Fechar
Metadados

@PhDThesis{Amaral:2019:DePaMi,
               author = "Amaral, Lia Martins Costa do",
                title = "Development of a passive microwave-based satellite precipitation 
                         estimation algorithm for Brazil",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2019",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2019-05-20",
             keywords = "Satellite, precipitation, artificial neural networks, passive 
                         microwave, GPM Microwave Imager (GMI), observational database. 
                         precipita{\c{c}}{\~a}o por sat{\'e}lite, redes neurais 
                         artificais, microondas passivo, GMI, DPR, CMB, banco de dados 
                         observacional.",
             abstract = "In order to develop a passive microwave-based satellite 
                         precipitation estimation algorithm optimized for Brazil, this work 
                         was divided in two parts. The first part consisted in extending 
                         the cloud-radiation database used as a priori information for the 
                         Cloud Dynamics and Radiation Database (CDRD) Bayesian algorithm in 
                         order to include the cloud resolving model simulations 
                         representative of brazilian rainfall regimes. Simulations of 
                         microphysical, dynamical and meteorological profiles were then 
                         generated using the University of Wisconsin - Nonhydrostatic 
                         Modeling System and the brightness temperature (TB) simulations 
                         were generated using the Radiative Transfer Equation Modeling 
                         System for the CHUVA (Amazon and Vale) golden cases and compared 
                         with observed TB. The results demonstrated that the simulations 
                         detected perturbations in the TB fields (in space and time) 
                         however in terms of the range of temperature values, the model did 
                         not reproduce the lowest values of TB that were present in the 
                         observations. The model also seemed to struggle with the riming 
                         process on graupel formation, providing small amounts of graupel 
                         content. These results demonstrated that the models needed 
                         adjustments to be able to describe the regional features of TB 
                         across a wide range of meteorological systems in Brazil. For these 
                         reasons, the second part of the work was developed by making use 
                         of an observational database from the sensors GPM Microwave Imager 
                         and Dualfrequency Precipitation Radar (GMI/DPR-CMB) in order to 
                         develop a screening of precipitation and rainfall retrieval 
                         algorithm over Brazil, based on artificial neural networks (ANN) 
                         and called Neural Network IMplementation of the Brazilian 
                         MUltilayer Perceptron for Screening and precipitation retrieval 
                         (NNIMBUS). The precipitation screening proved to be very effective 
                         in both detecting larger systems and smaller or isolated systems. 
                         Regarding the GMI/DPR-CMB validation dataset, the screening 
                         performed well, with an accuracy of 0.95, POD of 0.80, FAR of 0.39 
                         and bias of 1.34. When compared to the Goddard profiling algorithm 
                         (GPROF) the screening still had good performance, however with 
                         slightly smaller scores. It was observed that through the 
                         comparison maps with GPROF the NNIMBUS can detect agglomerates 
                         very similarly, however it does not detect the borders of the 
                         systems very well. This behavior might be associated with the 
                         precipitation thresholds that were configured with the training 
                         dataset (0.2 a 60 mm/h), which might be leading more stratiform 
                         regions of the systems to go undetected. The rainfall retrieval 
                         model also performed well when compared to the GMI/DPR-CMB 
                         observations, with an MAE of 4.19, standard deviation of 3.23 and 
                         RMSE of 5.59 for the validation dataset. Analyzing the rain rate 
                         classes, the retrieval tends to underestimate classes between 0.2 
                         and 1 mm/h, overestimate classes between 1 and 10 mm/h and 
                         underestimate classes greater than 10 mm/h. These features can be 
                         associated with the input dataset distribution, as well as with 
                         the criteria applied in data cleaning process. RESUMO: Com 
                         objetivo de desenvolver um algoritmo de estimativa de 
                         precipita{\c{c}}{\~a}o por sat{\'e}lite baseado em microondas 
                         passivo otimizado para o Brasil, este trabalho foi dividido em 
                         duas partes. A primeira parte consistiu em estender o banco de 
                         dados de radia{\c{c}}{\~a}o de nuvens usado como 
