Título: Global Temperature Assimilation By Artificial Neural Networks For An Atmospheric General Circulation Model
Autores: Cintra, Rosângela S.; Velho, Haroldo F. de Campos
Resumo: 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 weather forecasting. Data assimilation is a method to insert observational information into a physical-mathematical model. The use of observations from the earth-orbiting satellites in operational numerical prediction models 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. The numerical experiment is carried out with global model: the ”Simplified Parameterizations, primitivE-Equation DYnamics”(SPEEDY). For the data assimilation scheme was applied an Multilayer Perceptron(MLP) with supervised training. The MLP-ANN is able to emulate the analysis from the Local Ensemble Transform Kalman Filter(LETKF). The ANN was trained with first three months for years 1982, 1983, and 1984 from LETKF. A hindcasting experiment for data assimilation cycle was for January 1985, with a MLP-NN performed with the SPEEDY model. The results for analysis with ANN are very close with the results obtained from LETKF. The simulations show that the major advantage of using MLP-NN is the better computational performance, with similar quality of analysis.
Palavras-chave: Artificial neural networks; multilayer perceptron; data assimilation; numerical weather forecasting
Código DOI: 10.21528/CBIC2011-14.6
Artigo em pdf: st_14.6.pdf
Arquivo BibTex: st_14.6.bib