How Can Deep Learning Solve The Problem Of Global Climate Change?

Observing a drastic change in climatic conditions, global warming is inevitably the topmost concern of various scientists. Many efforts are being made regularly through technology and earth-orbiting satellites to understand and stop climate change. As we proceed towards a global challenge in form of glacial retreats, warming oceans, and shrinking ice sheets, it is the need-of-the-hour to find a solution.

But, can we preserve our planet with deep learning even after the damage caused by human emissions and other environmental mistakes?

Most certainly yes!

Artificial intelligence is a technology that enables machines to simulate human intelligence. It includes learning, reasoning, and self-correction, which allows AI to decipher the code to many world issues. It has two applications: deep learning and machine learning. While machine learning offers automatic learning through computer programs, deep learning designs artificial neural networks. The deep means layers of neurons such as in the human brain.

Artificial intelligence has the power to offer transformative and innovative solutions to climate change and other climate issues. Let’s see how.

Climate Modeling with Deep Learning

Particularly in this field, artificial intelligence is helping with the emergence of climate forecasting, which seems like a good move looking at the amount of data. We already have years’ worth of climate-related and weather data available at our disposal and deep learning can simply put it to use. Artificial intelligence empowers us to utilize this data for better decision-making. The performance and efficiency of the climate and weather models can be improved to process complicated data such as evaluating fluid dynamics for the sake of oceans and atmosphere. In some cases, deep learning also helps in reducing the bias related to climatic and weather models.

When humans predict weather in catastrophic and extreme situations such as cyclones, the accuracy received is uncertain. Computers used for this purpose just take too much time for climate simulations. But, when deep learning enters the picture, it helps achieve enhanced spatial resolution related to climate models, which are then used to observe images for patterns. This is, in fact, done in a fast manner through deep learning.

For instance, the National Energy Research Scientific Computing Center developed Cori, a supercomputer, which offers the capability of achieving 99.1% accuracy when analyzing air flows, weather fronts, and tropical cyclones.

It is known that the climate model images are related and connected to the classification algorithms of ML that are used for images. Using this knowledge, NERSC established an amazing technique which is called Convolutional Neural Network (CNN). The researchers of the team initiated by exploring various techniques and software such as Self Organizational Maps, Toolkit for Extreme Climate Analysis, and Deep Neural Network for Precipitation Nowcasting. These techniques show a development model of how ML can assist in climate analysis.

Using these insights, CNN was developed for various instances such as Weather Front, Atmospheric River, and Tropical Cyclone. The training was found out to be 89%, 90%, and 99% respectively as far as image accuracy is concerned.

In another instance, a project in Germany was initiated to locate and differentiate wind turbines through images obtained from satellites. The neural network architecture that was used for the process was U-Net and it led to image segmentation. The output received was in the form of pixel – predicting it belonged to a wind turbine. The deep learning structure used here was trained to find out polygons of the wind turbine in 280,000 satellite images, which involved North Rhine-Westphalia. The output obtained was moved to ArcGIS Geographic Information System and it was made available across various devices.

This CNN was made to find out the wind turbines on the ground using images from satellites. The output data helped in monitoring the current availability of wind energy.


Since climate data is huge and it is not possible for humans and even computers to process this data accurately at a fast pace, deep learning has a lot to offer us. These algorithms when trained to work in a certain order can offer deep insights about climate change, which can then be used to take appropriate future measures.