A High-Accuracy and Fast Retrieval Method of Atmospheric Parameters Based on Genetic-BP

In this article, we propose an accurate and fast method that is based on the genetic algorithm and the Back-Propagation neural network (GABP) to improve the accuracy and speed of retrieving atmospheric parameters (temperature, humidity, liquid water content, and so on). Firstly, the genetic algorithm is applied to improve and optimize the connection weights and thresholds of the BP neural network according to situ data measured by an sixteen-channel ground-based radiometer and a radiosonde, and then obtain a more proper range of those weights and thresholds. Secondly, the BP neural network is trained once again to get an ideal BP neural network model in a relatively short time. Finally, the model (or GABP), in which a genetic algorithm (GA) is combined with the BP neural network algorithm, is tested by the situ measurements of the sixteen-channel microwave radiometer. The tested results by GABP are not only in a good agreement with those of other known models, and but also has better accuracy and faster convergence speed than those reported algorithms for retrieving atmospheric parameters. It can make a conclusion that GABP is a new algorithm which is capable of quickly retrieving accurately atmospheric parameters.