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@PhDThesis{JucŠOliveira:2017:ChErMo,
               author = "Juc{\'a} Oliveira, R{\^o}mulo Augusto",
                title = "Characteristics and error modeling of GPM satellite rainfall 
                         estimates during CHUVA campaign in Brazil",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2017",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2017-03-24",
             keywords = "estimativa de precipita{\c{c}}{\~a}o por sat{\'e}lite, 
                         modelagem de erro, quantifica{\c{c}}{\~a}o de incertezas, 
                         estimativa de precipita{\c{c}}{\~a}o por radar, 
                         valida{\c{c}}{\~a}o, satellite rainfall estimation, error 
                         modeling, uncertainty quantification, radar rainfall estimation, 
                         validation.",
             abstract = "Studies that investigate and evaluate the quality, limitations and 
                         uncertainties of satellite rainfall estimates are fundamental to 
                         assure the correct and successful use of these products in 
                         applications, such as climate studies, hydrological modeling and 
                         natural hazard monitoring. Over regions of the globe that lack in 
                         situ observations, such studies are only possible through 
                         intensive field measurement campaigns, which provide a range of 
                         high quality ground measurements, e.g., CHUVA (Cloud processes of 
                         tHe main precipitation systems in Brazil: A contribUtion to cloud 
                         resolVing modeling and to the GlobAl Precipitation Measurement) 
                         and GoAmazon (Observations and Modeling of the Green Ocean Amazon) 
                         over the Brazilian Amazon during 2014/2015. This study aims to 
                         assess the uncertainty of the Global Precipitation Measurement 
                         (GPM) satellite constellation in representing the main 
                         characteristics of precipitation over different regions of Brazil. 
                         The Integrated Multi-satellitE Retrievals for GPM (IMERG) 
                         (level-3) and the Goddard Profiling Algorithm (GPROF) (level-2) 
                         algorithms are evaluated against ground-based radar observations, 
                         specifically, the S-band weather radar from the Amazon Protection 
                         National System (SIPAM) and the X-band dual polarization weather 
                         radar (X-band CHUVA radar) as references. The space-based rainfall 
                         estimates, based on active microwave (e.g., TRMM-PR and GPM-DPR 
                         [at Ku-band] radars) are also used as references. The results for 
                         the CHUVA-Vale campaign suggest that GPROF has relatively good 
                         agreement (spatial distribution and accumulated rainfall), 
                         especially for convective rain cases, due the significant presence 
                         of ice scattering. However, the intensity and volume of 
                         light/moderate rains is overestimated and performance related to 
                         light/heavy rains (underestimated) are intrinsically linked to 
                         convectivestratiform rainfall occurrences over the study region. 
                         For the study over the Central Amazon Region (CHUVA-GoAmazon), 
                         results showed that during the wet season, IMERG, which uses the 
                         GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) 
                         sensor, significantly overestimates the frequency of heavy 
                         rainfall volumes at around 00:0004:00 UTC and 15:0018:00 UTC. This 
                         overestimation is particularly evident over the Negro, 
                         Solim{\~o}es and Amazon rivers due to the poorlycalibrated 
                         algorithm over water surfaces. On the other hand, during the dry 
                         season, the IMERG product underestimates mean precipitation in 
                         comparison to the S-band SIPAM radar, mainly due to the fact that 
                         isolated convective rain cells in the afternoon are not detected 
                         by the satellite precipitation algorithm. The study based on 
                         verification of GPM level 2 by traditional and object-based 
                         analysis shows that volume and occurrence of heavy rainfall are 
                         underestimated, a good agreement of GPROF2014 for TMI and GMI 
                         versus TRMM PR and GPM DPR (Ku band) rainfall retrievals, 
                         respectively, was noted. Such most evident good performances were 
                         found through continuous and categorical analyses, especially 
                         during the wet season, where the number of objects and larger 
                         areas were observed. The larger object area seen by GPROF2014(GMI) 
                         compared to DPR (Ku band) was directly linked to the structure of 
                         vertical profiles of the precipitanting systems and the presence 
                         of bright band was the main source of uncertainty on the 
                         estimation of precipitation area and intensity. The results via 
                         error modeling, through the Precipitation Uncertainties for 
                         Satellite Hydrology (PUSH) framework, demonstrated that the PUSH 
                         model was suitable for characterizing the error from the IMERG 
                         algorithm when applied to S-band SIPAM radar estimates. PUSH could 
                         efficiently predict the error distribution in terms of spatial and 
                         intensity distributions. However, an underestimation 
                         (overestimation) of light satellite rain rates was observed during 
                         the dry (wet) period, mainly over the river. Although the 
                         estimated error showed a lower standard deviation than the 
                         observed error, they exhibited good correlations to other, 
                         especially in capturing the systematic error along the Negro, 
                         Solim{\~o}es and Amazon rivers, especially during the wet season. 
