@Article{AnochiAlmeCamp:2021:MaLeCl,
               author = "Anochi, Juliana Aparecida and Almeida, Vin{\'{\i}}cius 
                         Albuquerque de and Campos Velho, Haroldo Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal do Rio de Janeiro (UFRJ)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Machine Learning for Climate Precipitation Prediction Modeling 
                         over South America",
              journal = "Remote Sensing",
                 year = "2021",
               volume = "13",
               number = "13",
                pages = "e2468",
                month = "July",
             keywords = "machine learningclimate precipitation predictionneural 
                         networksoptimal neural architecturedeep learning.",
             abstract = "Many natural disasters in South America are linked to 
                         meteorological phenomena. Therefore, forecasting and monitoring 
                         climatic events are fundamental issues for society and various 
                         sectors of the economy. In the last decades, machine learning 
                         models have been developed to tackle different issues in society, 
                         but there is still a gap in applications to applied physics. Here, 
                         different machine learning models are evaluated for precipitation 
                         prediction over South America. Currently, numerical weather 
                         prediction models are unable to precisely reproduce the 
                         precipitation patterns in South America due to many factors such 
                         as the lack of region-specific parametrizations and data 
                         availability. The results are compared to the general circulation 
                         atmospheric model currently used operationally in the National 
                         Institute for Space Research (INPE: Instituto Nacional de 
                         Pesquisas Espaciais), Brazil. Machine learning models are able to 
                         produce predictions with errors under 2 mm in most of the 
                         continent in comparison to satellite-observed precipitation 
                         patterns for different climate seasons, and also outperform INPE's 
                         model for some regions (e.g., reduction of errors from 8 to 2 mm 
                         in central South America in winter). Another advantage is the 
                         computational performance from machine learning models, running 
                         faster with much lower computer resources than models based on 
                         differential equations currently used in operational centers. 
                         Therefore, it is important to consider machine learning models for 
                         precipitation forecasts in operational centers as a way to improve 
                         forecast quality and to reduce computation costs.",
                  doi = "10.3390/rs13132468",
                  url = "http://dx.doi.org/10.3390/rs13132468",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-13-02468.pdf",
        urlaccessdate = "06 maio 2024"
}