Weather forecasting can be an uncertain job. Traditionally, this has been done through physical simulations in which the atmosphere is modeled as a fluid, where future predictions were done based on the present state of dynamics.
However, this process has some uncertainties as understanding the complex process of atmospheric conditions is pretty unpredictable.
Think about it, how easily the weather change nowadays? Can you actually predict anything at all?
That’s why machine learning is gaining so much importance in the field of weather forecasting.
But before discussing that, let’s understand what machine learning is.
What is Machine Learning?
According to a Springer Link paper, “Machine Learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.”
It is closely related to computational statistics, which is basically using computers to make predictions.
Now, machine learning is based on AI & has a plethora of algorithms, models & approaches but we will keep that for another day.
Why use machine learning in weather forecasting?
With machine learning, it is relatively robust to perturbations and doesn’t require a complete understanding of the physical processes that govern the atmosphere.
Before the advancement of technology, weather forecasting was extremely difficult. Weather forecasters relied upon satellites, data model’s atmospheric conditions with less accuracy. With the advancement of Data Science, Artificial Intelligence, Scientists now do weather forecasting with high accuracy and predictability.
In today’s world where Global Warming is a serious issue, we need advanced technology to predict the uneven pattern of storms, hurricanes, cyclones or floods.
Right now, there is a lot of buzz around applying descriptive ML in hazard detection, especially to storm tracking.
Trained experts are able to recognize storms and trace their paths from weather imagery which will be a great achievement for the parts of the world with a high probability of natural calamities!
What technology is being used for weather forecasting via machine learning?
Well, to begin with, we need to talk about the two most important in predicting the weather, which is Linear Regression and a variation of Functional Regression.
These models are trained based on the historical data provided of any location. Input to these models is provided such as if predicting temperature, then minimum temperature, mean atmospheric pressure, maximum temperature, mean humidity, and classification for 2 days.
With the help of these models, we can actually predict the maximum & minimum temperature of 7 days at least.
Another company is Google, which is trying to prefect weather forecasting is using ML programs.
How Google is planning to use machine learning to predict the weather?
Have you heard about the Butterfly Effect?
It is basically a technology where any small change in a complex phenomenon can have a significant impact on the outcome, stays true to the process of weather forecasting, where a slight amount of change in one variable could drastically change the future climatic events.
Weather is really unpredictable & that is exactly where AI plays a huge role.
One of the most significant advantages of this trained ML model is the speed at which it can forecast the weather.
Google with its new model of ML would only take 5-10 minutes to generate a forecast, which used to carry around six hours earlier. This is because the newly trained model doesn’t try to model a complex weather system; instead, it makes predictions on simple radar data.
According to the paper submitted by the company, “As weather patterns get easily altered by climate change, which can lead to increase in extreme weather, it becomes crucial for weather departments to attain actionable insights at high spatial and temporal resolutions,”
How ML Algorithms are used in predicting the weather?
Weather Forecasting using Machine Learning Algorithms is primarily based on simulation-based on Physics and Differential Equations. Artificial Intelligence is also used for predicting weather. This basically includes models like Neural Networks and Probabilistic model Bayesian Network, Vector Machines.
Neural Network is widely used to capture non-linear dependencies of past weather trends and future weather conditions.
Another example of the ML application is that descriptive ML can address the challenges posed by data volume and complexity when dealing with data from physical simulations.
So basically, these highly multidimensional models could summarise the salient features and bring them to the forecaster’s attention would help streamline this task.
The future of weather forecasting
As machine learning advances and more weather models start integrating it, weather forecasting will become increasingly accurate.
There is so much scope & potential of global nowcasting, which is a relatively new addition to weather forecasting. Weather, machine learning can be added to weather forecasting to extend nowcasting to places such as Russia that lack widespread radar coverage.
As smart system penetration grows worldwide, more people will gain access to accurate, hyperlocal weather forecasting as well.
Well, how are you planning to implement machine learning & AI into your business? Want some guidance? Talk to Skyram Technologies. Not only guidance, we will develop AI programming perfect for your business! Connect with us here.
Also, for our other blogs on AI & machine learning, read here!