                         RESUMO: Estudos que investigam e avaliam a qualidade, 
                         limita{\c{c}}{\~o}es e incertezas das estimativas de 
                         precipita{\c{c}}{\~a}o de sat{\'e}lites s{\~a}o fundamentais 
                         para assegurar o uso correto e bem-sucedido desses produtos em 
                         aplica{\c{c}}{\~o}es, como estudos clim{\'a}ticos, modelagem 
                         hidrol{\'o}gica e monitoramento de desastres naturais. Em 
                         regi{\~o}es do globo que n{\~a}o possuem observa{\c{c}}{\~o}es 
                         in situ, esses estudos apenas s{\~a}o poss{\'{\i}}veis 
                         atrav{\'e}s de campanhas intensivas de medi{\c{c}}{\~a}o de 
                         campo, que oferecem uma gama de medi{\c{c}}{\~o}es de 
                         superf{\'{\i}}cie de alta qualidade, por exemplo, CHUVA 
                         (Cloudprocesses of tHe main precipitation systems in Brazil: A 
                         contribUtion to cloud re-solVing modeling and to the GlobAl 
                         Precipitation Measurement) e GoAmazon (Observations and Modeling 
                         of the Green Ocean Amazon) sobre a Amaz{\^o}nia Brasileira 
                         durante 2014/2015. Este estudo tem como objetivo avaliar as 
                         incertezas provenientes da constela{\c{c}}{\~a}o de 
                         sat{\'e}lites do Global Precipitation Measurement (GPM) em 
                         representar as principais caracter{\'{\i}}sticas da 
                         precipita{\c{c}}{\~a}o em diferentes regi{\~o}es do Brasil. Os 
                         algoritmos Integrated Multi-satellitE Retrievals for GPM (IMERG) 
                         (level-3) e Goddard Profiling Algorithm (GPROF) (level-2) s{\~a}o 
                         avaliados em contraste as observa{\c{c}}{\~o}es de radares 
                         meteorol{\'o}gicos, especificamente, do Sistema Nacional de 
                         Prote{\c{c}}{\~a}o da Amaz{\^o}nia (SIPAM) e o radar 
                         meteorol{\'o}gico banda X de dupla polariza{\c{c}}{\~a}o 
                         (X-band CHUVA radar) como refer{\^e}ncia. As estimativas de 
                         precipita{\c{c}}{\~a}o, baseadas em radares de microondas ativos 
                         (por exemplo, radares TRMM-PR e GPM-DPR [na banda Ku]) tamb{\'e}m 
                         s{\~a}o utilizadas como refer{\^e}ncia. Os resultados da 
                         campanha CHUVA-Vale sugerem que o GPROF possui uma boa 
                         concord{\^a}ncia (distribui{\c{c}}{\~a}o espacial e 
                         precipita{\c{c}}{\~a}o acumulada), especialmente para casos de 
                         chuva convectiva, devido {\`a} presen{\c{c}}a significativa de 
                         espalhamento por gelo. No entanto, a intensidade e volume de 
                         chuvas leves/moderadas {\'e} superestimada e um desempenho 
                         (subestimado) relacionado {\`a}s chuvas fracas/intensas 
                         diretamente ligado {\`a}s ocorr{\^e}ncias de chuvas 
                         convectivasestratiformes na regi{\~a}o do estudo. Para o estudo 
                         da regi{\~a}o da Amaz{\^o}nia Central (CHUVA-GoAmazon), os 
                         resultados mostraram que, durante a esta{\c{c}}{\~a}o chuvosa, o 
                         IMERG, que utiliza as estimativas de precipita{\c{c}}{\~a}o do 
                         GPROF2014 a partir do sensor GPM Microwave Imager (GMI), 
                         superestima significativamente a freq{\"u}{\^e}ncia de chuvas 
                         intensas em torno de 00:00-04:00 UTC e 15:00-18:00 UTC. Essa 
                         superestimativa {\'e} particularmente evidente nos rios Negro, 
                         Solim{\~o}es e Amazonas devido ao algoritmo apresentasse 
                         erroneamente calibrado sobre as superf{\'{\i}}cies de {\'a}gua. 
