Forecasting Future Atmospheric Events using Machine Learning
Kaushik K. Rana1, Ketan Sarvakar2, Anamika Mittal3

1Kaushik K. Rana, Assistant Professor in VGEC Chandkheda, Gujarat, (Gujarat), India.
2Ketan Sarvakar, Assistance Professor, Information Technology Department of U. V. Patel College of Engieering, Ganpat University, Kherva, Mehsana,  (Gujarat), India.
3Anamika Mittal, Assistance Professor, Information Technology Department of Government Engineering College, Gandhinagar, and (Gujarat), India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2782-2785 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8489078919/19©BEIESP | DOI: 10.35940/ijitee.I8489.078919
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Abstract: Over the year’s thunderstorms have been one of the major causes of death and one of the most catastrophic natural calamities in the country and the most challenging part is the prediction of thunderstorm beforehand, because of the random nature of our atmosphere. Through this research paper the attempt was made to do analyze the atmospheric stability indices (atmospheric instability causes thunderstorms) using INSAT-3D sounder data to predict thunderstorms by predicting their values using machine learning approach. By setting the indices possess a threshold value and also there is predictability in the data which can be used to predict their future values. The tephigram is used by meteorologist, scientist, weather observer, pilots to solve atmospheric temperature and humidity problems using simple graphical techniques. We can avoid extensive calculation for the mathematical relationships to generate diagram to predict the events. Meteorologists use the thermodynamic diagram daily to forecast cloud height and atmospheric stability and using the tephigram is use to generate and integrate LIVE events to show easier for the users to view the thermodynamic diagram instantly.
Keywords: Atmospheric events, Determinism Weather, Machine learning, Predictability, Weather prediction.

Scope of the Article: Artificial Intelligence and Machine Learning