                         informa{\c{c}}{\~a}o a priori para o algoritmo Bayesiano Cloud 
                         Dynamics and Radiation Database (CDRD), a fim de incluir as 
                         simula{\c{c}}{\~o}es representativas dos regimes de 
                         precipita{\c{c}}{\~a}o do Brazil. Simula{\c{c}}{\~o}es dos 
                         perfis microf{\'{\i}}sicos, din{\^a}micos e meteorol{\'o}gicos 
                         foram geradas usando o University of Wisconsin Nonhydrostatic 
                         Modeling System e as simula{\c{c}}{\~o}es de temperatura de 
                         brilho (TB) foram geradas usando o Radiative Transfer Equation 
                         Modeling System para os sitemas precipitantes observados durante o 
                         projeto CHUVA (campanhas do Vale do Para{\'{\i}}ba e Manaus). Os 
                         resultados demonstraram que as simula{\c{c}}{\~o}es detectaram 
                         as perturba{\c{c}}{\~o}es nos campos de TB (no espa{\c{c}}o e 
                         no tempo), por{\'e}m em termos do intervalos de TB, o modelo 
                         n{\~a}o reproduziu os menores valores de TB presentes nas 
                         observa{\c{c}}{\~o}es. O modelo aparentou ter dificuldade em 
                         gerar o processo de forma{\c{c}}{\~a}o de graupel, gerando 
                         pequenas valores de conte{\'u}do de graupel. Esses resultados 
                         demonstraram que os modelos precisavam de ajustes para poder 
                         descrever as caracter{\'{\i}}sticas regionais da TB para ampla 
                         gama de sistemas meteorol{\'o}gicos no Brasil. Por estas 
                         raz{\~o}es, a segunda parte do trabalho consistiu no 
                         desenvolvimento de um algoritmo de redes neurais artificais 
                         (denominado Neural Network Implementation of the Brazilian 
                         Multilayer Perceptron for Screening and precipitation retrieval 
                         (NNIMBUS), tanto para detec{\c{c}}{\~a}o da {\'a}rea 
                         precipitante screening como para recupera{\c{c}}{\~a}o da 
                         intensidade da precipita{\c{c}}{\~a}o, utilizando um banco de 
                         dados observacionais provindos dos sensores GPM Microwave Imager e 
                         Dual-frequency Precipitation Radar (GMI/DPR-CMB). A 
                         detec{\c{c}}{\~a}o de precipita{\c{c}}{\~a}o (screening) 
                         provou ser muito eficaz na detec{\c{c}}{\~a}o de sistemas 
                         maiores e sistemas menores ou isolados. Em rela{\c{c}}{\~a}o ao 
                         conjunto de dados de valida{\c{c}}{\~a}o do GMI/DPR-CMB, o 
                         algoritmo apresentou bom desempenho, com acur{\'a}cia de 0,95, 
                         POD de 0,80, FAR de 0,39 e vi{\'e}s de 1,34. Quando comparado ao 
                         algoritmo Goddard profiling algorithm (GPROF), a 
                         detec{\c{c}}{\~a}o de precipita{\c{c}}{\~a}o ainda apresentava 
                         bom desempenho, por{\'e}m com estat{\'{\i}}sticas ligeiramente 
                         menores. Atrav{\'e}s dos mapas de compara{\c{c}}{\~a}o com o 
                         GPROF, foi poss{\'{\i}}vel perceber que o NNIMBUS consegue 
                         detectar os aglomerados de forma muito semelhante, por{\'e}m 
                         n{\~a}o detecta muito bem as bordas dos sistemas. Esse 
                         comportamento pode estar associado aos limiares de 
                         precipita{\c{c}}{\~a}o que foram configurados com o conjunto de 
                         dados de treinamento (0,2 a 60 mm/h), o que pode estar levando a 
                         que regi{\~o}es mais estratiformes dos sistemas n{\~a}o sejam 
                         detectadas. O modelo de recupera{\c{c}}{\~a}o da 
                         precipita{\c{c}}{\~a}o tamb{\'e}m teve um bom desempenho quando 
                         comparado com as observa{\c{c}}{\~o}es GMI/DPR-CMB, com um MAE 
                         de 4,19, desvio padr{\~a}o de 3,23 e RMSE de 5,59 para o conjunto 
                         de dados de valida{\c{c}}{\~a}o. Analisando as classes de taxa 
                         de chuva, a recupera{\c{c}}{\~a}o tende a subestimar as classes 
                         entre 0,2 e 1 mm/h, superestimar as classes entre 1 e 10 mm/h e 
                         subestimar as classes acima de 10 mm/h. Essas 
                         caracter{\'{\i}}sticas podem estar associadass {\`a} 
                         distribui{\c{c}}{\~a}o do conjunto de dados de entrada, bem como 
                         aos crit{\'e}rios aplicados no processo de limpeza de dados.",
            committee = "Coelho, Simone Marilene Sievert da Costa (presidente) and Vila, 
                         Daniel Alejandro (orientador) and Machado, Luiz Augusto Toledo and 
                         Panegrossi, Giulia and Mattos, Enrique Vieira",
         englishtitle = "Desenvolvimento de um algoritmo de estimativa de 
                         precipita{\c{c}}{\~a}o baseada em microondas passivo para o 
                         Brasil",
             language = "en",
                pages = "139",
                  ibi = "8JMKD3MGP3W34R/3TADRES",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34R/3TADRES",
           targetfile = "publicacao.pdf",
        urlaccessdate = "28 nov. 2020"
}


Fechar