                         Por outro lado, durante a esta{\c{c}}{\~a}o seca, o produto 
                         IMERG subestima a precipita{\c{c}}{\~a}o m{\'e}dia em 
                         compara{\c{c}}{\~a}o com o radar banda-s do SIPAM, 
                         principalmente devido ao fato de que c{\'e}lulas convectivas 
                         isoladas {\`a} tarde n{\~a}o s{\~a}o detectadas por tal 
                         algoritmo. O estudo baseado na verifica{\c{c}}{\~a}o das 
                         estimativas do GPM Level 2 por abordagens tradicional e baseada em 
                         objeto mostra que, embora a subestimiativa do volume e 
                         ocorr{\^e}ncia de chuvas intensas, foi observada uma boa 
                         concord{\^a}ncia do GPROF2014 (TMI e GMI) versus TRMM PR e GPM 
                         DPR (Ku band), Respectivamente. Tais evidentes melhores 
                         desempenhos foram encontrados atrav{\'e}s de an{\'a}lises 
                         cont{\'{\i}}nua e categ{\'o}rica, especialmente durante a 
                         esta{\c{c}}{\~a}o chuvosa, onde o maior n{\'u}mero e maiores 
                         {\'a}reas de objetos foram observados. As maiores {\'a}reas, 
                         observadas pelo GPROF2014 (GMI) comparada ao DPR (banda Ku) esteve 
                         diretamente ligada {\`a} estrutura de perfis verticais dos 
                         sistemas de precipitantes e a presen{\c{c}}a de banda brilhante 
                         foi a principal fonte de incerteza na estimativa da {\'a}rea e 
                         intensidade de precipita{\c{c}}{\~a}o. Os resultados referentes 
                         {\`a} modelagem do erro, atrav{\'e}s da ferramenta Precipitation 
                         Uncertainties for Satellite Hydrology (PUSH), as an{\'a}lises 
                         demonstraram que o modelo PUSH foi adequado para caracterizar o 
                         erro do algoritmo IMERG quando aplicado {\`a}s estimativas de 
                         radar banda S do SIPAM. O modelo PUSH p{\^o}de prever 
                         eficientemente a distribui{\c{c}}{\~a}o de erro em termos 
                         espaciais e de intensidade. No entanto, observou-se uma 
                         subestimativa (superestimativa) das taxas de chuva fracas do 
                         sat{\'e}lite durante o per{\'{\i}}odo seco (chuvoso), 
                         especialmente ao longo do rio. Embora o erro estimado tenha 
                         apresentado menor desvio padr{\~a}o do que o erro observado, eles 
                         apresentaram boas correla{\c{c}}{\~o}es entre si, especialmente 
                         na captura do erro sistem{\'a}tico ao longo dos rios Negro, 
                         Solim{\~o}es e Amazonas, especialmente durante a 
                         esta{\c{c}}{\~a}o chuvosa.",
            committee = "Herdies, Dirceu Luis (presidente) and Vila, Daniel Alejandro 
                         (orientador) and Sapucci, Luiz Fernando and Maggioni, Viviana and 
                         Rodriguez, Carlos Augusto Morales",
         englishtitle = "Caracter{\'{\i}}sticas e modelagem de erro nas estimativas de 
                         precipita{\c{c}}{\~a}o do sat{\'e}lite gpm durante as campanhas 
                         do chuva no brasil",
             language = "en",
                pages = "161",
                  ibi = "8JMKD3MGP3W34P/3NU3598",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34P/3NU3598",
           targetfile = "publicacao.pdf",
        urlaccessdate = "03 dez. 2020"
}


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