Extreme weather events, whether scorching temperatures that ruin crops or killer storms that drown coastal towns, are likely to be more frequent and more powerful with climate change.
Quantifying the increase in these extreme events (and their economic and public health costs) requires combing through thousands of gigabytes of data that climate models generate every day.
Scientists can’t just look at the results of their climate models and count hurricanes or droughts. Instead, they are turning to to find such extreme weather events in their models’ data.
For decades, modelers have relied on heuristics—mathematical definitions of an object of interest—to pinpoint extreme weather events. But heuristics cannot capture the complexity and variability of weather, which are very difficult to condense into a set of values and threshold conditions. Although many algorithms based on heuristics can detect atmospheric rivers (air currents that transport water from the tropics to the poles), for instance, their results tend to be unreliable, with large discrepancies between algorithms. Researchers hope to replace them with a new generation of
Computer Visionaries
To build these better algorithms, climate scientists have turned to a particular subset of
Deep learning was first developed in the 1970s in the field of computer vision, with the goal of training computers to “see” or recognize certain objects in digital images.
“In the [19]80s, [19]90s, and 2000s, people kept coming up with heuristics for defining what makes a pedestrian, what makes a car, what makes a face, and so on and so forth,” said Prabhat, a computer scientist who leads big-data initiatives at the Lawrence Berkeley National Laboratory in California. “In the last 10 years it has been conclusively proved that
To test this approach, Prabhat and his colleagues attempted to train a
Experienced climate scientists have been “very convinced that the
Generating Training Data
Obtaining reliable training data is one of the main challenges for
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