Weather Forecasting: How Does Big Data Analytics Magnify it ?

  • Neelam Tyagi
  • Oct 17, 2019
  • Big Data
Weather Forecasting: How Does Big Data Analytics Magnify it ? title banner

Big data is a term used to define a massive amount of data on a large scale, be it structured, semistructured and unstructured, from several resources like media ( checkout role of ai in media) and public data, sensors data, warehouse data, etc. differing in formats like .txt and .csv files, image files, Html files, etc.

 

Data is collected and prepared at a very rapid rate with the help of superfast and highly processed computers for real-time and wide-ranged applications extensively. To bring this data into action and information, analysis of data is required and therefore big data analytics steps come out, we can deploy three main characteristics Volume, Velocity, and Variation to analyze large sets of data in order to ensure accurate information.   (You can also check out our blog on How Big Data Analytics is shaping up Internet of Things (IoT)’s)

 

Augmenting noticeable changes in weather becomes a serious issue to concern, day by day fluctuations in weather draws the attention of not only meteorologists but also analyst especially to forecast data. It also gives an interesting topic for researchers to explore and understand the reason behind the weather- everything like whats is going to happen tomorrow and what’s in the coming time. 

 

The study of changes in the weather is necessary to get numerous advantages such as saving lives, conquering risk, intensifying profits and quality of weather-based life, etc. To forecast weather, we need to analyze huge amounts of data, and thus big data is used as a trump card that provides many leads for forthcoming natural disasters like heavy rainfall, thunder, tornadoes, tsunamis, etc. in advance.

 

In this blog, you will learn about the weather forecasting as a function of Big Data technology and its application, we specify the role of big data analytics for predicting the weather changeable essence with respect to natural disasters and many other events. Let’s discuss this in detail. You might be already aware of how Kerala has affected by floods in 2018, from a study report major reason behind flood, “About 90% of the rainfall occurs during six monsoon months. The high-intensity storms prevailing during the monsoon months result in heavy discharges in all the rivers."

 

 

The animated image shows a glimpse of a huge loss of mankind in Kerela flood 2018

Kerala flood 2018: The flood was the worst in the state over the past years.

 

As a consequence, Kerala suffered from huge loss of mankind. There are many other similar events all over the world that can’t be controlled completely but we can save people, lands, properties, etc from these events.   

 

Below is a video by Al Jazeera that shows a glimpse of the situation in Kerala during the 2018 floods

 

 

Weather Forecasting is Imperative

 

Our day to day life is directly or indirectly depends on the weather in terms of economy and environment, it affects us with various factors as events, timing, duration, location, etc. In consideration of these factors, weather forecasting works with the parameters temperature, humidity, and wind speed. 

 

The weather forecasting is complex and challenging phenomena, an interaction between these factors and parameters is a necessity to authenticate weather forecasting. Explicit forecasting is an essential task directed by Big data analytics, in the era of data, many ways had developed in the analytics domain for correct and quicker results, thanks to analytical algorithms!!!

 

Weather-applications are generic to data analytics-based applications in weather, by knowing the accurate situation of the weather using data, it can be used for solving many unusual problems in many businesses; 

 

  1. In agriculture, the forecast is required for when to plant, irrigate and harvest crops on time. Weather forecasting also indicates the knocking floods, and it is suggested to harvest the crop timely even if only a 60% crop is matured. Similarly, an indication of initiation of rainy season helps farmers to sow crops timely. The presence of fungal pathogens in wind also are shown in weather predictions in order to treat a crop by managing pests and fertilizers use. (You can also check out our article on 5 Types of Approaches and Technologies to Improve Agriculture Analytics)

 

  1. In sports, weather prediction has its own roles in sports, there are many applications that tell us where to play, within how many days, what could be the best time, what will be the current climate of the place where the game is going to organize, etc provided in the what weather organization wants games.

 

  1. In forestry, a proper prediction is required for preventing and controlling, and the safety of wildlife and wildfires, circumstances of spreading the harmful insects can be predicted, etc. 

 

  1. In medication, environmental factors such as temperature, humidity, presence of dust, quality of air, cold or warm climate etc are related to patient health, predictions are helpful to provide a current condition of a particular place for patients of asthma, allergic, wheezing, cold and coughing, eye-flu, etc.

 

Many other organizations also depend on weather forecasting and demand for accurate weather predictions for their smooth functioning without any disrupt like airport control management, construction work, utility companies are the places where weather predictions are essential. 

 

Along with these business events, weather predictions have an impactful effect in predicting or estimating natural disasters like predicting floods, volcanos, thunderstorm, heavy rainfalls, etc. We know that Big data analytics can contribute to plenty of information and insights about disasters, this can be used to get daily climatic conditions and catastrophic events that give warnings about tsunamis, hurricanes, etc.

 

In your mobile apps, you must have seen apps as barometers, gyro meters, and other sensor IoT-based apps (Take a look at some other applications of IoT here) that record the data like wind pressure, wind speed, precipitation, temperature, and humidity of a particular location and the time at which data is recorded, required for weather predictions. 

 

All the industries get affected by weather unbiasedly, an organization could make the smart decision and strategies about the future by following the weather impact in advance, for this an organization need to unite its proprietary data with weather data in order to get a wider understanding of how to predict and how to influence business solutions. Many Organizations serving in retail, transportation, distribution, etc are major industries that use analytics to ascertain how to staff, design for demand, decrease damages, also have an opportunity to use weather data strategically.

 

 

IBM’s Deep Thunder: Weather Predictions Application 

 

It is a famous application for weather forecasting powered by Big data, it gives a forecast of the extremely specific location like a single city or single airport, local authorities get the sign of dangers in real-time and manage their work accordingly. 

 

 

Deep thunder is an application used for weather predictions in real-time using various modeling techniques.

IBM Deep thunder: preparing for harvesting on time with the modeling technique

 

Deep Thunder can yield much important information like evaluating areas where the floods more likely to have happened, estimating direction and scale of tropical storms, determining the amount of heavy snow, rainfall, and dropping of power lines in an area, estimating areas where roads and bridges are damaged and many more.

 

The following is a video published by IBM Research giving a glimpse of how IBM's Deep Thunder is being used for precision agriculture

 

 

Conclusion

 

I believe you must have gotten an idea of how Big data analytics has proven to be a fruitful application in weather forecasting. Data is like fuel, it is mandatory to prepare the correct form of data for decision-making and to come up with meaningful information. The data has to be taken with respect to the location and the time at which data is noted for weather forecasting. For more blogs on Analytics, Do read Analytics Steps!

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