Soil model parameter estimation with ensemble data assimilation
TL;DR: In this paper, a parameter estimation problem in context of ensemble data assimilation is addressed, where the parameters corresponding to the emissivity and to the effective depth between the surface and the lowest atmospheric model level are estimated together with the initial conditions for temperature.
read more
About: This article is published in Atmospheric Science Letters. The article was published on 01 Apr 2009. and is currently open access. The article focuses on the topics: Data assimilation & Estimation theory.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
The Ensemble Kalman Filter: Theoretical formulation and practical implementation
Geir Evensen
- 01 Apr 2003
TL;DR: The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it as mentioned in this paper, and also presents new ideas and alternative interpretations which further explain the success of the EnkF.
2.9K
Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
TL;DR: Ruiz, Juan Jose as mentioned in this paper, et al. as mentioned in this paper presented a study on the use of Fisica in Nacional del Nordeste (N. Chile) and N. Argentina.
142
Ensemble data assimilation methods for improving river water quality forecasting accuracy
TL;DR: To improve accuracy and skill of water quality forecasts along the Yeongsan River in South Korea three different ensemble data assimilation (DA) methods have been investigated: the traditional Ensemble Kalman Filter (EnKF) and two related algorithms that offer either possibilities to improve initial conditions for non-linear models or reduce computation time by using a (smaller) time-lagged ensemble to estimate the Kalman gain.
68
•Posted Content
Deterministic treatment of model error in geophysical data assimilation
TL;DR: In this article, the model error is treated as a deterministic process fully correlated in time, which allows for the derivation of the evolution equations for the relevant moments of the model errors required in data assimilation procedures, along with an approximation for application to large numerical models typical of environmental science.
11
Deterministic treatment of model error in geophysical data assimilation
Alberto Carrassi,Stéphane Vannitsem +1 more
- 04 Jun 2016
TL;DR: In this paper, the model error is treated as a deterministic process correlated in time, which allows for the derivation of the evolution equations for the relevant moments of model error statistics required in data assimilation procedures, along with an approximation for application to large numerical models typical of environmental science.
11
References
The Ensemble Kalman Filter: theoretical formulation and practical implementation
TL;DR: A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias, and an ensemble based optimal interpolation scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications.
The Ensemble Kalman Filter: Theoretical formulation and practical implementation
Geir Evensen
- 01 Apr 2003
TL;DR: The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it as mentioned in this paper, and also presents new ideas and alternative interpretations which further explain the success of the EnkF.
2.9K
Data Assimilation Using an Ensemble Kalman Filter Technique
TL;DR: In this article, the authors proposed an ensemble Kalman filter for data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as Ensemble Kalman filtering) in an idealized environment.
An Ensemble Adjustment Kalman Filter for Data Assimilation
TL;DR: In this paper, an ensemble adjustment Kalman filter is proposed to estimate the probability distribution of the state of a model given a set of observations using Monte Carlo approximations to the nonlinear filter.
Ensemble Data Assimilation without Perturbed Observations
TL;DR: In this paper, the EnSRF algorithm is proposed, which uses the traditional Kalman gain for updating the ensemble mean but uses a reduced loss to update deviations from the EnKF mean.
1.7K