Weather forecasting is one of the most challenging jobs given the risk involved if wrongly interpreted and the lives of people being at stake. The large number of variables involved and their interdependence on each other further makes it complex. In order to make the right decisions, predicting the weather conditions is extremely important for both humans and businesses like agriculture, air traffic controlling, construction and many more. In one way or the other, every business is dependent on favorable weather conditions. Given the need to predict the weather conditions accurately, the meteorological department collects huge amounts of data every day and every minute to keep an eye on every change taking place and that data has to be stored for future reference and, therefore, needs to be in an organized form to extract useful correlations from it. Data science empowers the weather forecasters to collect, store and analyze this enormous amount of data to be used in more scientific and organized manner to forecast accurate and efficient weather conditions. Softweb’s Intelligence and Analytics software processes the following feed:
Live weather information from different parts of the world
Precipitation, pressure, cloud structure, temperature, wind speed, water level in water bodies and humidity data.
Previous weather data from data libraries for referral.
Data science had proved to be a boon for the meteorological department in the context of huge data they have to deal with and for the society in general. Some of the direct benefits of accurate weather forecasting due to advancement in technology are:
Helps people to take timely action in case of any natural calamity been predicted to
minimize the loss of life and property.
Helping people plan their activities in advance according to the weather forecast.
Helps farmers and agricultural organizations to plan buying and selling of livestock.
Helps farmers in planning the plantation of crops, water supply and reaping.
Provides businesses the ability to make decisions about future manufacturing and
An example of big data application in weather forecasting is Deep Thunder by IBM. Deep Thunder gives the weather forecast of extremely specific geographical locations such as an airport or a small island, unlike other systems that give general information for a large region. It can also estimate severity of flood in a region, direction and strength of an approaching storm, precipitation amount in an area, and wind speeds. The information is very helpful to prepare for emergency conditions like evacuating low-lying areas, arranging adequate supply of food and medicine etc. Deep Thunder was used by Brazil during Rio Olympics 2016 and helped in the successful organization of event by precisely knowing the weather conditions in advance. Such programs are extremely important for tropical countries to prevent any loss to nature. Data science has made it possible to predict natural disasters like flood, drought, hurricanes etc. The dramatic change in precision from that of twenty years back has allowed disaster management organizations to be prepared far well than they used to a few decades ago. This has also helped insurance companies to sell insurance policies to farmers for their crops. Data Science helps these companies analyze the risk involved by referring the weather reports of that particular area.
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