Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography

• Published in: Bulletin of the American Meteorological Society 1998-09, Vol.79 (9), p.1855-1870
• Main Authors: Hsieh, William W., Tang, Benyang
• Format: Article
• Language: English
• Subjects: Adjoints; Atmospheric models; Climate models; Earth, ocean, space; Exact sciences and technology; External geophysics; Forecasting models; Geophysics. Techniques, methods, instrumentation and models; Marine; Meteorology; Modeling; Neurons; Oceanography; Principal components analysis; Time series forecasting; Weather forecasting
• ISSN: 0003-0007; 1520-0477
• DOI: 10.1175/1520-0477(1998)079<1855:annmtp>2.0.co;2

Empirical or statistical methods have been introduced into meteorology and oceanography in four distinct stages: 1) linear regression (and correlation), 2) principal component analysis (PCA), 3) canonical correlation analysis, and recently 4) neural network (NN) models. Despite the great popularity of the NN models in many fields, there are three obstacles to adapting the NN method to meteorology–oceanography, especially in large-scale, low-frequency studies: (a) nonlinear instability with short data records, (b) large spatial data fields, and (c) difficulties in interpreting the nonlinear NN results. Recent research shows that these three obstacles can be overcome. For obstacle (a), ensemble averaging was found to be effective in controlling nonlinear instability. For (b), the PCA method was used as a prefilter for compressing the large spatial data fields. For (c), the mysterious hidden layer could be given a phase space interpretation, and spectral analysis aided in understanding the nonlinear NN relations. With these and future improvements, the nonlinear NN method is evolving to a versatile and powerful technique capable of augmenting traditional linear statistical methods in data analysis and forecasting; for example, the NN method has been used for El Niño prediction and for nonlinear PCA. The NN model is also found to be a type of variational (adjoint) data assimilation, which allows it to be readily linked to dynamical models under adjoint data assimilation, resulting in a new class of hybrid neural–dynamical models.