DeepAI AI Chat
Log In Sign Up

A multi-model ensemble Kalman filter for data assimilation and forecasting

02/04/2022
by   Eviatar Bach, et al.
proton mail
0

Data assimilation (DA) aims to optimally combine model forecasts and noisy observations. Multi-model DA generalizes the variational or Bayesian formulation of the Kalman filter, and we prove here that it is also the minimum variance linear unbiased estimator. However, previous implementations of this approach have not estimated the model error, and have therewith not been able to correctly weight the separate models and the observations. Here, we show how multiple models can be combined for both forecasting and DA by using an ensemble Kalman filter with adaptive model error estimation. This methodology is applied to the Lorenz96 model, and it results in significant error reductions compared to the best model and to an unweighted multi-model ensemble.

READ FULL TEXT

page 1

page 2

page 3

page 4

08/28/2019

Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations

Concurrent observation technologies have made high-precision real-time d...
06/09/2022

Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems

Data assimilation (DA) is a key component of many forecasting models in ...
02/25/2021

Multifidelity Ensemble Kalman Filtering using surrogate models defined by Physics-Informed Autoencoders

The multifidelity ensemble Kalman filter aims to combine a full-order mo...
04/08/2020

Ensemble Kalman Filter with perturbed observations in weather forecasting and data assimilation

Data assimilation provides algorithms for widespread applications in var...
02/14/2023

Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data

For oceanographic applications, probabilistic forecasts typically have t...
01/25/2018

Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures

This paper studies inflation: the complementary scaling of the state cov...