author = "Casella, Daniele and Amaral, Lia Martins Costa do and Dietrich, 
                         Stefano and Marra, Anna Cinzia and Sano, Paolo and Panegrossi, 
          affiliation = "{Institute of Atmospheric Sciences and Climate} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {National Research 
                         Council} and {National Research Council} and {National Research 
                         Council} and {National Research Council}",
                title = "The cloud dynamics and radiation database algorithm for AMSR2: 
                         exploitation of the GPM observational dataset for operational 
              journal = "IEEE Journal of Selected Topics in Applied Earth Observations and 
                         Remote Sensing",
                 year = "2017",
               volume = "10",
               number = "9",
                pages = "3985--4001",
                month = "Sept.",
             keywords = "Advanced Microwave Scanning Radiometer 2 (AMSR2), Bayesian 
                         retrieval algorithm, Cloud Dynamics and Radiation Database (CDRD), 
                         Global Precipitation Measurement (GPM) mission, passive microwave 
                         (PMW) radiometer, satellite precipitation.",
             abstract = "A new precipitation retrieval algorithm for the AMSR2 is 
                         described. The algorithm is based on the cloud dynamics and 
                         radiation database (CDRD) Bayesian approach and represents an 
                         evolution of the previous version applied to SSMIS observations, 
                         and used operationally within the EUMETSAT H-SAF program. This new 
                         product presents as main innovation the use of a very large 
                         database entirely empirical, derived from coincident radar and 
                         radiometer observations from the NASA/JAXA GPM-CO launched on 
                         February 28, 2014. The other new aspects are: 1) a new 
                         rain-/no-rain screening approach; 2) use of EOF and CCA for 
                         dimensionality reduction; 3) use of new ancillary variables to 
                         categorize the database and mitigate the problem of non-uniqueness 
                         of the retrieval solution; and 4) development and implementations 
                         of modules for computation time minimization. A verification study 
                         for case studies over Italy and for coincident AMSR2/GPM-CO 
                         observations over the MSG full disk area has been carried out. 
                         Results show remarkable AMSR2 capabilities for RR retrieval over 
                         ocean (for RR > 0.1 mm/h), good capabilities over vegetated land 
                         (for RR > 1 mm/h), while for coastal areas the results are less 
                         certain. Comparisons with NASA GPM products, and with ground-based 
                         radar data, show that the new CDRD for AMSR2 is able to depict 
                         very well the areas of high precipitation over all surface types. 
                         The algorithm is also able to handle an extremely large 
                         observational database available from GPM-CO and to provide 
                         rainfall estimate with minimum latency, making it suitable for NRT 
                         hydrological and operational applications.",
                  doi = "10.1109/JSTARS.2017.2713485",
                  url = "http://dx.doi.org/10.1109/JSTARS.2017.2713485",
                 issn = "1939-1404 and 2151-1535",
             language = "en",
           targetfile = "casella_cloud.pdf",
        urlaccessdate = "03 dez. 2020"