Abstract
Using a feed-forward neural network, an inverse algorithm was developed to profile the vertical structure of temperature and specific humidity. The inverse algorithm (inverse model) was used to calculate temperature and humidity profiles, which were then compared with other existing methods. The inverse model is found efficient in profiling the vertical structure of temperature and humidity as compared to other existing methods. For example, the statistical methods notorious for their high computational cost, altitude-dependent error, and inability to accurately retrieve the vertical temperature and humidity profiles, are enhanced with an inverse model. The inverse model’s diurnal and seasonal cycle profiles are also found superior to those of other existing methods, which could be useful for assimilation in numerical weather forecast models. We suggest that incorporating such an inverse model into the ground-based microwave radiometer (GMWR) will enhance the accuracy of the vertical structure of temperature and humidity profiles, and so the improvement in weather forecasting. The developed inverse model has a resolution of 50 m between the surface to 500 m and 100 m between 500–2000 m, and 500 m beyond 2000 m.