A neural network approach for the retrieval of profiles of liquid water from radiometric data

An inversion technique based on neural networks is presented to retrieve profiles of cloud liquid from ground-based multifrequency radiometric data. In the study the use of neural networks is also applied for the dimensionality reduction of the unknown vectors. The effectiveness of the approach has been evaluated comparing its performance to that of more traditional linear techniques