It might not be possible to avoid natural calamities however a good early warning could enable adequate preparedness. We have seen a lot of natural calamities occur worldwide recently. The most recent one being Cloudburst and fast flood in the Uttarakhand state of India which resulted in loss of 1000+ lives of pilgrims. The rescue efforts are still underway where the Army and local authorities are working day and night to save those still stranded. Scientist and environmentalist might be well working to identify what caused this erratic behavior of Mother Nature but I believe data science could help predict such events more accurately and make sure that we are more prepared to tackle such events in the future. (I am sure there are a lot who would like to doubt the preparedness of the local authorities even if such early warning system was available)
The weather patterns have been changing a lot in the past few decades(I hope you guys watched the hollywood flick “2012”) and this could be attributed to the rapid urbanization and environmental pollution. Although a lot of data is being captured from satellite and weather stations, the traditional prediction system may fail to provide accurate information owing to various factor. This is where I believe Big Data could be of great help.
South Korea is upgrading its national weather information system with the goal of understanding weather patterns better and predicting better the location and ferocity of weather events. The Korean Meteorological Administration has increased its agency’s data storage capacity by nearly 1000% (9.3 petabytes)
The Indian Meteorological Department (IMD) too has a lot of data; I recently saw an advertisement from the IMD notifying sale of rainfall data for 100 years (1901 – 2000) available for Rs.1,000/- (Just wondering if this was too much or too cheap for a decades data). One thing for sure is that we have the data but we still are unable to use it well. Weather forecasting is a crucial application in meteorology. Weather is a continuous, data-intensive, multidimensional, dynamic process that makes weather forecasting a formidable challenge. This is where Big data analytics outsmarts the traditional data mining.