Diagnosis of background errors for radiances and other observable quantities in a variational data assimilation scheme, and the explanation scheme, and the explanation of a case of poor convergence

2000 ◽  
Vol 126 (565) ◽  
pp. 1455-1472 ◽  
Author(s):  
ERIK ANDERSSON ◽  
MIKE FISHER ◽  
ROSEMARY MUNRO ◽  
ANTHONY MCNALLY
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Poor Convergence ◽  
Assimilation Scheme

The Met. Office global three-dimensional variational data assimilation scheme

2000 ◽  
Vol 126 (570) ◽  
pp. 2991-3012 ◽  
Author(s):  
A. C. Lorenc ◽  
S. P. Ballard ◽  
R. S. Bell ◽  
N. B. Ingleby ◽  
P. L. F. Andrews ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

A Parallel Three-dimensional Variational Data Assimilation Scheme

2011 ◽  
Author(s):  
L. D’Amore ◽  
R. Arcucci ◽  
L. Marcellino ◽  
A. Murli ◽  
Theodore E. Simos ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

scholarly journals Displacing Potential Vorticity Structures by the Assimilation of Pseudo-Observations

2011 ◽  
Vol 139 (2) ◽  
pp. 549-565 ◽  
Author(s):  
Yann Michel
Keyword(s):  
Data Assimilation ◽  
Potential Vorticity ◽  
Variational Data Assimilation ◽  
Tuning Parameter ◽  
Data Assimilation Scheme ◽  
The Difference ◽  
Upper Level ◽  
Meteorological Features ◽  
Assimilation Scheme ◽  
Dry Intrusions

Abstract Classic formulations of variational data assimilation in amplitude space are not able to directly handle observations that measure the geographical positions of meteorological features like fronts and vortices. These observations can be derived from satellite images, as is already the case for tropical cyclones. Although some advanced data assimilation algorithms have been specifically designed to tackle the problem, a widespread way of dealing with this information is to use so-called bogussing pseudo-observations: user-specified artificial observations are inserted in a traditional data assimilation scheme. At the midlatitudes, there is a relationship between dry intrusions in water vapor images and upper-level potential vorticity structures. Some prior work has also shown that it was possible to automatically identify dry intrusions with tracking algorithms. The difference of positions between model and image dry intrusions could therefore be used as observations of the misplacement of potential vorticity structures. One strategy to achieve the displacement of potential vorticity anomalies is to sample them, and assimilate the values at displaced locations. The uncertainty of these pseudo-observations is left as a tuning parameter to try to make the displacement both effective and robust. A simple one-dimensional assimilation model is used to study the displacement of curves defined by Gaussian humps. The concept is then illustrated in realistic examples from real synoptic systems, where pseudo-observations of potential vorticity are incorporated in a global variational data assimilation scheme. Overall and despite reasonable optimization, the results contain artifacts. This suggests that the use of pseudo-observations to displace identifiable structures is not an effective strategy.

Learning missing part of physics-based models within a variational data assimilation scheme

2020 ◽  
Author(s):  
Arthur Filoche ◽  
Julien Brajard ◽  
Anastase Charantonis ◽  
Dominique Béréziat
Keyword(s):  
Machine Learning ◽  
Data Assimilation ◽  
Hybrid Model ◽  
Automatic Differentiation ◽  
Variational Data Assimilation ◽  
Model Parameters ◽  
Time Data ◽  
Physical Knowledge ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

<p>The analogy between data assimilation and machine learning has already been shown and is still being investigated to address the problem of improving physics-based models. Even though both techniques learn from data, machine learning focuses on inferring model parameters while data assimilation concentrates on hidden system state estimation with the help of a dynamical model. <br> <br>Also, neural networks and more precisely ResNet-like architectures can be seen as dynamical systems and numerical schemes, respectively. They are now considered state of the art in a vast amount of tasks involving spatio-temporal forecasting. But to train such networks, one needs dense and representative data which is rarely the case in earth sciences. At the same time, data assimilation offers a proper Bayesian framework allowing to learn from partial, noisy and indirect observations. Thus, each of this field can profit from the other by providing either a learnable class of dynamical models or dense data sets.</p><p>In this work, we benefit from powerful and flexible tools provided by the deep learning community based on automatic differentiation that are clearly suitable for variational data assimilation, avoiding explicit adjoint modelling. We use a hybrid model divided into 2 terms. The first term is a numerical scheme that comes from the discretisation of physics-based equations, the second is a convolutional neural network that represents the unresolved part of the dynamics. From the Data Assimilation point of view, our network can be seen as a particular parametrisation of the model error. We then jointly learn this parameterisation and estimate hidden system states within a variational data assimilation scheme. Indirectly, the issue of incorporating physical knowledge into machine learning models is also addressed. </p><p>We show that the hybrid model improves forecast skill compared to traditional data assimilation techniques. The generalisation of the method on different models and data will also be discussed.</p>

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