The AI ​​revolution in climate and climate science has not changed

Some limitations should be apparent. This does not mean a species that has never been trained or any other species that is significantly different from the model. The quality of the training data is also very important. If we only use images of chickadees in pine trees, the example would include pine needles in its definition of chickadee-ness.

Without a lot of additional work, we won’t know Why? the model arrives at its solutions. The internal processes are pretty much a black box most of the time.

The highs are real, though. Machine learning algorithms often outperform our human-made algorithms, especially in terms of computing power, if not accuracy. They just have to be used properly, or deficiencies will show.

Cloud computing

For weather forecasting, the process is not very different from our bird recognition model, but the models are trained on two weather patterns that occur a short time apart.

Because they are not solving many physics equations at each location, these models run much faster than normal weather conditions.

Several companies, including Google, Nvidia, Huaweiand Microsoftthey have developed preliminary models—sometimes in collaboration with independent academics—that can closely match the predictive models we currently use. As we began to understand where models were successful and where they struggled, some of the major weather stations began to develop their own.

The European Center for Medium-Range Weather Forecasts (ECMWF) has deployed its first machine learning model. in February 2025it is running alongside its earlier version of the Integrated Forecasting System (IFS).

The Image of AIFS are taught using a reanalysis– a map that is created based on the weather conditions and writes a similar picture where we don’t have measurements. This sophisticated tool greatly supports the machine learning task of global forecasting (six hours ahead) based on past images.