Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analog ensemble data assimilation and a method for constructing analogs
  with variational autoencoders   It is proposed to use analogs of the forecast mean to generate an ensemble of
perturbations for use in ensemble optimal interpolation (EnOI) or ensemble
variational (EnVar) methods. A new method of constructing analogs using
variational autoencoders (VAEs; a machine learning method) is proposed. The
resulting analog methods using analogs from a catalog (AnEnOI), and using
constructed analogs (cAnEnOI), are tested in the context of a multiscale
Lorenz-`96 model, with standard EnOI and an ensemble square root filter for
comparison. The use of analogs from a modestly-sized catalog is shown to
improve the performance of EnOI, with limited marginal improvements resulting
from increases in the catalog size. The method using constructed analogs
(cAnEnOI) is found to perform as well as a full ensemble square root filter,
and to be robust over a wide range of tuning parameters.

18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph