DeepMind and Google’s mid-term weather forecasting models

DeepMind and Google’s mid-term weather forecast models outperform top weather stations

get 22 1 DeepMind and Google's mid-term weather forecasting models
get 22 3 DeepMind and Google's mid-term weather forecasting models
Image source: Original paper

Medium-term weather forecasting plays an important role in travel, agriculture and other industries.

Before planning a trip, we often want to know what the weather will be like in the next week.

However, traditional medium-term weather forecasting models have been facing technical difficulties.

As the volume of data was detected to grow rapidly, the predictive performance of the model failed to improve accordingly.

Recently, a team from DeepMind and Google published a paper building a multi-scale mesh graph neural network (GNN) called GraphCast for medium-term weather forecasting.

Outperforms the best previous machine learning models on 99% of the prediction objectives.

And outperformed HRES, the operational weather forecasting model in use today, on 90% of the forecast objectives.

Previously, they published research on short-term rainfall forecasting back in 2021.

The GraphCast approach is roughly as follows: First, the surface of the Earth is cut into small pieces.

Given the data of the weather at each location at the previous time points (one time point = 6 hours).

It calculates the weather data recursively and step by step for each of the next 40 time points.

The spatial resolution of the prediction reached 0.25° latitude and longitude.

The drawback of traditional convolutional neural networks (CNNs) is that they can only express local associations of data.

The multi-layered grid technology developed by GraphCast allows for the establishment of spatially spanning correlations between any two regions on Earth.

This is more appropriate for weather forecasting.

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