Reconstruction of global Ozone density data using a gradient-descent algorithm

The Ozone density and its decrease in the atmosphere are one of the main concerns of nowadays. The Ozone density is tracked using the Ozone Measurements Instrument (OMI). During tracking and collection of information, missing data occur because of the pathway the instrument is going. Reconstruction of those missing data can be done using the results from the compressive sensing (CS) approach. The application of a CS algorithm based on the gradient-descent method for the Ozone density data reconstruction is presented in this paper. The algorithm is based on varying the missing data to promote sparsity in each frame. The algorithm is modified for a more efficient reconstruction by using the dynamic information about previous frames. The reconstruction results for some recent Ozone data are presented.