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@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 = "24 jan. 2021" }

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