@PhDThesis{VargasJr:2020:AsImLi,
author = "Vargas Junior, Vanderlei Rocha de",
title = "Assessing the impact of lightning data assimilation in the WRF
model",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2020",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2019-10-09",
keywords = "assimilation, lightning, BrasilDAT, WRF, assimila{\c{c}}{\~a}o,
rel{\^a}mpagos.",
abstract = "The increasing dependence of society on weather-sensitive
technologies as well as the expansion of urban centers to risk
areas are making meteorological modeling even more important in
the last decades. Moreover, the development of powerful
computational systems has made the implementation of new physical
models capable of representing more precisely the atmosphere
inducing several sectors of the economy to become even more
dependent on weather forecasting. This present work is the first
one to apply a lightning data assimilation technique in order to
improve the short-term weather forecasting in South America. The
use of this new data source in the assimilation procedures has the
potential to increase the efficiency of the initialization methods
currently used in meteorological operation centers, especially in
South America. The main goal of this research was to implement and
improve a data assimilation algorithm responsible for inserting
lightning data into the WRF model. Specifically, it was intended
to evaluate the performance of the experiments with lightning data
assimilation comparing them with the experiments with no
assimilation procedures applied, focusing on the impact in
short-term forecasts. The area selected for this work was set in
South America specifically over the southern portion of Brazil.
This area is well covered by many types of observation stations
and at the same time, it has favorable conditions for the
occurrence of several meteorological systems which implies in the
occurrence of many storms with a high incidence of lightning. In
order to perform the simulations, evaluate the experiments and
track the meteorological system it was used data from different
sources such as: Precipitation data from the National Institute of
Meteorology; Lightning data from BrasilDAT provided by the
Atmospheric Electricity Group of the National Institute for Space
Research (INPE); Satellite images from GOES-16 and synoptic
weather charts from the Center for Weather Forecasting and Climate
Studies of INPE; and initial and boundary conditions from the GFS
model provided by the Computational and Information Systems
Laboratory from University Corporation for Atmospheric Research.
This study used the WRF-ARW model version 3.9.1.1 and the WRFDA
system version 3.9.1 with the 3DVAR methodology. The assimilation
algorithm developed in this study to assimilate lightning data and
correct the initial conditions of the model was based on the
equation developed by Fierro et al. (2012). This study proceeded
with three different experiments during the occurrence of two
distinct meteorological events aiming to assess the assimilation
algorithm implemented here. The experiments were basically divided
in: control (CTRL), where no assimilation procedures were used;
lightning data assimilation (LIGHT), where lightning data was
assimilated using the equation developed by Fierro et al. (2012);
and ALIGHT, where lightning data was assimilated using the
equation with an adaptative relative humidity threshold developed
in this study. Based on the experiments performed in this study,
it was possible to conclude that in general, the use of the
Lightning Data Assimilation System improved the short-term weather
forecast for the precipitation field induced by large-scale
systems, especially when the correction in the relative humidity
threshold was applied. Additionally, the assimilation algorithm
also improved the timing and positioning of a squall line that
affected the study area possibly due to the correct representation
of cold pools during the assimilation process. In the second case
analyzed, the assimilation algorithm improved the representation
of the precipitation field in a few simulation cycles but it was
noticed that when the convection is associated with thermal
forcing the assimilation of lightning data using the algorithm
presented in this study had a negative impact in the experiments.
The assimilation methodology for lightning data presented in this
study represents a significative contribution to the data
assimilation field. The operational use of an alternative data
source such as lightning has the potential to improve the
shortterm forecasts impacting positively several sectors of
society. RESUMO: A crescente depend{\^e}ncia da sociedade em
tecnologias sens{\'{\i}}veis ao tempo bem como a expans{\~a}o
de centros urbanos para {\'a}reas de risco est{\~a}o tornando a
modelagem meteorol{\'o}gica ainda mais importante nas
{\'u}ltimas d{\'e}cadas. Al{\'e}m disso, o desenvolvimento de
sistemas computacionais mais eficientes tornou a
implementa{\c{c}}{\~a}o de novos modelos f{\'{\i}}sicos
n{\~a}o apenas capazes de representar com mais precis{\~a}o a
atmosfera, mas tamb{\'e}m fez com que v{\'a}rios setores da
economia se tornassem ainda mais dependentes da previs{\~a}o do
tempo. Este trabalho {\'e} o primeiro a aplicar uma t{\'e}cnica
de assimila{\c{c}}{\~a}o de dados de rel{\^a}mpagos a fim de
melhorar as previs{\~o}es meteorol{\'o}gicas de curto prazo na
Am{\'e}rica do Sul. O uso dessa nova fonte de dados nos
procedimentos de assimila{\c{c}}{\~a}o tem o potencial de
aumentar a efici{\^e}ncia dos m{\'e}todos de
inicializa{\c{c}}{\~a}o atualmente utilizados em centros de
opera{\c{c}}{\~o}es meteorol{\'o}gicas, especialmente na
Am{\'e}rica do Sul. O principal objetivo desta pesquisa foi
implementar e aperfei{\c{c}}oar um algoritmo de
assimila{\c{c}}{\~a}o de dados respons{\'a}vel pela
inser{\c{c}}{\~a}o de dados de rel{\^a}mpagos no modelo WRF.
