@InProceedings{CintraCock:2014:LoEnTr,
               author = "Cintra, Rosangela Saher Correa and Cocke, Steven",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Florida 
                         State University}",
                title = "A Local Ensemble Transform Kalman Filter Data Assimilation System 
                         for the FSU Global Atmospheric Model",
            booktitle = "Anais...",
                 year = "2014",
         organization = "Uncertainties 2014.",
             keywords = "Data assimilation, Kalman filter, numerical weather prediction, 
                         global atmospheric model.",
             abstract = "Projections of future climate or weather are produced using 
                         complex atmospheric general circulation models (AGCMs). Due to the 
                         inherent uncertainty of our knowledge of the weather/climate 
                         system it is inevitable that there exists model errors. Data 
                         assimilation is the process by which measurements and model 
                         predictions are combined to obtain an accurate representation of 
                         the state of the modeled system. Data assimilation is recognized 
                         as essential in weather prediction and climate analysis. All data 
                         assimilation systems require reasonable estimates of the initial 
                         condition (analysis) to run AGCMs considering the errors of the 
                         model, the observations and the analysis. In this work, a data 
                         assimilation system, the local ensemble transform Kalman filter 
                         (LETKF) was implemented. By local we mean that the analysis can be 
                         carried out independently at each grid point with the use of only 
                         local observations. Uncertainty is represented not by a covariance 
                         matrix, but by an ensemble of estimates in state space. The 
                         ensemble is evolved in time through the full model, which 
                         eliminates any need for a linear hypothesis as to the temporal 
                         evolution. The LETKF assimilation scheme was tested with Florida 
                         State University Global Spectral Model (FSUGSM). The model is a 
                         multilevel (27 vertical levels) spectral primitive equation model 
                         with a vertical \σ-coordinate. All variables are expanded 
                         horizontally in a truncated series of spherical harmonic functions 
                         (at resolution T63) and a transform technique is applied to 
                         calculate the physical processes in real space. The LETKF data 
                         assimilation uses the synthetic conventional observations and 
                         satellite data (surface pressure, absolute temperature, zonal 
                         component wind, meridional component wind and humidity). The 
                         observations are localized at every other grid point of the model. 
                         The ensemble forecast size is 20 members, which run parallel (one 
                         single model member per computer node simultaneously) and the 
                         assimilation scheme is parallelized via MPI. The numerical 
                         experiment has a one-month assimilation cycle, for the period 
                         01/01/2001 to 31/01/2001 at (00, 06, 12 and 18 GMT) for each day. 
                         An important source of information for the evaluation of the 
                         quality of any data assimilation is the observation-minus-forecast 
                         (OMF) and the observationminus- analysis (OMA) statistics. The 
                         histogram of OMF and OMA for a range of spatial and temporal 
                         scales is calculated, and the results are consistent. The results 
                         showing the analysis from the assimilation of the observations 
                         will be presented.",
  conference-location = "Rouen, France",
      conference-year = "2014",
                label = "lattes: 8185155301349092 1 CintraCock:2014:LoEnTr",
             language = "pt",
           targetfile = "Cintra_local.pdf",
                  url = "http://uncertainties2014.insa-rouen.fr/",
        urlaccessdate = "26 abr. 2024"
}