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@Article{AzevedoGonįKalnWesp:2020:PeMoTe,
               author = "Azevedo, Helena Barbieri de and Gon{\c{c}}alves, Luis Gustavo 
                         Gon{\c{c}}alves de and Kalnay, Eugenia and Wespetal, Matthew",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of 
                         Maryland} and {University of Maryland}",
                title = "Dynamically weighted hybrid gain data assimilation: perfect model 
                         testing",
              journal = "Tellus Series A: Dynamic Meteorology and Ocenography",
                 year = "2020",
               volume = "72",
               number = "1",
                month = "Jan.",
             keywords = "data assimilation, hybrid systems, ensemble Kalman filter, 
                         numerical weather prediction.",
             abstract = "Hybrid systems have become the state of the art among data 
                         assimilation methods. These systems combine the benefits of two 
                         other systems that are traditionally used in operational weather 
                         forecasting: an ensemble-based system and a variational system. 
                         One of the most recently proposed hybrid approaches is called 
                         hybrid gain (HG). It obtains the final analysis as a linear 
                         combination of two analyses, assuming that the innovations (i.e. 
                         the forecast and the set of observations used) between the two 
                         data assimilation methods are identical. A perfect model 
                         experiment was performed using the HG in the SPEEDY model to show 
                         a new methodology to assign different weights to the two analyses, 
                         LETKF and 3D-Var in the generation of the final analysis. Our new 
                         approach uses, in the assignment of the weights, the ensemble 
                         spread, considered to be a measure of uncertainty in the LETKF. 
                         Thus, it is possible to use the estimation of the uncertainty of 
                         the analysis that the LETKF provides, to determine where the 
                         system should give more weight to the LETKF or the 3D-Var 
                         analysis. For this purpose, we define a geographically varying 
                         weighting factor alpha, which multiplies the 3D-Var analysis, as 
                         the normalised spread for each variable at each level. Then, 
                         (1-alpha), which decreases with increasing spread, becomes the 
                         factor that multiplies the LETKF analysis. The underlying 
                         mechanism of the spread-error relationship is explained using a 
                         toy model experiment. The results are very encouraging: the 
                         original HG and the new weighted HG analyses have similar high 
                         quality and are better than both 3D-Var and LETKF. However, the 
                         dynamically weighted HG analyses are significantly more balanced 
                         than the original HG analyses are, which has probably contributed 
                         to the consistently improved performance observed in the weighted 
                         HG, which increases with time throughout the 5-day forecasts.",
                  doi = "10.1080/16000870.2020.1835310",
                  url = "http://dx.doi.org/10.1080/16000870.2020.1835310",
                 issn = "0280-6495",
             language = "en",
           targetfile = "azevedo_dynamically.pdf",
        urlaccessdate = "18 abr. 2021"
}


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