DeepMind is able to forecast the weather for the next 10 days in 60 seconds

Weather forecasting is another activity that will be affected by Artificial Intelligence, and recently the company DeepMind has published an article in Science magazine indicating that its program (with the help of AI) can provide more accurate weather data for the next 10 days of air pressure, winds, humidity and temperature.

That is, the weather forecast that is published in all the media. And they have called that “model” GraphCast. Just as an example, what GraphCast calculates in one minute, current weather forecasting programs take several hours, and achieve less accuracy.

Weather prediction is a complex task that requires processing a large amount of data. Traditional weather models are based on physical equations that describe the behavior of the atmosphere. However, these models can be slow and expensive to run.

Artificial intelligence (AI) offers a new way to approach weather prediction. AI models can learn from data without needing to understand the underlying physical equations. This makes them faster and more flexible than traditional weather models.

GraphCast works by analyzing historical and current climate data. The model uses a neural network architecture called “graph” to represent the relationships between the different factors that affect climate. This allows the model to learn the complex relationships between these factors and make better forecasts.

GraphCast uses a 10-year dataset of climate observations from around the world. The data set includes data on temperature, pressure, humidity, wind and other weather factors.

The GraphCast AI model is trained on this data set. The model learns to predict future climate from current and past climate data.

The model uses the neural network architecture called graph. Graph neural networks are a type of neural network used to represent relationships between data. In the case of GraphCast, the graph neural network represents the relationships between the different factors that affect the climate.

GraphCast has been tested on a number of test data sets. On these data sets, GraphCast has proven to be more accurate than traditional weather models.

For example, on a test data set covering the United States, GraphCast was able to predict temperature with 95% accuracy. This is significantly better than the 90% accuracy typically achieved with traditional weather models.

This program has various applications. It can be used to improve weather forecasts for travel planning, agriculture and other activities.

It can also be used to predict extreme weather events, such as hurricanes and floods. This could help people prepare for these events and reduce the risk of harm.

GraphCast is a promising new tool for weather prediction. It is faster and more accurate than traditional weather models, and has the potential to improve our understanding of climate.

And as it continues to develop, it could have a significant impact on the way we forecast the weather and prepare for extreme weather events.

Over time we will see how it can help us, but what is very clear is that AI is introduced into all activities that require thinking and calculating.