TRMM Satellite Performance in Estimated Rainfall Over the Midwest Region of Brazil

Danelichen, Victor Hugo de Morais; Machado, Nadja Gomes; Biudes, Marcelo Sacardi; Souza, Maísa Caldas

doi:10.5380/abclima.v12i1.31203

Cited by 8 publications

(13 citation statements)

References 18 publications

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“…Vale salientar que, no presente trabalho, não foram realizados testes estatísticos de avaliação dos produtos do TRMM com base em dados de superfície, uma vez que diversos autores averiguaram o desempenho das estimativas do TRMM e encontraram alta correlação com resultados obtidos em estações meteorológicas (Camparotto et al, 2013;Danelichen et al, 2013;Curtarelli et al, 2013;Pereira et al 2013…”

Section: Procedimentos Metodológicosunclassified

Aplicação das estimativas de precipitação do Tropical Rainfall Measuring Mission (TRMM) no estudo da pluviometria habitual e excepcional de Goiás e Distrito Federal (Application of rainfall estimates of the Tropical Rainfall Measuring Mission (TRMM) in the study of the usual and exceptional rainfall of Goiás and Federal District)

Nascimento

Oliveira

2018

Rev. Bras. Geog. Fis.

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RESUMO Desde as primeiras imagens de nuvens registradas a partir de abril de 1960 pelo satélite meteorológico TIROS I, diversos parâmetros climáticos têm sido registrados e mensurados por sensoriamento remoto, permitindo o avanço para além da simples observação dos elementos atmosféricos, a ponto de subsidiar complexas análises climáticas. É nesse contexto que se destacam as técnicas de estimativas de precipitação, sob o aporte de satélites, sensores e metodologias distintas. Com o intuito de averiguar a potencialidade de emprego de imagens de sensoriamento remoto na climatologia, o presente estudo objetivou aplicar as estimativas satelitárias de precipitação realizadas pelo Tropical Rainfall Measuring Mission (TRMM) no estudo do regime habitual e excepcional das chuvas no estado de Goiás e Distrito Federal, tendo como base os anos-padrão habitual (2011), seco (2007) e chuvoso (2009). Foram utilizadas as estimativas mensais do TRMM (produto 3B43) para análise da variação espacial e distribuição temporal da precipitação anual, sazonal e mensal entre os três anos supracitados, com o intuito de identificar os limiares dos totais anuais e sazonais e do regime mensal das chuvas na condição habitual e na excepcional (regime seco e chuvoso). Pelos dados apresentados, foi possível constatar distinto padrão das chuvas entre os regimes habitual e excepcional das precipitações no estado de Goiás e no Distrito Federal. Também se verificou que a condição de ano seco, habitual e chuvoso não se refere essencialmente à altura pluviométrica anual, mas, sobretudo, à distribuição das chuvas no decorrer dos meses e à delimitação dos períodos chuvoso e seco. Palavras-chave: Chuvas, estimativas, precipitação, TRMM, anos-padrão. Application of rainfall estimates of the Tropical Rainfall Measuring Mission (TRMM) in the study of the usual and exceptional rainfall of Goiás and Federal District ABSTRACT Since the first images of clouds recorded in April 1960 by the TIROS I meteorological satellite, several climatic parameters have been registered and measured by remote sensing, allowing the advancement beyond the simple observation of the atmospheric elements, to the point of subsidizing complex climatic analyzes. It is in this context that the techniques of precipitation estimates are highlighted, with the aid of satellites, sensors and different methodologies. The present study aimed to apply the data generated by the Tropical Rainfall Measuring Mission (TRMM) in the study of the usual and exceptional regime of rainfall in the state of Goiás and the Federal District, in Brazil, in order to investigate the potential of using remote sensing images in climatology, based on the standard years considered as usual (2011), dry (2007) and rainy (2009). The monthly estimates of the TRMM (product 3B43) were used to analyze the spatial variation and temporal distribution of the annual, seasonal and monthly precipitation, between the three years above, in order to identify the annual and seasonal totals thresholds and the monthly rainfall in the usu...

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Section: Procedimentos Metodológicosunclassified

Aplicação das estimativas de precipitação do Tropical Rainfall Measuring Mission (TRMM) no estudo da pluviometria habitual e excepcional de Goiás e Distrito Federal (Application of rainfall estimates of the Tropical Rainfall Measuring Mission (TRMM) in the study of the usual and exceptional rainfall of Goiás and Federal District)

Nascimento

Oliveira

2018

Rev. Bras. Geog. Fis.

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“…A state with a flat landscape, alternating plateaus and plain areas, which presents three different ecosystems: Amazon forest, Cerrado (Brazilian savanna) and the Pantanal (wetland) (COSTA et al, 2010). According to Köppen, the regional climate is Aw (SOUZA et al, 2013), with a dry season from May to September and a wet season from October to April (DANELICHEN et al, 2013). The annual temperature average ranges from 23°C to 26.8°C and the annual rainfall average ranges from 1,200 to 2,000 mm (SOUZA et al, 2013).…”

