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Can Machine Learning Replace Traditional Weather Prediction?

Can Machine Learning Replace Traditional Weather Prediction?

Machine learning uses artificial intelligence to teach computers how to learn concepts and processes without the aid of humans. As technology evolves, so do its applications. Machine learning is part of the future of society as machines can assist humans in performing tasks more efficiently. 

In machine learning, machines build algorithms based on supervised or unsupervised data. These algorithms help machines predict several outcomes. Machines will be able to make decisions and process big data without human help. 

There are many applications of machine learning. Do you ever wonder how weather predictions are made? Machines most certainly have a say in that department.

How is the weather forecasted?

The first weather forecasts were made around the 15th century after the invention of the thermometer and the barometer. In the 1800s, the first weather maps were invented. Scientists of the time would rely on these maps to predict the weather for the next few days. Early weather forecasts relied on previous observations to predict the weather for the next days.

Fast forward to the 21st century, reading weather maps is quickly becoming a thing of the past. Today, weather forecasts are made by using machines. This is where machine learning becomes important. 

Numerical Weather Prediction Models

Data observation is done by meteorologists with the use of machines and supercomputers. The data is then inputted into a numerical weather prediction (NWP) model. The model processes and analyzes the data to come up with a fairly accurate weather forecast.

Weather prediction models can differ per state, country, and region. The best weather models have high resolutions. These NWP models can predict the weather in several small areas as well as in a larger area, such as an entire state. Typically, forecasts made for the next five days are almost 90% accurate.

As weather prediction models are different from each other, the resulting forecast can vary as well. Some weather models focus on temperature and wind speed while others may focus on humidity. Experienced meteorologists operate most weather models used today.

Although machines can forecast the weather, there are some conditions that machines cannot predict. The experience of a meteorologist in the area is important to get a more accurate analysis of the data. Acts of nature like hurricanes and tornadoes are generally harder to predict. This is why scientists who have experience in the area are usually needed to analyze erratic or sudden changes in the observed data.

How can machine learning improve weather forecasts?

In this age of technology, machines are constantly improving. Machine learning is predicted to replace several human tasks to boost productivity. When talking about the weather, can machines replace traditional weather forecasting?

The NWP models that are used today can provide fairly accurate results. These models work by processing data that come from several other models. The combination of data from satellites and human observation is used to forecast the weather. Although data is constantly gathered, current weather models are unable to process all the data at once.

In recent years, machine learning techniques have been tested for weather prediction. These techniques are being adapted to improve the accuracy of data processing and analysis. The goal is to create a more accurate weather forecast. But, machine learning algorithms are not yet as accurate as experts would like.

Can machine learning replace traditional weather forecasting? Yes, it can. Will machine learning replace it right now? Well, not quite yet.

Machine learning still has ways to go to improve weather forecasts. Scientists and data engineers are constantly working on developing machine learning applications for use in weather prediction. These models aim to predict the weather with several variables. These variables include wind, temperature, humidity, and precipitation, among others.

Advanced machine learning models can likely find a solution to inaccurate weather forecasts. But the accuracy of the algorithms created by these machine learning models will still depend on data parameters.

Eventually, the goal of machine learning for weather prediction is to make use of the abundance of data coming from other models. Machines will be able to analyze all the unlabeled data to create more accurate forecasts. Deep learning models may also be utilized to create accurate predictions.

Conclusion

Machine learning is still in the stage of development. Scientists, data engineers, and tech professionals are continuously working to improve machine learning applications.

At this point, the use of numerical weather prediction models is still strongly backed by the scientific community. NWPs have already gone through several stages of research and development. These models have been continually improved upon by scientists and meteorologists alike. 

Machine learning models may not be ready to replace NWPs yet, but the future is bright for machines. Sooner or later, machine learning models can develop more accurate algorithms. This can potentially make weather prediction more consistent than numerical weather prediction models.

Submitted by bashsarmiento on May 17, 2022

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