Assessing the impact of lightning data assimilation in the WRF model

• Ano de defesa: 2019
• Autor(a) principal: Vanderlei Rocha de Vargas Junior
• Orientador(a): Osmar Pinto Junior, Dirceu Luis Herdies
• Banca de defesa: Kleber Pinheiro Naccarato, Fabrício Pereira Harter, Mário Francisco Leal Quadro
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Instituto Nacional de Pesquisas Espaciais (INPE)
• Programa de Pós-Graduação: Programa de Pós-Graduação do INPE em Geofísica Espacial/Ciências Atmosféricas
• País: BR
• Link de acesso: http://urlib.net/sid.inpe.br/mtc-m21c/2019/10.29.16.19

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.