Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …
Data assimilation in the geosciences: An overview of methods, issues, and perspectives
We commonly refer to state estimation theory in geosciences as data assimilation (DA). This
term encompasses the entire sequence of operations that, starting from the observations of a …
term encompasses the entire sequence of operations that, starting from the observations of a …
Autodifferentiable ensemble Kalman filters
Data assimilation is concerned with sequentially estimating a temporally evolving state. This
task, which arises in a wide range of scientific and engineering applications, is particularly …
task, which arises in a wide range of scientific and engineering applications, is particularly …
[HTML][HTML] Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
Recent progress in machine learning has shown how to forecast and, to some extent, learn
the dynamics of a model from its output, resorting in particular to neural networks and deep …
the dynamics of a model from its output, resorting in particular to neural networks and deep …
[HTML][HTML] A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error
Covariance Matrices in Ensemble Data Assimilation in: Monthly Weather Review Volume 148 …
Covariance Matrices in Ensemble Data Assimilation in: Monthly Weather Review Volume 148 …
[HTML][HTML] Remote sensing data assimilation in crop growth modeling from an agricultural perspective: new insights on challenges and prospects
J Wang, Y Wang, Z Qi - Agronomy, 2024 - mdpi.com
The frequent occurrence of global climate change and natural disasters highlights the
importance of precision agricultural monitoring, yield forecasting, and early warning …
importance of precision agricultural monitoring, yield forecasting, and early warning …
Observation error covariance specification in dynamical systems for data assimilation using recurrent neural networks
Data assimilation techniques are widely used to predict complex dynamical systems with
uncertainties, based on time-series observation data. Error covariance matrices modeling is …
uncertainties, based on time-series observation data. Error covariance matrices modeling is …
Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors
Soil moisture controls the land surface water and energy budget and plays a crucial role in
land surface processes. Based on certain mathematical rules, data assimilation can merge …
land surface processes. Based on certain mathematical rules, data assimilation can merge …
[HTML][HTML] A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation
A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation in:
Monthly Weather Review Volume 146 Issue 1 (2018) Jump to Content Jump to Main Navigation …
Monthly Weather Review Volume 146 Issue 1 (2018) Jump to Content Jump to Main Navigation …
Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures
This paper studies multiplicative inflation: the complementary scaling of the state covariance
in the ensemble Kalman filter (EnKF). Firstly, error sources in the EnKF are catalogued and …
in the ensemble Kalman filter (EnKF). Firstly, error sources in the EnKF are catalogued and …