Study of Aerosol Optical Depth Climatology Using Modis Remote Sensing Data

Souza, Amaury de; Abreu, Marcel Carvalho; Oliveira‐Júnior, José Francisco de; Santos, Cícero Manoel dos; Pobocikova, Ivana; Fernandes, Widinei Alves; Torsen, Emmanuel; Silva, Elania Barros da; Mbaga, Yohana Vandi

doi:10.17628/ecb.2020.9.291-299

“…Possible factors may be related to the fact that the TRMM product considerably overestimates dry season precipitation. The lower precipitation values in this period associated with increased fires and, consequently, increased aerosol emissions to atmospheres may be the cause of the overestimation of precipitation (Souza et al, 2020).…”

Section: Resultsmentioning

confidence: 91%

“…The largest source of aerosols to the atmosphere in SA are emissions from forest fires and savannahs, which occur mainly in the dry season (Souza et al, 2020) evaluated the optical thickness of aerosols in the atmosphere and found that the highest emissions occur in the dry season due to increased fires and a long period of drought.…”

Section: Resultsmentioning

confidence: 99%

“…They also state that the hydrological cycle may be changing due to the emission of large amounts of particles that act as cloud condensation nuclei, and cloud micro-physics properties are being altered. Possibly, these changes in cloud micro-physics may be altering the pattern of precipitation in the Amazon and Pantanal region, leading to the occurrence of high clouds, and the suppression of the formation of shallow clouds (Oliveira et al, 1986;Souza et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…The methodology used to calculate the Rainfall Anomaly Index (RAI) was the same adopted by Oliveira et al (2020) for the State of Mato Grosso do Sul. Figures 6a and 6b show the annual and historical series mean and trends and values of the RAI for CMS and Figures 6c and 6d show the annual and historical series mean and trends and RAI values for stations from TRMM satellite data for the years 1998 to 2019.…”

Section: Analysis Of Trends and Anomaly Patterns For Precipitation Le...mentioning

confidence: 99%

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2022

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Remote sensing can assist in the acquisition of scarce surface data. The analyzes for validation of the precipitation product estimated by the TRMM satellite (Tropical Rainfall Measuring Mission) were carried out with the precipitation data observed on the surface during the period from 1998 to 2019. For this purpose, precipitation data from the Pantanal biome meteorological stations were used, located between the 16 and 22°S parallels and the 55 and 58°W meridians, and compared with the data from the TRMM 3B42 V7 product algorithms. Statistical analysis was performed based on the correlation coefficient, root mean square error (RMSE), and relative bias (BIAS) between the monthly precipitation data observed on the surface and the estimated precipitation data. The results found for product 3B43 V7 indicated that the precipitation estimates were representative when compared to the surface observations. However, when compared for the rainy and dry periods, there was underestimation and overestimation, respectively, of the product. The product 3B42 V7 satisfactorily represents the precipitation that occurs on the surface.

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“…Climate and land-use changes (mainly deforestation), which synergistically increase the frequency and intensity of drought-related fires in tropical regions, lead to the dominance of grass at forest edges, which is savannization. An additional factor is ozone (O 3 ), produced by the activity of the sun in atmospheric pollution, which, being phytotoxic, is well-known for its damaging effect on vegetation (Cirino et al, 2013 ; Souza et al 2020a , b ). Bioaerosols (enzymes, viruses or debris) also play a significant role in air pollution because they can be pathogenic.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the air temperature using the Extreme Value Theory for selected biomes in Mato Grosso do Sul (Brazil)

Reis

¹

,

Souza

²

,

Graf

³

et al. 2022

Stoch Environ Res Risk Assess

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This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007–2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov–Smirnov test, the corrected Akaike criterion AIC c , the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R 2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28–32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes. Supplementary Information The online version contains supplementary material available at 10.1007/s00477-022-02206-1.

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Evaluation of TRMM 3b43v7 Satellite Precipitation in the Pantanal of Mato Grosso Do Sul in the Years 1998 to 2019

Souza

¹

,

Almeida²,

Abreu³

et al. 2022

RM

1

0

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Remote sensing can assist in the acquisition of scarce surface data. The analyzes for validation of the precipitation product estimated by the TRMM satellite (Tropical Rainfall Measuring Mission) were carried out with the precipitation data observed on the surface during the period from 1998 to 2019. For this purpose, precipitation data from the Pantanal biome meteorological stations were used, located between the 16 and 22°S parallels and the 55 and 58°W meridians, and compared with the data from the TRMM 3B42 V7 product algorithms. Statistical analysis was performed based on the correlation coefficient, root mean square error (RMSE), and relative bias (BIAS) between the monthly precipitation data observed on the surface and the estimated precipitation data. The results found for product 3B43 V7 indicated that the precipitation estimates were representative when compared to the surface observations. However, when compared for the rainy and dry periods, there was underestimation and overestimation, respectively, of the product. The product 3B42 V7 satisfactorily represents the precipitation that occurs on the surface. Keywords: Tropical Rainfall, Remote Sensing, Conventional Meteorological Observation Precipitation Estimate.

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Study of Aerosol Optical Depth Climatology Using Modis Remote Sensing Data

Cited by 3 publications

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Untitled

Untitled

Modeling of the air temperature using the Extreme Value Theory for selected biomes in Mato Grosso do Sul (Brazil)

Evaluation of TRMM 3b43v7 Satellite Precipitation in the Pantanal of Mato Grosso Do Sul in the Years 1998 to 2019

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