Manuel Pulido

发表

Kernel embedded nonlinear observational mappings in the variational mapping particle filter

Derek J. Posselt, Manuel Pulido, Peter Jan van Leeuwen, 2019, ICCS.

Joint Estimation of Model and Observation Error Covariance Matrices in Data Assimilation: a Review

Marc Bocquet, Takemasa Miyoshi, Alberto Carrassi, 2018, 1807.11221.

Model selection: Using information measures from ordinal symbolic analysis to select model sub-grid scale parameterizations

Osvaldo A. Rosso, Manuel Pulido, O. Rosso, 2017 .

Soil Science Challenges in a New Era: A Transdisciplinary Overview of Relevant Topics

Hamid Reza Pourghasemi, Manuel Pulido, Luca Salvati, 2020, Air, Soil and Water Research.

Model error covariance estimation in particle and ensemble Kalman filters using an online expectation-maximization algorithm

Manuel Pulido, Tadeo Javier Cocucci, Magdalena Lucini, 2020 .

The Analog Data Assimilation

Redouane Lguensat, Ronan Fablet, Manuel Pulido, 2017 .

Sequential Monte Carlo with kernel embedded mappings: The mapping particle filter

Peter Jan van Leeuwen, Manuel Pulido, P. Leeuwen, 2019, J. Comput. Phys..

Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter pdf

Manuel Pulido, Peter Jan van Leeuwen, P. Leeuwen, 2018, ArXiv.

Estimating model‐error covariances in nonlinear state‐space models using Kalman smoothing and the expectation–maximization algorithm

Thierry Chonavel, Ibrahim Hoteit, Manuel Pulido, 2017 .

Toward data-driven methods in geophysics: the Analog Data Assimilation

Redouane Lguensat, Ronan Fablet, Manuel Pulido, 2017 .

Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: Application to a subgrid‐scale orography parametrization

Manuel Pulido, Pierre Tandeo, François Lott, 2013 .

Inference of stochastic parametrizations for model error treatment using nested ensemble Kalman filters

Juan Ruiz, Manuel Pulido, Guillermo Scheffler, 2018, Quarterly Journal of the Royal Meteorological Society.

A framework for causal discovery in non-intervenable systems.

Manuel Pulido, Peter Jan van Leeuwen, Michael DeCaria, 2021, Chaos.

Model Error Estimation Using the Expectation Maximization Algorithm and a Particle Flow Filter

Manuel Pulido, Peter Jan van Leeuwen, María Magdalena Lucini, 2021, SIAM/ASA J. Uncertain. Quantification.

Evaluation of machine learning techniques for forecast uncertainty quantification

Manuel Pulido, Pierre Tandeo, Maximiliano A. Sacco, 2021, Quarterly Journal of the Royal Meteorological Society.

Inference in epidemiological agent-based models using ensemble-based data assimilation

Juan Aparicio, Manuel Pulido, Juan Ruiz, 2021, PloS one.

Machine learning-based uncertainty quantification for data assimilation: a simple model experiment

Manuel Pulido, Pierre Tandeo, Yicun Zhen, 2021 .

A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation

Takemasa Miyoshi, Alberto Carrassi, Marc Bocquet, 2018 .

Estimating Model Parameters with Ensemble-Based Data Assimilation: Parameter Covariance Treatment

Takemasa Miyoshi, Manuel Pulido, Juan Ruiz, 2013 .

Parameter Estimation Using Ensemble-Based Data Assimilation in the Presence of Model Error

Manuel Pulido, Juan Ruiz, J. Ruiz, 2015 .

Stratospheric and Mesospheric Data Assimilation: The Role of Middle Atmospheric Dynamics

Manuel Pulido, S. Polavarapu, M. Pulido, 2017 .

Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review

Takemasa Miyoshi, Manuel Pulido, Juan Ruiz, 2013 .

Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods

Alberto Carrassi, Marc Bocquet, Manuel Pulido, 2017, 1709.07328.

Recent developments in gravity‐wave effects in climate models and the global distribution of gravity‐wave momentum flux from observations and models

Shingo Watanabe, Manuel Pulido, Peter Preusse, 2010 .