@InProceedings{VendrascoHerdAnge:2015:Co3DRa,
author = "Vendrasco, Eder Paulo and Herdies, Dirceu Luis and Angelis, Carlos
Frederico",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} 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",
year = "2015",
organization = "Conference on Radar Meteorology, 37.",
abstract = "It is known from previous studies that radar data assimilation can
improve the short-range forecast of precipitation, mainly when
radial wind and reflectivity are available. However, from our
experience the radar data assimilation, when using the 3D-Var
technique, can produce spurious precipitation and large errors on
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 3D-Var 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 3D-Var analysis with the constraint is
more accurate when verified against an independent dataset. The
performance of this new constraint on improving precipitation
forecast is tested using six convective cases and verified against
radar-derived precipitation by four skill indices. All skill
indices show improved forecast when using the methodology
presented in this study.",
conference-location = "Norman, OK",
conference-year = "14-18 Sept.",
urlaccessdate = "28 abr. 2024"
}