Editorial Type:
Article
Article Type:
Research Article

Constraining a 3DVAR Radar Data Assimilation System with Large-Scale Analysis to Improve Short-Range Precipitation Forecasts

Eder Paulo Vendrasco National Institute for Space Research (INPE), Cachoeira Paulista, Sao Pãulo, Brazil

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Juanzhen Sun National Center for Atmospheric Research,* Boulder, Colorado

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Dirceu Luis Herdies National Institute for Space Research (INPE), Cachoeira Paulista, São Paulo, Brazil

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Carlos Frederico de Angelis National Center for Monitoring and Early Warning of Natural Disaster, Cachoeira Paulista, São Paulo, Brazil

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Print Publication:
01 Mar 2016
DOI:
https://doi.org/10.1175/JAMC-D-15-0010.1
Page(s):
673–690
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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.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Eder Paulo Vendrasco, Rodovia Presidente Dutra, Km 39, Cachoeira Paulista, São Paulo CEP 16300-000, Brazil. E-mail: eder.vendrasco@cptec.inpe.br

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.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Eder Paulo Vendrasco, Rodovia Presidente Dutra, Km 39, Cachoeira Paulista, São Paulo CEP 16300-000, Brazil. E-mail: eder.vendrasco@cptec.inpe.br
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