@Article{LealGuiDalPalKam:2021:CaStUs,
               author = "Leal, Philipe Riskalla and Guimar{\~a}es, Ricardo Jos{\'e} de 
                         Paula Souza e and Dall Cortivo, F{\'a}bio and Palharini, Rayana 
                         Santos de Ara{\'u}jo and Kampel, Milton",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Evandro Chagas (IEC)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "A new approach to detect extreme events: a case study using 
                         remotely-sensed precipitation time-series data",
              journal = "Remote Sensing Applications: Society and Environment",
                 year = "2021",
               volume = "24",
                pages = "e100618",
                month = "Nov.",
             keywords = "Extreme event detection, Precipitation time-series analysis, 
                         Brazilian Amazon region, Climate change.",
             abstract = "Detecting and predicting extreme events are of major importance 
                         for socioeconomic, healthcare and ecological purposes. This study 
                         proposes an alternative model to detect extreme events based on 
                         analyses of probability distribution functionffns s (f((X))), 
                         called Optimum Probability Distribution Function Searcher Model 
                         (Opt.PDF-model). The Opt.PDFmodel involves the optimization of a 
                         fitness function between an histogram and a set of theoretical 
                         f((X)), and the subsequent evaluation of the Probability Point 
                         Function (PPF) of the fittest theoretical (f((X))) to assess 
                         threshold values for the classification of extreme events. Any 
                         occurrence in the dataset with a PPF value equal to or greater 
                         than 90% was considered an extreme event candidate. A 
                         satellite-derived precipitation time-series (Climate Hazards Group 
                         InfraRed Precipitation with Station data) was used to calibrate 
                         and validate the proposed model, with data on accumulated 
                         precipitation from more than 30 years (Jan.1981 to Dec.2018) of 
                         the Brazilian Amazon region. The proposed method was pairwise 
                         cross-validated with two other extreme event models based on Gamma 
                         and Gaussian distributions, as applied by the European Drought 
                         Observatory of the European Environment Agency. Aditionally, all 
                         three extreme event classification models were cross-validated 
                         relative to the El Nino Southern Oscillation (ENSO). By means of 
                         the Opt.PDF-model, it was possible to evidence two positive 
                         temporal trends for the area of study: one for more intense 
                         precipitation events, and another for less intense events. The 
                         pairwise cross-validation analysis returned specific Kappa's 
                         similarity indices, and the highest similarity was observed 
                         between the Gamma and the Opt.PDF models (48% for PPF(97.7%)). 
                         This analysis indicated that extreme event detection models are 
                         highly sensitive to distribution family priors and to threshold 
                         definitions. The proposed approach and the results obtained here 
                         are potentially useful for climate change warnings, and can be 
                         extended to other scientific areas that involve time-series 
                         analyses.",
                  doi = "10.1016/j.rsase.2021.100618",
                  url = "http://dx.doi.org/10.1016/j.rsase.2021.100618",
                 issn = "2352-9385",
             language = "en",
           targetfile = "leal_new.pdf",
        urlaccessdate = "05 maio 2024"
}