.pdf
  • Ailliot P. (2006), Some theoretical results on a Markov-switching autoregressive models with gamma innovations, Comptes Rendus de l'Académie des Sciences de Paris, 343(4),  pp 271-274  abstract, preprint pdf

Book chapter

  • Tandeo P., Ailliot P.,  Ruiz J.,  Hannart A.,Chapron B., Cuzol A., Monbet V.,  Easton R. and Fablet R. (2015). Combining analog method and ensemble data assimilation: application to the Lorenz-63 chaotic system. Machine Learning and Data Mining Approaches to Climate Science (Springer), preprint pdf

PhD

Ailliot P., (2004), Modèles autorégressifs à changements de régimes markoviens. Applications aux séries temporelles de vent. Thèse de l'université de Rennes 1. download pdf
Past workshops: