ENHANCING VERTICAL RESOLUTION OF SATELLITE ATMOSPHERIC PROFILE DATA: A MACHINE LEARNING APPROACH

We developed a statistical approach using the Artificial Neural Networks (ANN) to improve the vertical resolution of tropospheric relative humidity profiles (RH) from 20 pressure levels to 171 pressure levels. The model is based on an unconventional method in which we used the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Global Positioning System Radio Occultation (GPS RO) data and the corresponding observed values of RH data. The model was developed using 3 years COSMIC daily data during 2007-2009 over the north Indian Ocean and produced high vertical resolution RH (171 pressure levels) output data from the coarse resolution inputs (20 pressure levels). We achieved the best performance in generating high vertical resolution data with a Pearson's correlation coefficient (CC) of greater than 0.94 and scatter index (SI) of less than 0.1 throughout all pressure levels. Thus, the present approach is an efficient method to achieve the better vertical resolution of RH data from geostationary satellites.