Schumann resonance: A latest wonder for climate forecast!

Enhanced tropical convection is associated with increased upper-tropospheric relative humidity. The positive co-relationship between deep convection and upper-tropospheric humidity is observed for both regional and temporal variations over a wide range of space and time scales. The regions of increased upper-tropospheric moisture are found to be strongly correlated with an enhanced greenhouse trapping, although the effects of lower-tropospheric moisture and temperature lapse rate are also observed to be important. The greenhouse effect of water vapour increases sharply when temperature increases, leading to a positive feed back for climate change. Upper-tropospheric water-vapour (UTWV) variability and global lightning activity are closely linked suggesting that upper-tropospheric water-vapour changes can be inferred from the records of global lightning activity. The continental deep-convective thunderstorms could transport large amount of water vapour into the upper troposphere, which would dominate the variations of global UTWV while producing most of the lightning on Earth. Global lightning produces Schumann resonance modes, which are trapped in the Earth-ionospheric waveguide. As there are approximately 50-100 lightning flashes per second around the globe, the variability of the intensity of the Schumann resonances represents a continuous measure of the variability of global lightning activity which is dependent on UTWV. Thus monitoring of SR would provide a tool for time-tracking UTWV changes which will contribute to a better understanding of the processes affecting climate change. The correlation between troposheric water content and Schumann Resonance field can be explored using data of several years, which is still lacking to the world researchers. From the observed nature of correlation of time series data, a model will be built which will be made useful to forecast the water vapour content from the observed value of Schumann Resonance data. Climatic condition would be forecasted using SR data with the sequence below: SR data ⇒ Water vapour content ⇒ climate forecasting.