@InProceedings{CintraCamp:2012:SaOb,
               author = "Cintra, Rosangela Saher Correa and Campos Velho, H. F.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Global Temperature Assimilation using Artificial Neural Networks 
                         in SPEEDY Model: Satellite Observation",
            booktitle = "Abstracts...",
                 year = "2012",
         organization = "European Geosciences Union (EGU) General Assembly.",
             abstract = "An Artificial Neural Network (ANN) is designed to investigate a 
                         application for data assimilation. This procedure provides an 
                         appropriated initial condition to the atmosphere to numerical 
                         weather prediction (NWP). The NWP incorporates the equations of 
                         atmospheric dynamics with physical process and it can predict the 
                         future state of the atmosphere. Data assimilation procedure 
                         combines information from observations and from a prior short-term 
                         forecast producing an current state estimate. Operational 
                         satellite data are taken and processed in real-time and 
                         distributed around the world. The use of observations from the 
                         earth-orbiting satellites in operational NWP provides large data 
                         volumes and increases the computational effort. The goal here is 
                         to simulate the process for assimilating temperature data computed 
                         from satellite radiances and introduce new technique in analysis 
                         to Weather Forecasting and climate. This performance can be faster 
                         than conventional schemes for data assimilation. The numerical 
                         experiment is carried out with global model: the Simplified 
                         Parameterizations, primitivE-Equation DYnamics (SPEEDY) and the 
                         synthetic observations of temperatures from model plus a random 
                         noise. For the data assimilation technique was applied a 
                         Multilayer Perceptron (MLP-NN) with supervised training, which 
                         observation, local point observation and the Local Ensemble 
                         Transform Kalman Filter (LETKF) analysis are used as input vector. 
                         The global analysis is done in the activation MLP-NN with only, 
                         synthetic observation and its local point. In this experiment, the 
                         MLP-ANN was trained with the first six months considering the 
                         years 1982, 1983, and 1984 data. A hindcasting experiment for data 
                         assimilation performed a cycle for January of 1985 with MLP-NN and 
                         SPEEDY model. LETKF was performed at the same cycle. The results 
                         for MLP-NN analysis are very close with the results obtained from 
                         LETKF. The simulations show that the major advantage of using ANN 
                         is the better computational performance, with similar quality of 
                         analysis. The CPU-time assimilation with MLP-NN is 80% less than 
                         LETKF with the same observations.",
  conference-location = "Viena",
      conference-year = "22 a 27 de abril de 2012",
             language = "en",
           targetfile = "cintra_global.pdf",
        urlaccessdate = "07 maio 2024"
}