Especificamente, pretendeu-se avaliar o desempenho dos
experimentos com assimila{\c{c}}{\~a}o de dados de
rel{\^a}mpagos, comparando-os com experimentos sem procedimentos
de assimila{\c{c}}{\~a}o de dados, com foco no impacto dos
algoritmos de assimila{\c{c}}{\~a}o nas previs{\~o}es de curto
prazo. A {\'a}rea selecionada para este trabalho foi definida na
Am{\'e}rica do Sul, especificamente na parte sul do Brasil. Esta
{\'a}rea apresenta uma boa cobertura de esta{\c{c}}{\~o}es de
observa{\c{c}}{\~a}o e, ao mesmo tempo, possui
condi{\c{c}}{\~o}es favor{\'a}veis para a ocorr{\^e}ncia de
v{\'a}rios sistemas meteorol{\'o}gicos, o que implica na
ocorr{\^e}ncia de muitas tempestades com alta incid{\^e}ncia de
rel{\^a}mpagos. Para realizar as simula{\c{c}}{\~o}es, avaliar
os experimentos e acompanhar os sistemas meteorol{\'o}gicos,
foram utilizados dados de diferentes fontes, tais como: Dados de
precipita{\c{c}}{\~a}o do Instituto Nacional de Meteorologia;
Dados de rel{\^a}mpagos da BrasilDAT fornecidos pelo Grupo de
Eletricidade Atmosf{\'e}rica do Instituto Nacional de Pesquisas
Espaciais (INPE); Imagens de sat{\'e}lite do GOES-16 e cartas
sin{\'o}ticas do Centro de Previs{\~a}o Meteorol{\'o}gica e
Estudos Clim{\'a}ticos do INPE; e condi{\c{c}}{\~o}es iniciais
e de contorno do modelo GFS fornecido pelo Computational and
Information Systems Laboratory from University Corporation for
Atmospheric Research. Este estudo utilizou o modelo WRF-ARW
vers{\~a}o 3.9.1.1 e o sistema WRFDA vers{\~a}o 3.9.1 com a
metodologia 3DVAR. O algoritmo de assimila{\c{c}}{\~a}o
desenvolvido neste estudo para assimilar dados de rel{\^a}mpagos
e corrigir as condi{\c{c}}{\~o}es iniciais do modelo foi baseado
na equa{\c{c}}{\~a}o desenvolvida por Fierro et al. (2012). Este
estudo prosseguiu com tr{\^e}s experimentos diferentes durante a
ocorr{\^e}ncia de dois eventos meteorol{\'o}gicos distintos, com
o objetivo de avaliar o algoritmo de assimila{\c{c}}{\~a}o
implementado. Os experimentos foram basicamente divididos em:
controle (CTRL), onde n{\~a}o foram utilizados procedimentos de
assimila{\c{c}}{\~a}o, em assimila{\c{c}}{\~a}o de dados de
rel{\^a}mpagos (LIGHT), onde os dados de rel{\^a}mpagos foram
assimilados, e em assimila{\c{c}}{\~a}o de dados de
rel{\^a}mpagos com um limiar de umidade relativa adaptativo
(ALIGHT). Com base nos experimentos realizados neste estudo, foi
poss{\'{\i}}vel concluir que, em geral, o uso do Sistema de
Assimila{\c{c}}{\~a}o de Dados de Rel{\^a}mpagos melhorou a
previs{\~a}o de curto prazo para o campo de
precipita{\c{c}}{\~a}o induzido por sistemas de grande escala,
especialmente quando a corre{\c{c}}{\~a}o do limiar de umidade
relativa do ar foi aplicada. Al{\'e}m disso, o algoritmo de
assimila{\c{c}}{\~a}o tamb{\'e}m melhorou o timing e o
posicionamento de uma linha de tempestade que afetou a {\'a}rea
de estudo, possivelmente devido {\`a} melhor
representa{\c{c}}{\~a}o das piscinas frias durante o processo de
assimila{\c{c}}{\~a}o. No segundo caso analisado, o algoritmo de
assimila{\c{c}}{\~a}o melhorou a representa{\c{c}}{\~a}o do
campo de precipita{\c{c}}{\~a}o em alguns ciclos de
simula{\c{c}}{\~a}o, mas notou-se que, quando a
convec{\c{c}}{\~a}o est{\'a} associada {\`a} for{\c{c}}antes
t{\'e}rmicas, a assimila{\c{c}}{\~a}o de dados de
rel{\^a}mpagos usando o algoritmo apresentado neste estudo teve
um impacto negativo nos experimentos. A metodologia de
assimila{\c{c}}{\~a}o de dados de rel{\^a}mpagos apresentada
neste estudo representa uma contribui{\c{c}}{\~a}o significativa
para o campo de assimila{\c{c}}{\~a}o de dados. O uso
operacional de uma fonte de dados alternativa como os
rel{\^a}mpagos tem o potencial de melhorar as previs{\~o}es de
curto prazo, impactando positivamente v{\'a}rios setores da
sociedade.",
committee = "Saba, Marcelo Magalh{\~a}es Fares (presidente) and Pinto Junior,
Osmar (orientador) and Herdies, Dirceu Luis (orientador) and
Naccarato, Kleber Pinheiro and Harter, Fabr{\'{\i}}cio Pereira
and Quadro, M{\'a}rio Francisco Leal",
englishtitle = "Avalia{\c{c}}{\~a}o do impacto da assimila{\c{c}}{\~a}o de
rel{\^a}mpagos no modelo WRF",
language = "en",
pages = "115",
ibi = "8JMKD3MGP3W34R/3UAML3H",
url = "http://urlib.net/ibi/8JMKD3MGP3W34R/3UAML3H",
targetfile = "publicacao.pdf",
urlaccessdate = "18 abr. 2024"
}