Artificial Neural Networks (ANNs) that were originally proposed to model brain functions mathematically are now being used in many branches of research, including the atmospheric sciences. The main contribution of this paper is the development of ANN to identify the presence of thunderstorms (and their location) based on microwave measurements from all five AMSU-B frequencies and on surface observations over Iran (45–65°E, 25–45°N). From the surface observations, the position of each of the different meteorological events (thunderstorm, heavy rain, rain, light rain, snowfall and clear sky) is located in AMSU-B images. An area around this position with roughly the same brightness (visually) is selected. Then the brightness temperatures (TBs) of a few randomly selected pixels from this area are noted. In all, there were 560 pixel-points, over 20 passes of AMSU-B, in these selected areas. It is found that the thunderstorms are related to image features that are bright in all of the five-frequency images (i.e. have low TB), as expected.

The architecture of the neural network that was finally selected for identifying thunderstorm events (as against other reported events), after a series of trial and error for the best accuracy, was achieved using the four-layered Feed-Forward Artificial Neural Network (FFANN) with five nodes in input, two hidden layers with four nodes each, and one node in the output layer. In the ANN output, the thunderstorms clustered clearly away from other phenomena. ANN gave more correct thunderstorm recognition (96.7%), whereas the discriminant analysis gave 95.3% correct recognition without clear separation between the two classes.