| Stochastic weather generators: an overview of weather type models P Ailliot, D Allard, V Monbet, P Naveau Journal de la société française de statistique 156 (1), 101-113, 2015 | 208 | 2015 |
| Markov-switching autoregressive models for wind time series P Ailliot, V Monbet Environmental Modelling & Software 30, 92-101, 2012 | 168 | 2012 |
| The analog data assimilation R Lguensat, P Tandeo, P Ailliot, M Pulido, R Fablet Monthly Weather Review 145 (10), 4093-4107, 2017 | 161 | 2017 |
| Survey of stochastic models for wind and sea state time series V Monbet, P Ailliot, M Prevosto Probabilistic engineering mechanics 22 (2), 113-126, 2007 | 132 | 2007 |
| Space–time modelling of precipitation by using a hidden Markov model and censored Gaussian distributions P Ailliot, C Thompson, P Thomson Journal of the Royal Statistical Society Series C: Applied Statistics 58 (3 …, 2009 | 116 | 2009 |
| An autoregressive model with time‐varying coefficients for wind fields P Ailliot, V Monbet, M Prevosto Environmetrics 17 (2), 107-117, 2006 | 98 | 2006 |
| A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation P Tandeo, P Ailliot, M Bocquet, A Carrassi, T Miyoshi, M Pulido, Y Zhen Monthly Weather Review 148 (10), 3973-3994, 2020 | 74 | 2020 |
| Lee wave generation rates in the deep ocean CJ Wright, RB Scott, P Ailliot, D Furnival Geophysical Research Letters 41 (7), 2434-2440, 2014 | 66 | 2014 |
| Non-homogeneous hidden Markov-switching models for wind time series P Ailliot, J Bessac, V Monbet, F Pene Journal of Statistical Planning and Inference 160, 75-88, 2015 | 55 | 2015 |
| Combining analog method and ensemble data assimilation: application to the Lorenz-63 chaotic system P Tandeo, P Ailliot, J Ruiz, A Hannart, B Chapron, A Cuzol, V Monbet, ... Machine Learning and Data Mining Approaches to Climate Science: proceedings …, 2015 | 54 | 2015 |
| Mixed methods for fitting the GEV distribution P Ailliot, C Thompson, P Thomson Water Resources Research 47 (5), 2011 | 45 | 2011 |
| Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature V Monbet, P Ailliot Computational Statistics & Data Analysis 108, 40-51, 2017 | 38 | 2017 |
| Long term object drift forecast in the ocean with tide and wind P Ailliot, E Frenod, V Monbet Multiscale Modeling & Simulation 5 (2), 514-531, 2006 | 38 | 2006 |
| Linear Gaussian state-space model with irregular sampling: application to sea surface temperature P Tandeo, P Ailliot, E Autret Stochastic Environmental Research and Risk Assessment 25, 793-804, 2011 | 36 | 2011 |
| Space–time models for moving fields with an application to significant wave height fields P Ailliot, A Baxevani, A Cuzol, V Monbet, N Raillard Environmetrics 22 (3), 354-369, 2011 | 34 | 2011 |
| Global observations of ocean-bottom subinertial current dissipation CJ Wright, RB Scott, D Furnival, P Ailliot, F Vermet Journal of physical oceanography 43 (2), 402-417, 2013 | 32 | 2013 |
| Comparison of hidden and observed regime-switching autoregressive models for ()-components of wind fields in the northeastern Atlantic J Bessac, P Ailliot, J Cattiaux, V Monbet Advances in Statistical Climatology, Meteorology and Oceanography 2 (1), 1-16, 2016 | 24 | 2016 |
| Joint estimation of model and observation error covariance matrices in data assimilation: a review P Tandeo, P Ailliot, M Bocquet, A Carrassi, T Miyoshi, M Pulido, Y Zhen | 22 | 2018 |
| Consistency of the maximum likelihood estimate for non-homogeneous Markov–switching models P Ailliot, F Pene ESAIM: Probability and Statistics 19, 268-292, 2015 | 20 | 2015 |
| Modeling extreme values of processes observed at irregular time steps: Application to significant wave height N Raillard, P Ailliot, J Yao | 18 | 2014 |
