@Article{AzevedoGonįKalnWesp:2020:PeMoTe,
author = "Azevedo, Helena Barbieri de and Gon{\c{c}}alves, Luis Gustavo
Gon{\c{c}}alves de and Kalnay, Eugenia and Wespetal, Matthew",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {University of
Maryland} and {University of Maryland}",
title = "Dynamically weighted hybrid gain data assimilation: perfect model
testing",
journal = "Tellus Series A: Dynamic Meteorology and Oceanography",
year = "2020",
volume = "72",
number = "1",
month = "Jan.",
keywords = "data assimilation, hybrid systems, ensemble Kalman filter,
numerical weather prediction.",
abstract = "Hybrid systems have become the state of the art among data
assimilation methods. These systems combine the benefits of two
other systems that are traditionally used in operational weather
forecasting: an ensemble-based system and a variational system.
One of the most recently proposed hybrid approaches is called
hybrid gain (HG). It obtains the final analysis as a linear
combination of two analyses, assuming that the innovations (i.e.
the forecast and the set of observations used) between the two
data assimilation methods are identical. A perfect model
experiment was performed using the HG in the SPEEDY model to show
a new methodology to assign different weights to the two analyses,
LETKF and 3D-Var in the generation of the final analysis. Our new
approach uses, in the assignment of the weights, the ensemble
spread, considered to be a measure of uncertainty in the LETKF.
Thus, it is possible to use the estimation of the uncertainty of
the analysis that the LETKF provides, to determine where the
system should give more weight to the LETKF or the 3D-Var
analysis. For this purpose, we define a geographically varying
weighting factor alpha, which multiplies the 3D-Var analysis, as
the normalised spread for each variable at each level. Then,
(1-alpha), which decreases with increasing spread, becomes the
factor that multiplies the LETKF analysis. The underlying
mechanism of the spread-error relationship is explained using a
toy model experiment. The results are very encouraging: the
original HG and the new weighted HG analyses have similar high
quality and are better than both 3D-Var and LETKF. However, the
dynamically weighted HG analyses are significantly more balanced
than the original HG analyses are, which has probably contributed
to the consistently improved performance observed in the weighted
HG, which increases with time throughout the 5-day forecasts.",
doi = "10.1080/16000870.2020.1835310",
url = "http://dx.doi.org/10.1080/16000870.2020.1835310",
issn = "0280-6495",
language = "en",
targetfile = "azevedo_dynamically.pdf",
urlaccessdate = "09 maio 2024"
}