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Author: search.filters.author.Oliveira-Júnior, José Francisco de

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    Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America
    (2023-08-01)
    Shah, Munawar
    ;
    Shahzad, Rasim
    ;
    Jamjareegulgarn, Punyawi
    ;
    Ghaffar, Bushra
    ;
    Oliveira-Júnior, José Francisco de
    The identification of atmospheric and ionospheric variations through multiple remote sensing and global navigation satellite systems (GNSSs) has contributed substantially to the development of the lithosphere-atmosphere-ionosphere coupling (LAIC) phenomenon over earthquake (EQ) epicenters. This study presents an approach for investigating the Petrolia EQ (Mw 6.2; dated 20 December 2021) and the Monte Cristo Range EQ (Mw 6.5; dated 15 May 2020) through several parameters to observe the precursory signals of various natures. These parameters include Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), Outgoing Longwave Radiations (OLRs), and vertical Total Electron Content (TEC), and these are used to contribute to the development of LAIC in the temporal window of 30 days before and 15 days after the main shock. We observed a sharp increase in the LST in both the daytime and nighttime of the Petrolia EQ, but only an enhancement in the daytime LST for the Monte Cristo Range EQ within 3–7 days before the main shock. Similarly, a negative peak was observed in RH along with an increment in the OLR 5–7 days prior to both impending EQs. Furthermore, the Monte Cristo Range EQ also exhibited synchronized ionospheric variation with other atmospheric parameters, but no such co-located and synchronized anomalies were observed for the Petrolia EQ. We also applied machine learning (ML) methods to confirm these abrupt variations as anomalies to further aid certain efforts in the development of the LAIC in order to forecast EQs in the future. The ML methods also make prominent the variation in the different data.
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    Urban rainfall in the Capitals of Brazil: Variability, trend, and wavelet analysis
    (2022-04-01)
    Oliveira-Júnior, José Francisco de
    ;
    Correia Filho, Washington Luiz Félix
    ;
    Monteiro, Lua da Silva
    ;
    Shah, Munawar
    ;
    Hafeez, Amna
    The patterns of urban rainfall in Brazil's capitals are critical, due to population growth and extreme weather. Therefore, the objectives are: i) to identify homogeneous rainfall groups and meteorological systems, ii) to evaluate the trend of the monthly rainfall time series and iii) to apply wavelet analysis to estimate the variance at different frequencies in the rainfall series in the capitals of the Brazil. Monthly rainfall data during 1960–2020 for 27 stations located in the capitals of Brazil were used. The data were flawed, and data imputation (mtsdi package) was applied via Fully Conditional Specification (FCS). Rainfall data were submitted to descriptive, exploratory statistics (boxplot), multivariate analysis (Cluster Analysis - CA) and the Mann-Kendall (MK) test. Seven CA methods (Ward, Single, Complete, Average, McQuity, Median and Centroid) were tested using the cophenetic correlation coefficient (CCC) with a significance level of 5%, the Average method obtained CCC > 0.81 (S). The CA identified three homogeneous regions (G1, G2 and G3) in the capitals of Brazil. The G1 group is formed by the capitals of the Northeast of Brazil (NEB), except for Boa Vista, (North of Brazil - NB). The G2 group is the largest group formed by the capitals of the Midwest (MWB), Southeast (SEB) and South (SB) of Brazil. The G3 group is the smallest group, with the capitals of the NB and some of the NEB. The capitals with the category of significant growth trend were only Porto Alegre and Florianópolis (SB), Vitória (SEB) and Belém (NB). The category of non-significant increase trend prevailed in most capitals of Brazil, with emphasis on the corridor formed between the NB and the Center-South, except for Natal (NEB). The without trend category prevailed in the North, Northeast and Midwest regions of Brazil. Monthly precipitation analyzes for trend detection purposes via Wavelet Analysis showed that ENSO phases are significant in rainfall variability in Brazilian capitals.
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    Wet and dry periods in the state of Alagoas (Northeast Brazil) via Standardized Precipitation Index
    (2021-11-01)
    Oliveira-Júnior, José Francisco de
    ;
    de Gois, Givanildo
    ;
    Silva, Iago José de Lima
    ;
    de Oliveira Souza, Edson
    ;
    Jardim, Alexandre Maniçoba da Rosa Ferraz
    The state of Alagoas has its economic base directed to agriculture, and the knowledge of wet and dry periods is fundamental for the success of agricultural enterprises. The objective was to define the wet and dry periods in the state of Alagoas via multivariate analysis applied to the Standardized Precipitation Index (SPI). Monthly rainfall data for 20 weather stations from 1960 to 2016 were used, with fault filling via imputation. Moreover, Cluster analysis (CA) was applied to the time series (definition of homogeneous regions) and SPI-12 (annual) - (determination of dry and wet spell periods). The annual accumulated rainfall showed two distinct periods: 1960 to 1990 (P1) and 1990 to 2016 (P2), according to Pettitt test. Both periods alternate according to the phases of La Niña and Neutral (rainy years) and El Niño (dry years). In the monthly rainfall, only the Eastern part of Alagoas increased the rainy months during long time series. In tested connection methods, the average connection, also known as cophenetic correlation coefficient (CCC = 0.8760), represented homogeneous groups of rainfall, featuring two regions: one on the coast (G<inf>1</inf>) and another hinterland and arid (G<inf>2</inf>); and two non-homogeneous groups (NA) in the state. The annual SPI helped identify dry and wet periods, regardless of El Niño-Southern Oscillation (ENSO) categorization. The SPI-12 categories show high annual and decadal variability in the groups, except for the extremely dry and wet categories. There is greater variability in dry and wet periods near the coast than hinterland of the state.