Machine Learning for Weather Modelling: Predicting the Climate with Supercomputing

Predicting the weather is one of humanities’ oldest pastimes and direct necessities. From shamans casting lots in caves to satellites circumnavigating the planet; we have become more sophisticated and far more knowledgeable. Weather forecasting has never been easier than it is today and for that, we thank the happy marriage  of AI and High-Performance Computing.

What is HPC (High-Performance Computing)?

High Performance Computing (HPC) – also often referred to as supercomputing – is the ability to process data and perform complex calculations at high speeds by means of aggregating compute power.

Supercomputing is used for almost anything nowadays: rendering, robotics, video games, the cloud industry,  the metaverse… you name it! The compute power generated by HPC clusters far exceeds that provided by a regular home computer and allows us to process the big data required to build the AI models used in machine learning and deep learning.  Today, we explore the applications of supercomputing when studying the weather and its effects.

Weather Modelling and Forecasting

Being able to anticipate the whims of Mother Nature is an extremely important endeavour and, when done correctly, has the potential to save countless lives. Unfortunately, the data required for this process is particularly large and there are no less variables to consider. Weather forecasters utilise mathematical equations that factor in the physics behind the variables that influence weather – solar radiation, orbital distance from the sun, pressure, wind, temperature, and moisture – among many others. All this information is obtained through sensors or satellites and then fed into such equations, being  capable of determining future incidents based on the patterns of nature’s behaviour. This is how AI models are built, and how we can know of a volcano’s eruption weeks in advance or whether it will rain in your hometown or not.

We are speaking of billions of data, enough information to keep your laptop busy for hundreds (if not thousands) of hours, depending on the model trained. With supercomputing, we can do so in minutes and turn this tasking mathematical riddle into Numerical Weather Prediction models (NWP).

Some Examples…

NWP models describe the essential physical processes in the atmosphere, at the surface and in the soil and take their impact on the temporal evolution of the model variables like pressure, temperature, wind, water vapour, clouds and precipitation into account. In other words, they translate climate data into a comprehensible overview of the weather that can be used for weather forecasting and even anticipate natural disasters. Here are some examples:

● Air pollution forecasting: predict when the concentrations of pollutants in the air will attain levels that are hazardous to public health.

● Climate modelling: using General Circulation Models (GCM) to assess the human impact on the environment, deepening our understanding about the effect of chemical emissions or the greenhouse effect and its relation to climate change.

● Ocean surface modelling: studying ocean dynamics by using ocean wave models that determine the amount of energy transferred from the atmosphere into the layer at the surface of the ocean. This helps us anticipate wave movement and shoaling and estimate their potential dangers.

● Tropical cyclone forecasting: based on similar principles to the other NWP models, they study atmospheric dynamics and are used to predict tropical cyclones or hurricanes and their intensity.● Wildfire modelling: studying weather, fuel characteristics and topography to simulate wildfires and anticipate their behaviour, like how the fire spreads, its direction or the amount of heat generated.

Author: Guillermo Iznaola

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Supercomputing powers many aspects of our lives, and when rightly applied to areas such as weather forecasting, it has the potential to save them. At DeepSquare, we are continuously working on expanding our ecosystem and further developing an already growing software library that will allow developers to use the computing resources they need in a sustainable way. If you want to learn more about the Project, or if you want to connect with the team and the community, follow us on Twitter, LinkedIn or join our Telegram group.

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