Navegando por Orientadores "MOTA, Galdino Viana"

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Acesso aberto (Open Access)
Características e distribuição das descargas atmosféricas e dos sistemas precipitantes produtores de raios na Amazônia Oriental
(Universidade Federal do Pará, 2010) TEIXEIRA, Venize Assunção; SOUZA, José Ricardo Santos de; http://lattes.cnpq.br/2797414407717271; MOTA, Galdino Viana; http://lattes.cnpq.br/8821024246942574
This study analyzed 10 years of spatial and temporal distribution of lightning and precipitation systems and their characteristics such as reflectivity, brightness temperature and height of the precipitation systems sampled by the Lightning Imaging Sensor (LIS), Precipitation Radar (PR) e TRMM Microwave Imager (TMI) sensors onboard of the Tropical Rainfall Measuring Mission (TRMM) satellite. This dataset is organized and stored by the research group of tropical convection of the University of Utah from December 1997 to February 2009. This work also analyzes data from outbreaks of fires detected by INPE in the period 1998 to 2008. It was selected an area bounded between 60°W to 45°W of longitudes and between 10°S to 5°N of latitudes, which was subsequently divided into nine sub-areas for more detailed information. To investigate the possible influence of burning in the number of lightning flashes, we selected eight areas, in which four present the highest number of fire outbreaks and four with the lowest ones. The precipitation systems were classified according to the method of Nesbitt et. al (2000), and obeying the new definition of the data proposed by Liu (2007). The precipitation features used in this work are named ALLPFS, which are all systems with rainfall pixels estimated by the 2A25 algorithm. These systems have the categories of PFS (present information of brightness temperature) and OTHPFS (without information of brightness temperature). Within the PFS, the systems are defined as those without ice signature (NOICE), with ice signature (WICE) and mesoscale convective systems (MCS). The most intense MCSs are defined as IMCS. The results show that the southern regions of Pará, around Belém and Marajo Island were the ones with the highest occurrence of lightning in the Amazon region, with values exceeding 20 to 35 lightning flashes/ km²/year. The NOICE systems were equally frequent in all regions. The categories WICE and MCS are those which contribute most to the production of lightning over these regions. It was also observed that the electrified systems have great contribution to the estimated amount of rainfall over central and southern parts, with percentages above 50% in the area SOUTH. The monthly variation of the lightning occurrence densities in the studied area showed that the highest occurrence of lightning was found over the city of Belem during the months from January to June, peaking in January. The highest occurrences in the southern sector of eastern Amazonia were concentrated in the months of September to December. In the analysis on the interaction between lightning and burning spot areas, it was not possible to verify a consistent correlation between lightning and fires, showing that despite the large number of fires observed on these areas, other factors influence the production of lightning flashes.
Acesso aberto (Open Access)
Estudo comparativo da distribuição espaço-temporal da precipitação na Amazônia Oriental
(Universidade Federal do Pará, 2008) GOMES, Nilzele de Vilhena; MOTA, Galdino Viana; http://lattes.cnpq.br/8821024246942574
This work used precipitation data during January 2000 to September 2007 from the gauge on the micrometeorological tower located in the Ferreira Pena Scientific Station in the Caxiuanã forest. This data was compared with the 3B42 algorithm, an estimate based on microwave to ajust infrared measurements. Additionally, the comparisons were extended do the Eastern Amazonia using five algorithms: The Geostationary Environmental Satellite Precipitation Index (GPI); the 3B42; 3A12 and 3A25 that are based on the sensors of microwave and radar from the Tropical Rainfall Measuring Mission (TRMM) satellite; and the Global Precipitation Climatology Center (GPCC), from January 1998 to December 2007.The comparison between the 3B42 estimates with the gauge showed that the 3B42 algorithm overestimates the precipitation from the gauge for all period. The rainy trimonthly periods were in March-April-May (MAM) and December-January-February (DJF) and the less rainy periods were September-October-November (SON) and June-July-August (JJA), This seasonality of precipitation is caused by the different meteorological systems over the region, especially the Intertropical Convergence Zone (ITCZ) which modulates the rainy season over the region. The seasonal analyses showed that the 3B42 algorithm overestimates (underestimates) the rainfall compared with the gauge in MAM and JJA (DJF and SON); and DJF is the quarter what the estimates of precipitation is closer regarding the gauge measure in micrometeorology tower of Caxiuanã. In the monthly averages, the 3B42 algorithm underestimates the rainfall from October to January and overestimates from Mach to August compared to the gauge. The 3B42 algorithm overestimated (underestimated) the nocturnal (the morning and the afternoon) precipitation compared to the gauge in the six grids around Caxiuanã Reservation. However, both data showed the maximum period of precipitation of the diurnal cycle around 18:00 local time (LT). Also, the analysis of diurnal cycle seasonal average indicate what in DJF to 0900 LT, 1500 LT and 1800 LT have a precipitation estimated for 3B42 algorithm closer to gauge measured punctually in Caxiuanã.. The months of November to February have a major maximum of precipitation in the afternoon in both datasets. In the period from may to July the maximum of precipitation becomes nocturnal and in the early morning, changing the diurnal cycle compared to the other months. The comparisons between the five algorithms over the Eastern Amazonia showed different behaviors among the estimators. The GPI algorithm underestimated the precipitation compared to the other algorithms in the Amapá state and French Guyana; and overestimated in central area of Amazonia. Both estimators from TRMM satellite the 3A12 and 3A25 algorithms, presented less precipitation than the other algorithms. The 3B42 algorithm presented similar pattern of precipitation as that showed by Figueroa e Nobre (1990). However, the GPCC estimator showed less details in the spatial distribution of rainfall in the Northwest of Pará state. The differences between the algorithms here considered might be related to the characteristics of each algorithm and/or the methodology used. The comparison between a locally data from the gauge with the averaged data from satellites might be the explanation for the discrepancies in the seasons or in the diurnal cycle. However, the differences could be due to the differences of the nature of precipitation among the subregions; as systems modulating the diurnal cycle of rainfall over the Eastern Amazonia.