Section: Study Areamentioning

confidence: 99%

“…According to Köppen, the regional climate is Aw (SOUZA et al, 2013), with a dry season from May to September and a wet season from October to April (DANELICHEN et al, 2013). The annual temperature average ranges from 23°C to 26.8°C and the annual rainfall average ranges from 1,200 to 2,000 mm (SOUZA et al, 2013). Mato Grosso contributes to form three basins: Paraguay (176,800.60 km 2 ), Amazon (592,382.54 km 2 ) and Tocantins (132,237.56 km 2 ) (MATO GROSSO, 1995).…”

Section: Study Areamentioning

confidence: 99%

“…We used data from 3B43 V6 products. We chose TRMM satellite estimates because it has estimated appropriately the annual accumulated rainfall in the Central-West Region of Brazil (DANELICHEN et al, 2013).…”

Section: Meteorological Datamentioning

confidence: 99%

“…17 -JUL/DEZ 2015 43 dynamics for many purposes such as: assessing the spatiotemporal dynamics of two subregions of the Pantanal (ADAMI et al, 2008); studying daily variability of rainfall in the Amazon basin (SILVA et al, 2011); identifying warm season in urban regions of some cities in USA (SHEPHERD et al, 2002); and investigate flooding causes in a city in south Brazil (COLLISCHONN, 2010). Rainfall estimated by TRMM was validated over the Ethiopian highlands (DINKU et al, 2011) and the Central-West Region of Brazil (DANELICHEN et al, 2013). TRMM precipitation dataset is now considered by the Earth science community as the most accurate rain observations from space (YANG and NESBITT, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Rainfall by Neural Network Over a Neotropical Region (Estimativa De Chuvas Pela Rede Neural Sobre a Região Neotropical)

Machado¹,

Ventura²,

Danelichen³

et al. 2015

ABClima

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Rainfall is the key element in regional water balance, and has direct influence over economic activity. Quantifying rainfall at spatial and temporal scales in regions where meteorological stations are scarce is important for agriculture, natural resource management and land-atmosphere interactions science. Thus, we evaluated neural network performance for rainfall estimates over Mato Grosso State located in the Brazilian Midwest region. A dataset of 12 meteorological stations was used to train the neural network, and then was run to perform estimates, which allowed comparing with TRMM satellite estimates. Rainfall estimates were performed by neural network as a function of latitude and longitude for model 1 (NN1), and latitude, longitude, and altitude for model 2 (NN2). In general, rainfall values were higher overestimated by NN1 in January (18.7%), and September (119.2%) than by NN2 in January (6.8%), and September (0%). While NN2 had rainfall pattern closer to TRMM both in January and in September, NN1 rainfall estimates captured a small amount of the rainfall pattern over Mato Grosso state. There was no higher difference between NN1 and NN2 rainfall estimates in January. On the other hand, rainfall estimates had better performance in NN2 than NN1 in September. Bad rainfall estimates using neural network in Mato Grosso state were due to (i) a short temporal dataset, (ii) few stations with poor spatial variability, (iii) few auxiliary variables to build neural network, which could better capture the rain phenomenon over Neotropical regions. The next step will be to analyze the rainfall and other climatic estimates performed by neural network for the whole year for several years on the Central-West Region of Brazil including other auxiliary variables besides latitude, longitude, and altitude.

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Ground validation of TRMM 3B43 V7 precipitation estimates over Colombia. Part I: Monthly and seasonal timescales

Vallejo-Bernal

Urrea

Bedoya-Soto

et al. 2020

Intl Journal of Climatology

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In this study, we validate precipitation estimates remotely sensed by the Tropical Rainfall Measuring Mission (TRMM) at monthly and seasonal timescales, during the period 1998-2015, by calculating and analyzing diverse error metrics between the 3B43 V7 product and in situ measurements from 1,180 rain gauges over Colombia, of which at least 987 are fully independent of TRMM. We explore the existence of spatiotemporal patterns to assess the performance of 3B43 V7 over the five major natural regions of Colombia: Caribbean, Pacific, Andes, Orinoco and Amazon. The results show that 3B43 V7 product is able to capture the phase of the annual cycle of monthly mean precipitation, but the performance is not good for the amplitude, in particular over the Andes and Pacific regions owing to complex climatic and topographic conditions. In general, 3B43 V7 exhibits good performance in the low-lying and plain Amazon, Orinoco and Caribbean regions. Over the Andes region, characterized by complex topography, overestimation errors are identified [root mean squared error (RMSE) ≥83.59 mm•month −1 and relative bias (BIAS) ≥4.69%], whereas the extremely wet rainfall regime of the Pacific region is largely underestimated (RMSE ≥253.52 mm •month −1 and BIAS ≤−11.75%). These errors are greater during the wet seasons when the metrics reach worse scores than those reported in similar studies worldwide. Occurrence analyses showed that 3B43 V7 misses very frequent light rainfall events and less frequent but very heavy storms, which contribute to the overall underestimation (overestimation) observed over the Pacific (Andes) region. The error characteristics identified and quantified in this study confirm the well-documented limitations of remote precipitation sensing and constitute a warning about major challenges that complex climatic and physiographic features can impose on satellite rainfall missions.

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TRMM Satellite Performance in Estimated Rainfall Over the Midwest Region of Brazil

Cited by 8 publications

References 18 publications

Estimation of Rainfall by Neural Network Over a Neotropical Region (Estimativa De Chuvas Pela Rede Neural Sobre a Região Neotropical)

Ground validation of TRMM 3B43 V7 precipitation estimates over Colombia. Part I: Monthly and seasonal timescales

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