Data Science for Weather Prediction – The Prerequisite to all Natural Disasters

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Data Science for Weather Prediction – An Exceptional Support 

Ever wondered how the news channel predicts the weather conditions accurately? The answer is because of data science. It always works in the background in the whole process of weather prediction. For all individuals and organizations, it is a great deal to know the accurate situation of the weather.

Many businesses are directly or indirectly linked with climatic conditions. For instance, agriculture relies on weather forecasting to plan for when to plant, irrigate and harvest.

Similarly, other occupations like construction work, airport control authorities and many more are dependent on the forecasting of weather. With its help, businesses can work with more accuracy and without any disruptions.

Advantages of Weather Forecasting

Below are the essential benefits of weather forecasting:

  • People are warned prior to what the weather will be like on a particular day.
  • To help people take proper precautions to secure themselves and their families in case of unwanted occurrences.
  • Organizations can work better with the help of accurate weather predictions.
  • It helps to deliver visual forecasts by various methods that most companies prefer.
  • Weather forecasting highly benefits the agriculture sector for buying/selling livestock.
  • It also assists the farmers to decide when to plant crops, pastures, and when to irrigate. Because having a system that tells you the soil is dry but you don’t need to irrigate because it is going to rain after a few hours seems to be an interesting use case. Isn’t it?
  • It is the best method for management of inventory, selling strategies and crop forecasts.
  • It provides the business with valuable information that the business can use to make decisions about future business strategies.

Weather forecasts are made by collecting the maximum amount of data possible about the current state of the atmosphere (particularly the temperature, humidity, and wind) and using the understanding of atmospheric processes to determine how the atmosphere evolves in the future.

The people usually responsible for the collection and analysis of data are the Data Science Experts!!

Data Science for Weather Prediction

There are various subprocesses involved in the complete process of data science for weather prediction:

Data Science for Weather Prediction

1. Predictive Modeling and Machine Learning

Weather models are at the heart and they are used both for forecasting and to recreate historical data. However, over the last decade, machine learning has increasingly come to be applied in atmospheric science.

Machine learning takes weather data and builds relationships between the available data and the relative predictors. ML can help improve physically grounded models, and by combining both approaches, they can get accurate results. Sophisticated models and ML are used to forecast the weather using a combination of physical models and measured data on huge computer systems.

Over the last few years, data scientists have come to realize that in the foreseeable future they are always going to need ML and predictive models to be able to provide close to perfect results. They say- Artificial Intelligence (AI) is the next step to guard the storms!

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2. Data – A Crucial Part of Weather Predictions

It is necessary to have the right data to be close to accurate decisions. The data needs to be taken with respect to the location and the time at which it is noted has to be considered.

Today, all the devices are IoT-enabled with gyrometer, barometers and all sorts of sensors in it. So, the location from one standpoint to another is very well available. Therefore, mobile phones proved to be revolutionizing the analytics weather industry and they have really changed the industry.

In the case of using weather data, the data has to be used within minutes itself because nobody wants to know what had happened in the past. All of which is important – what is happening now and what will happen in the future. So in order to come up with meaningful information, the data has to fall in and fall out quickly and recycle quickly, within minutes.

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3. Weather Data – An Aid for many Events

  • Prediction of Floods and Natural Disasters – Floods and other natural disasters can be predicted by weather data analytics using models. This requires collecting data like the surrounding road condition and the rainfall of the area that year.
  • Sports – In sports matches such as cricket, weather like rainfall can lead to delaying or even abandoning the game in between. Weather forecasting can help in deciding the time for matches prior to reducing the chances of pausing the game.
  • Predict Asthma Attacks – Weather data can be used to predict severe medical issues such as asthma. The inhalers used during an asthma attack have sensors in them which can gather data to ensure that they are properly used by the patients. It collects data related to the temperature, humidity, air quality, and presence of dust in particular areas (where the patient spends the most time). This information can help reduce the chances of attacks by predicting where asthma can be triggered.
  • Predict Car Sales – Weather data can even be used by car dealer/sellers to figure out car sales in a particular climatic situation. For example – in the rainy season, people feel timid but have to go out due to work or other reasons and hence end up buying a car.

4. Satellite Imagery and Sensor Data

Today, the primary source of atmospheric science is satellite imagery and that does not mean pretty pictures though!

Satellite imagery comes in different sizes and shapes. Some satellites operate in the black and white spectrum, some can be useful to identify and measure clouds, others to measure winds over the oceans.

Most data scientists rely on satellite imagery to generate short term forecasts, to determine whether a forecast is correct, and to validate models too.

Machine learning is also used here for pattern matching. If it acknowledges a pattern that has already appeared in the past, it can be used to predict what is going to happen in the future.

Sensor data are mostly used to make predictions at a local level to ground-truth weather models when using reliable equipment.

This is a satellite image of the formation of low-pressure areas in Odisha coast in India. Thirteen days before the cyclone ‘Fani’ hit the area, IMD ( the India Meteorological Department) had an indication that there could be a massive storm and they started preparing for the outbreak.

A record 1.2 million people (equal to the population of Mauritius) were evacuated in less than 48 hours just because of data scientists. It was one of the strongest cyclones to have hit India in the last 20 years.


This was all in data science for weather prediction article. There is still room for many businesses to understand that historical weather data and data science models can help them improve their tactical and strategic decision-making. Data is the new currency, more and more of it exists and so more and more decisions can be made using it.

Anyone planning a BBQ or a trip to somewhere over the course of the summer will already have done their work with the weather app. People are getting aware and they are relying on weather forecasts to plan rainy days-in and sunny days-out!

In what way, you are going to use the weather prediction for your benefit? Do tell us in the comment section. And, don’t forget the role of data science in it😁.

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1 Response

  1. Joerg says:

    My city just hit by a severe stom. 20,000 people with out power.

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