@Book{FrançaAlbuCamp:2023:BrInIm,
               author = "Fran{\c{c}}a, Gutemberg Borges and Albuquerque Neto, Francisco L. 
                         de and Campos Velho, Haroldo Fraga de",
                title = "Nowcasting using Machine Learning and Deterministic Models: A 
                         Brazilian Initiative to improve aviation meteorology",
            publisher = "EDUNIFA",
                 year = "2023",
              address = "Rio de Janeiro (RJ)",
             keywords = "Aviation meteorology, Nowcasting, Machine learning, Mesoscale 
                         meteorological model.",
             abstract = "The present book is a compilation of recent research dedicated to 
                         the applications of prediction models for weather nowcasting 
                         linked to aeronautical meteorology. Models embrace differential 
                         equations for atmospheric dynamics, as well as data-driven 
                         approaches. Convective weather, wind, clear air turbulence, 
                         visibility, and ceiling are the significant phenomena affecting 
                         aviation events investigated by the C{\'a}tedra project of 
                         aeronautical meteorology. The project is a joint effort between 
                         the graduate meteorology program from the Federal University of 
                         Rio de Janeiro (UFRJ), the Department of Airspace Control (DECEA) 
                         and the Air Force University (UNIFA). The book focuses on aviation 
                         operational meteorology and deals with numerical weather forecast 
                         simulation results obtained by deterministic and hybrid models. 
                         The latter is based on the composition of deterministic modeling 
                         and computational intelligence techniques. The studies presented 
                         in this publication make use of data from remote sensing sensors, 
                         such as satellite, radiometer, ceilometer, and sodar, as well as 
                         information from insitu observations for monitoring and developing 
                         short-term forecast models. These aim to predict convective 
                         weather, surface wind shifts, wind gusts, clear air turbulence, 
                         low visibility due to fog, and low ceilings. All these are 
                         important for landing and takeoff procedures, as well as for 
                         scheduling flights and increasing safety on Brazilian air routes. 
                         This volume provides a comprehensive overview of research results, 
                         including comments on the currently existing knowledge, and the 
                         numerous remaining difficulties in predicting and measuring issues 
                         related to aforementioned meteorological events at different time 
                         and space scales. It will be helpful to academics with an interest 
                         in operational meteorology and aviation as well as weather 
                         offices, pilots, meteorologists, aviation experts, scientists, 
                         college students, postgraduates, and others. Most of the chapters 
                         are produced by C{\'a}tedra project´s researchers and published 
                         in scientific journals.",
          affiliation = "{Universidade Federal do Rio de Janeiro (UFRJ)} and {Universidade 
                         Federal do Rio de Janeiro (UFRJ)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                 isbn = "9786589535096",
                label = "lattes: 5142426481528206 3 Fran{\c{c}}aAlbuCamp:2023:BrInIm",
             language = "en",
                pages = "282",
           targetfile = "FRANcA, G.B.pdf",
        urlaccessdate = "13 maio 2024"
}