The analog data assimilation

R Lguensat, P Tandeo, P Ailliot… - Monthly Weather …, 2017‏ - journals.ametsoc.org
In light of growing interest in data-driven methods for oceanic, atmospheric, and climate
sciences, this work focuses on the field of data assimilation and presents the analog data …

Role of circulation in European heatwaves using flow analogues

A Jézéquel, P Yiou, S Radanovics - Climate dynamics, 2018‏ - Springer
The intensity of European heatwaves is connected to specific synoptic atmospheric
circulation. Given the relatively small number of observations, estimates of the connection …

OCEANBENCH: the sea surface height edition

JE Johnson, Q Febvre, A Gorbunova… - Advances in …, 2024‏ - proceedings.neurips.cc
The ocean is a crucial component of the Earth's system. It profoundly influences human
activities and plays a critical role in climate regulation. Our understanding has significantly …

Analog forecasting with dynamics-adapted kernels

Z Zhao, D Giannakis - Nonlinearity, 2016‏ - iopscience.iop.org
Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which
predicts the evolution of states of a dynamical system (or observables defined on the states) …

Data-driven Models for the Spatio-Temporal Interpolation of satellite-derived SST Fields

R Fablet, PH Viet, R Lguensat - IEEE Transactions on …, 2017‏ - ieeexplore.ieee.org
Satellite-derived products are of key importance for the high-resolution monitoring of the
ocean surface on a global scale. Due to the sensitivity of spaceborne sensors to the …

Ensemble variational Fokker-Planck methods for data assimilation

AN Subrahmanya, AA Popov, A Sandu - Journal of Computational Physics, 2025‏ - Elsevier
Particle flow filters solve Bayesian inference problems by smoothly transforming a set of
particles into samples from the posterior distribution. Particles move in state space under the …

Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context

M Khaki, I Hoteit, M Kuhn, E Forootan… - Science of the Total …, 2019‏ - Elsevier
With a growing number of available datasets especially from satellite remote sensing, there
is a great opportunity to improve our knowledge of the state of the hydrological processes …

Data-driven reconstruction of partially observed dynamical systems

P Tandeo, P Ailliot, F Sévellec - Nonlinear Processes in …, 2023‏ - npg.copernicus.org
The state of the atmosphere, or of the ocean, cannot be exhaustively observed. Crucial parts
might remain out of reach of proper monitoring. Also, defining the exact set of equations …

On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation

MA Sacco, M Pulido, JJ Ruiz… - Quarterly Journal of the …, 2024‏ - Wiley Online Library
Quantifying forecast uncertainty is a key aspect of state‐of‐the‐art numerical weather
prediction and data assimilation systems. Ensemble‐based data assimilation systems …

Using local dynamics to explain analog forecasting of chaotic systems

P Platzer, P Yiou, P Naveau, P Tandeo… - Journal of the …, 2021‏ - journals.ametsoc.org
Analogs are nearest neighbors of the state of a system. By using analogs and their
successors in time, one is able to produce empirical forecasts. Several analog forecasting …