• Corpus ID: 219177313

What limits the number of observations that can be effectively assimilated by EnKF

@article{Hotta2020WhatLT,
  title={What limits the number of observations that can be effectively assimilated by EnKF},
  author={Daisuke Hotta and Yoichiro Ota},
  journal={arXiv: Data Analysis, Statistics and Probability},
  year={2020}
}
  • D. Hotta, Yoichiro Ota
  • Published 31 May 2020
  • Environmental Science
  • arXiv: Data Analysis, Statistics and Probability
The ability of ensemble Kalman filter (EnKF) algorithms to extract information from observations is analyzed with the aid of the concept of the degrees of freedom for signal (DFS). A simple mathematical argument shows that DFS for EnKF is bounded from above by the ensemble size, which entails that assimilating much more observations than the ensemble size automatically leads to DFS underestimation. Since DFS is a trace of the posterior error covariance mapped onto the normalized observation… 
1 Citations

Figures from this paper

Forecasts of the July 2020 Kyushu Heavy Rain Using a 1000-Member Ensemble Kalman Filter
Forecast performances of the July 2020 Kyushu heavy rain have been revisited with the aim of improving the forecasts for this event. While the Japan Meteorological Agency’s (JMA) deterministic

References

SHOWING 1-10 OF 50 REFERENCES
Analysis sensitivity calculation in an ensemble Kalman filter
Analysis sensitivity indicates the sensitivity of an analysis to the observations, which is complementary to the sensitivity of the analysis to the background. In this paper, we discuss a method to
Localization and the iterative ensemble Kalman smoother
TLDR
It is argued that the time evolution of the localization operator depends strongly on the forecast dynamics, and several localization strategies meant to address the issue are proposed and tested.
Adaptive sampling with the ensemble transform Kalman filter
TLDR
The ET KF technique is used by the National Centers for Environmental Prediction in the Winter Storm Reconnaissance missions of 1999 and 2000 to determine where aircraft should deploy dropwindsondes in order to improve 24‐72-h forecasts over the continental United States.
An Adaptive Ensemble Kalman Filter
Abstract To the extent that model error is nonnegligible in numerical models of the atmosphere, it must be accounted for in 4D atmospheric data assimilation systems. In this study, a method of
A Local Ensemble Kalman Filter for Atmospheric Data Assimilation
TLDR
A new, local formulation of the ensemble Kalman Filter approach for atmospheric data assimilation based on the hypothesis that, when the Earth's surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region.
Is the Local Ensemble Transform Kalman Filter suitable for operational data assimilation
TLDR
An attempt to account for realistically correlated observational errors makes LETKF and other ensemble-space and model-space Ensemble Kalman Filters (EnKF) computationally inefficient.
Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation
TLDR
For the EnKF in general, but higher-resolution applications in particular, it is desirable to use a short assimilation window, and a focus on approaches for maintaining balance during the EnkF update is focused on.
Ensemble Data Assimilation without Perturbed Observations
The ensemble Kalman filter (EnKF) is a data assimilation scheme based on the traditional Kalman filter update equation. An ensemble of forecasts are used to estimate the background-error covariances
Improving Assimilation of Radiance Observations by Implementing Model Space Localization in an Ensemble Kalman Filter
Experiments using the National Oceanic and Atmospheric Administration Finite‐Volume Cubed‐Sphere Dynamical Core Global Forecasting System (FV3GFS) reveal that the four‐dimensional
Balance and Ensemble Kalman Filter Localization Techniques
Abstract In ensemble Kalman filter (EnKF) data assimilation, localization modifies the error covariance matrices to suppress the influence of distant observations, removing spurious long-distance
...
...