scholarly journals Multiscale Representation of Observation Error Statistics in Data Assimilation

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1460
Author(s):  
Vincent Chabot ◽  
Maëlle Nodet ◽  
Arthur Vidard
Keyword(s):  
Data Assimilation ◽  
Real Life ◽  
Covariance Matrices ◽  
Multiscale Modelling ◽  
Observation Error ◽  
Error Statistics ◽  
Life Data ◽  
Promising Tool ◽  
Error Covariance ◽  
Real Life Data

Accounting for realistic observation errors is a known bottleneck in data assimilation, because dealing with error correlations is complex. Following a previous study on this subject, we propose to use multiscale modelling, more precisely wavelet transform, to address this question. This study aims to investigate the problem further by addressing two issues arising in real-life data assimilation: how to deal with partially missing data (e.g., concealed by an obstacle between the sensor and the observed system), and how to solve convergence issues associated with complex observation error covariance matrices? Two adjustments relying on wavelets modelling are proposed to deal with those, and offer significant improvements. The first one consists of adjusting the variance coefficients in the frequency domain to account for masked information. The second one consists of a gradual assimilation of frequencies. Both of these fully rely on the multiscale properties associated with wavelet covariance modelling. Numerical results on twin experiments show that multiscale modelling is a promising tool to account for correlations in observation errors in realistic applications.

scholarly journals Estimating Observation Error Statistics Using a Robust Filter Method for Data Assimilation

2020 ◽  
Author(s):  
Yue Wang ◽  
Yulong Bai
Keyword(s):  
Data Assimilation ◽  
Error Estimation ◽  
Filter Method ◽  
Robust Filtering ◽  
Observation Error ◽  
Error Statistics ◽  
H Infinity ◽  
Error Covariance ◽  
Lorenz 96 ◽  
Data Assimilation System

For the data assimilation algorithms, the observation error covariance plays an important role, because they control the weight that is given to the model forecast and to the observation in the solution, i.e., the analysis. In order to easily calculate, we often assume observation to be a diagonal matrix, however, the observation errors are correlated to the state and have a certain dependence on time, such as certain observing types which are remotely sensed. In this work, we obtain the time-dependent and correlated observation error by the method of observation error estimation in the data assimilation system. We combine the ensemble time-local H-infinity filter (EnTLHF) with an estimate of observation error covariance matrix, named ensemble time-local H-infinity filter with observation error covariance estimation (EnTLHF-R). In the experiment, a classical nonlinear Lorenz-96 model to evaluate the performance of new method is used. The results show that the robust filtering with observation error estimation is more accurate, more robust, and the filtering is more stable.

scholarly journals A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation

2020 ◽  
Vol 148 (10) ◽  
pp. 3973-3994 ◽  
Author(s):  
Pierre Tandeo ◽  
Pierre Ailliot ◽  
Marc Bocquet ◽  
Alberto Carrassi ◽  
Takemasa Miyoshi ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Current Data ◽  
Covariance Matrices ◽  
Estimation Methods ◽  
Observation Error ◽  
Correlated Noise ◽  
Unified Framework ◽  
Error Covariance ◽  
Error Terms ◽  
Observation Space

AbstractData assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as additive Gaussian noise, specified by their covariance matrices and , respectively. These error covariances, and specifically their respective amplitudes, determine the weights given to the background (i.e., the model forecasts) and to the observations in the solution of data assimilation algorithms (i.e., the analysis). Consequently, and matrices significantly impact the accuracy of the analysis. This review aims to present and to discuss, with a unified framework, different methods to jointly estimate the and matrices using ensemble-based data assimilation techniques. Most of the methods developed to date use the innovations, defined as differences between the observations and the projection of the forecasts onto the observation space. These methods are based on two main statistical criteria: 1) the method of moments, in which the theoretical and empirical moments of the innovations are assumed to be equal, and 2) methods that use the likelihood of the observations, themselves contained in the innovations. The reviewed methods assume that innovations are Gaussian random variables, although extension to other distributions is possible for likelihood-based methods. The methods also show some differences in terms of levels of complexity and applicability to high-dimensional systems. The conclusion of the review discusses the key challenges to further develop estimation methods for and . These challenges include taking into account time-varying error covariances, using limited observational coverage, estimating additional deterministic error terms, or accounting for correlated noise.

scholarly journals A Projection Method for the Estimation of Error Covariance Matrices for Variational Data Assimilation in Ocean Modelling

2021 ◽  
Vol 9 (12) ◽  
pp. 1461
Author(s):  
Jose M. Gonzalez-Ondina ◽  
Lewis Sampson ◽  
Georgy I. Shapiro
Keyword(s):  
Data Assimilation ◽  
Projection Method ◽  
Covariance Function ◽  
Covariance Matrices ◽  
New Method ◽  
Observation Error ◽  
Covariance Functions ◽  
Error Covariance ◽  
Best Fit ◽  
True Values

Data assimilation methods are an invaluable tool for operational ocean models. These methods are often based on a variational approach and require the knowledge of the spatial covariances of the background errors (differences between the numerical model and the true values) and the observation errors (differences between true and measured values). Since the true values are never known in practice, the error covariance matrices containing values of the covariance functions at different locations, are estimated approximately. Several methods have been devised to compute these matrices, one of the most widely used is the one developed by Hollingsworth and Lönnberg (H-L). This method requires to bin (combine) the data points separated by similar distances, compute covariances in each bin and then to find a best fit covariance function. While being a helpful tool, the H-L method has its limitations. We have developed a new mathematical method for computing the background and observation error covariance functions and therefore the error covariance matrices. The method uses functional analysis which allows to overcome some shortcomings of the H-L method, for example, the assumption of statistical isotropy. It also eliminates the intermediate steps used in the H-L method such as binning the innovations (differences between observations and the model), and the computation of innovation covariances for each bin, before the best-fit curve can be found. We show that the new method works in situations where the standard H-L method experiences difficulties, especially when observations are scarce. It gives a better estimate than the H-L in a synthetic idealised case where the true covariance function is known. We also demonstrate that in many cases the new method allows to use the separable convolution mathematical algorithm to increase the computational speed significantly, up to an order of magnitude. The Projection Method (PROM) also allows computing 2D and 3D covariance functions in addition to the standard 1D case.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

The epidemiology of thyroid cancer within a center of excellence in Athens: Real life data

2017 ◽  
Author(s):  
Eleni Pantazi ◽  
Alexios Travlos ◽  
Evaggelia Vogiatzi ◽  
Ifigenia Kostoglou-Athanassiou
Keyword(s):  
Thyroid Cancer ◽  
Real Life ◽  
Life Data ◽  
Center Of Excellence ◽  
Real Life Data
Sign in / Sign up

Export Citation Format

Share Document