@Article{VendrascoSunHerdAnge:2016:Co3DRa,
author = "Vendrasco, Eder Paulo and Sun, Juanzhen and Herdies, Dirceu Luis
and Angelis, Carlos Frederico de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {National
Center for Atmospheric Research} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Constraining a 3D-Var radar data assimilation system with
large-scale analysis to improve short-range precipitation
forecast",
journal = "Journal of Applied Meteorology and Climatology",
year = "2016",
volume = "55",
number = "3",
pages = "673--690",
keywords = "Data assimilation, Nowcasting, Numerical weather
prediction/forecasting, Short-range prediction.",
abstract = "It is known from previous studies that radar data assimilation can
improve short-range forecasts of precipitation, mainly when radial
wind and reflectivity are available. However, from the authors'
experience radar data assimilation, when using the
three-dimensional variational data assimilation (3DVAR) technique,
can produce spurious precipitation results and large errors in the
position and amount of precipitation. One possible reason for the
problem is attributed to the lack of proper balance in the
dynamical and microphysical fields. This work attempts to minimize
this problem by adding a large-scale analysis constraint in the
cost function. The large-scale analysis constraint is defined by
the departure of the high-resolution 3DVAR analysis from a
coarser-resolution large-scale analysis. It is found that this
constraint is able to guide the assimilation process in such a way
that the final result still maintains the large-scale pattern,
while adding the convective characteristics where radar data are
available. As a result, the 3DVAR analysis with the constraint is
more accurate when verified against an independent dataset. The
performance of this new constraint on improving precipitation
forecasts is tested using six convective cases and verified
against radar-derived precipitation by employing four skill
indices. All of the skill indices show improved forecasts when
using the methodology presented in this paper.",
doi = "10.1175/JAMC-D-15-0010.1",
url = "http://dx.doi.org/10.1175/JAMC-D-15-0010.1",
issn = "1558-8432 and 1558-8424",
label = "lattes: 3752951275341381 3 VendrascoSunHerdAnge:2015:Co3DRa",
language = "en",
urlaccessdate = "03 jun. 2024"
}