@Article{VendrascoMacRibFreNeg:2020:EvRaDa,
               author = "Vendrasco, {\'E}der Paulo and Machado, Luiz Augusto Toledo and 
                         Ribeiro, B. Z. and Freitas, E. D. and Negri, Renato Galante",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University at Albany} 
                         and {Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)}",
                title = "Cloud-resolving model applied to nowcasting: An evaluation of 
                         radar data assimilation and microphysics parameterization",
              journal = "Weather and Forecasting",
                 year = "2020",
               volume = "35",
               number = "6",
                pages = "2345--2365",
                month = "Dec.",
             abstract = "This research explores the benefits of radar data assimilation for 
                         short-range weather forecasts in southeastern Brazil using the 
                         Weather Research and Forecasting (WRF) Models three-dimensional 
                         variational data assimilation (3DVAR) system. Different data 
                         assimilation options are explored, including the cycling 
                         frequency, the number of outer loops, and the use of null-echo 
                         assimilation. Initially, four microphysics parameterizations are 
                         evaluated (Thompson, Morrison, WSM6, and WDM6). The Thompson 
                         parameterization produces the best results, while the other 
                         parameterizations generally overestimate the precipitation 
                         forecast, especially WDSM6. Additionally, the Thompson scheme 
                         tends to overestimate snow, while the Morrison scheme 
                         overestimates graupel. Regarding the data assimilation options, 
                         the results deteriorate and more spurious convection occurs when 
                         using a higher cycling frequency (i.e., 30 min instead of 60 min). 
                         The use of two outer loops produces worse precipitation forecasts 
                         than the use of one outer loop, and the null-echo assimilation is 
                         shown to be an effective way to suppress spurious convection. 
                         However, in some cases, the null-echo assimilation also removes 
                         convective clouds that are not observed by the radar and/or are 
                         still not producing rain, but have the potential to grow into an 
                         intense convective cloud with heavy rainfall. Finally, a cloud 
                         convective mask was implemented using ancillary satellite data to 
                         prevent null-echo assimilation from removing potential convective 
                         clouds. The mask was demonstrated to be beneficial in some 
                         circumstances, but it needs to be carefully evaluated in more 
                         cases to have a more robust conclusion regarding its use.",
                  doi = "10.1175/WAF-D-20-0017.1",
                  url = "http://dx.doi.org/10.1175/WAF-D-20-0017.1",
                 issn = "0882-8156",
                label = "self-archiving-INPE-MCTIC-GOV-BR",
             language = "en",
           targetfile = "vendrasco_cloud.pdf",
        urlaccessdate = "27 abr. 2024"
}