scholarly journals A local ensemble transform Kalman particle filter for convective‐scale data assimilation

2017 ◽  
Vol 144 (713) ◽  
pp. 1279-1296 ◽  
Author(s):  
Sylvain Robert ◽  
Daniel Leuenberger ◽  
Hans R. Künsch
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Convective Scale ◽  
Scale Data ◽  
Ensemble Transform

scholarly journals Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling

2018 ◽  
Vol 25 (4) ◽  
pp. 731-746 ◽  
Author(s):  
Sangeetika Ruchi ◽  
Svetlana Dubinkina
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Particle Filter ◽  
Reservoir Modeling ◽  
Model Parameters ◽  
Uncertain Parameters ◽  
Initial Ensemble ◽  
Leading Mode ◽  
Ensemble Transform

Abstract. Over the years data assimilation methods have been developed to obtain estimations of uncertain model parameters by taking into account a few observations of a model state. The most reliable Markov chain Monte Carlo (MCMC) methods are computationally expensive. Sequential ensemble methods such as ensemble Kalman filters and particle filters provide a favorable alternative. However, ensemble Kalman filter has an assumption of Gaussianity. Ensemble transform particle filter does not have this assumption and has proven to be highly beneficial for an initial condition estimation and a small number of parameter estimations in chaotic dynamical systems with non-Gaussian distributions. In this paper we employ ensemble transform particle filter (ETPF) and ensemble transform Kalman filter (ETKF) for parameter estimation in nonlinear problems with 1, 5, and 2500 uncertain parameters and compare them to importance sampling (IS). The large number of uncertain parameters is of particular interest for subsurface reservoir modeling as it allows us to parameterize permeability on the grid. We prove that the updated parameters obtained by ETPF lie within the range of an initial ensemble, which is not the case for ETKF. We examine the performance of ETPF and ETKF in a twin experiment setup, where observations of pressure are synthetically created based on the known values of parameters. For a small number of uncertain parameters (one and five) ETPF performs comparably to ETKF in terms of the mean estimation. For a large number of uncertain parameters (2500) ETKF is robust with respect to the initial ensemble, while ETPF is sensitive due to sampling error. Moreover, for the high-dimensional test problem ETPF gives an increase in the root mean square error after data assimilation is performed. This is resolved by applying distance-based localization, which however deteriorates a posterior estimation of the leading mode by largely increasing the variance due to a combination of less varying localized weights, not keeping the imposed bounds on the modes via the Karhunen–Loeve expansion, and the main variability explained by the leading mode. A possible remedy is instead of applying localization to use only leading modes that are well estimated by ETPF, which demands knowledge of which mode to truncate.

The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation

2019 ◽  
Vol 229 ◽  
pp. 208-223 ◽  
Author(s):  
Shibo Gao ◽  
Jinzhong Min ◽  
Limin Liu ◽  
Chuanyou Ren
Keyword(s):  
Data Assimilation ◽  
Convective Scale ◽  
Scale Data

scholarly journals Simulation of satellite infrared radiances for convective-scale data assimilation over the Mediterranean

2010 ◽  
Vol 115 (D15) ◽  
Author(s):  
Fanny Duffourg ◽  
Véronique Ducrocq ◽  
Nadia Fourrié ◽  
Geneviève Jaubert ◽  
Vincent Guidard
Keyword(s):  
Data Assimilation ◽  
Convective Scale ◽  
The Mediterranean ◽  
Infrared Radiances ◽  
Scale Data

Representation of model error in convective scale data assimilation

2020 ◽  
Author(s):  
Tijana Janjic ◽  
Yuefei Zeng ◽  
Alberto de Lozar ◽  
Yvonne Ruckstuhl ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Additive Noise ◽  
Model Error ◽  
Ensemble Member ◽  
Radar Reflectivity ◽  
Model Errors ◽  
Estimation Of Parameters ◽  
Convective Scale ◽  
Reflectivity Data ◽  
Scale Data

<p>Model error is one of major contributors to forecast uncertainty. In addition, statistical representations of possible model errors substantially affect the data assimilation results. We investigate variety of methods of taking into account model error in ensemble based convective scale data assimilation. This is done using the operational convection-permitting COSMO model and data assimilation system KENDA of German weather service, for a two-week convective period in May 2016 over Germany. Conventional and radar reflectivity observations are assimilated hourly by the LETKF. For example, to take into account the model error due to unresolved scales and processes, we use the additive noise with samples coming from the difference between high-resolution model run and low-resolution experiment. We compare this technique for assimilation of radar reflectivity data to other methods such as RTPS, warm bubble initialization, stochastic boundary layer perturbation and estimation of parameters. To further improve on additive noise technique, which consists of perturbing each ensemble member with a sample from a given distribution, we propose a more flexible approach in which the model error samples are treated as additional synthetic ensemble members that are used in the update step of data assimilation but are not forecasted. In this way, the rank of the model error covariance matrix can be chosen independently of the ensemble. This altered additive noise method is analyzed as well.</p>

scholarly journals Representation of Model Error in Convective‐Scale Data Assimilation: Additive Noise Based on Model Truncation Error

2019 ◽  
Vol 11 (3) ◽  
pp. 752-770 ◽  
Author(s):  
Yuefei Zeng ◽  
Tijana Janjić ◽  
Matthias Sommer ◽  
Alberto Lozar ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Truncation Error ◽  
Additive Noise ◽  
Model Error ◽  
Convective Scale ◽  
Scale Data
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