author = "Cintra, Rosangela. S. and Velho, Haroldo F. de Campos",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Global Data Assimilation Using Artificial Neural Networks in 
                         Speedy Model",
            booktitle = "Proceedings...",
                 year = "2012",
                pages = "648--654",
         organization = "International Symposium on Uncertainty Quantification and 
                         Stochastic Modeling, 1.",
             keywords = "data assimilation, artificial neural network, ensemble kalman 
                         Filter, numerical weather forecasting.",
             abstract = "Weather forecasting systems require a model for the time evolution 
                         and an estimate of the current state of the system. Data 
                         assimilation provides such an initial estimate of the atmosphere 
                         where it combines information from observations and from a prior 
                         short-term forecast producing an current state estimate. An 
                         Artificial Neural Network (ANN) is designed for data assimilation. 
                         The use of observations from the earth-orbiting satellites in 
                         operational numerical prediction models is performed for improving 
                         weather forecasts. The data related to atmospheric, oceanic, and 
                         land surface state from satellites provides increasingly large 
                         volumes. However, the use of this amount of data increases the 
                         computational effort. The goal here is to simulate the process for 
                         assimilating temperature data computed from satellite radiances. 
                         The numerical experiment is carried out with the global model 
                         Simplified parameterizations, primitive-Equation Dynamics (SPEEDY 
                         ) with simplified physical processes of an atmospheric general 
                         circulation in tri-dimensional coordinates. For the data 
                         assimilation scheme was applied an ANN: a Multilayer 
                         Perceptron(MLP) with supervised training. The MLP-ANN is able to 
                         emulate the analysis from the Local Ensemble Transform Kalman 
                         Filter(LETKF). LETKF is a version of Kalman Filter with 
                         Monte-Carlo ensembles of short-term forecasts. In this experiment, 
                         the MLP-ANN was trained with supervision from first six months 
                         considering the years 1982, 1983, and 1984. A hindcasting 
                         experiment for data assimilation performed a cycle for january of 
                         1985 with MLP-NN, LETKF and SPEEDY model. The synthetic 
                         temperature observations were used. The numerical results 
                         demonstrate the effectiveness of this ANN technique on atmospheric 
                         data assimilation. The results for analysis with ANN are very 
                         close with the results from LETKF data assimilation. The 
                         simulations show that the major advantage of using MLP-NN is the 
                         better computational performance, with similar quality of 
                         analysis. The CPU-time assimilation with MLP-NN is 75% less than 
                         LETKF with the same observations. Actually, considering the 
                         supervised ANN for data assimilation, the most relevant issue is 
                         the computational speed-up for computing the analyzed initial 
                         condition for state model that accelerates the whole process of 
                         numerical weather prediction.",
  conference-location = "S{\~a}o Sebasti{\~a}o, SP",
      conference-year = "Feb. 26th to Mar. 2nd, 2012",
                 issn = "2238-1007",
           targetfile = "106RCintra.pdf",
        urlaccessdate = "15 jan. 2021"