Global Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation

摘要

An Artificial Neural Network (ANN) is designed to investigate its 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 goal here is the process for assimilating meteorological observations. The numerical experiment is carried out with global model: the "Simplified Parameterizations, primitivE-Equation DYnamics" (SPEEDY). For the data assimilation scheme, it was applied a supervised ANN: the Multilayer Perceptron (MLP). The MLP-NN 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 with MLP-NN was 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.