Using deep learning to predict imminent precipitations

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Enter information. Credit: Bakkay et al.

Deep studying fashions have proved to be very efficient for analyzing giant quantities of knowledge and precisely predicting future occasions. This makes them advantageous for a variety of purposes, together with climate forecasting.

Whereas meteorologists can now predict basic climate tendencies for the following two to 3 days pretty properly, climate change has led to an increase in surprising extreme weather events, together with thunderstorms, hailstorms, and hurricanes. Precisely predicting these sudden meteorological occasions a couple of hours upfront might assist to organize for them, probably limiting their impression and hostile penalties.

Researchers at IRT AESE Saint Exupéry and Météo-France have just lately developed three deep neural networks to foretell impending precipitations. These community, launched in a paper pre-published on arXiv, might permit meteorologists, governments, sport occasion organizers, and different organizations to foretell the incidence of storms, hurricanes, and different excessive climate occasions one to 6 hours upfront.

“We propose the use of three popular deep learning models (U-net, ConvLSTM and SVG-LP) trained on two-dimensional precipitation maps for precipitation nowcasting,” the researchers wrote of their paper. “We also proposed an algorithm for patch extraction to obtain high resolution precipitation maps.”

Right now, most long-term climate forecasts depend on numerical models that may simulate atmospheric physics processes utilizing photographs of the sky, radar information, and different accessible atmospheric information. Whereas these strategies can predict precipitations with good accuracy, they typically want to hold out intensive computations and thus take a very long time to make predictions. Consequently, these strategies typically don’t carry out equally properly on precipitation nowcasting, or the prediction of imminent precipitations.

Using deep learning to predict imminent precipitations
Two examples of precipitation predictions. High row: Floor fact; second row: U-net mannequin; third row: ConvLSTM mannequin; backside row: SVG-LP mannequin. The outcomes for the community signify 6 outputs from the mannequin. Credit: Bakkay et al.

The important thing goal of the latest work by Mohamed Chafik Bakkay and his colleagues at IRT AESE Saint Exupéry and Météo-France was to develop deep neural networks that might sort out precipitation nowcasting extra successfully than numerical climate forecasting fashions. Of their paper, they introduced three completely different fashions, specifically a U-net, a ConvLSTM and an SVG-LP community.

The three networks had been educated on a dataset containing 20,352 high-resolution photographs captured by Météo-France utilizing radar echo expertise between 2017 and 2018. These photographs coated an space of roughly 1000 x 1000 km2 in France.

As immediately feeding the high-resolution precipitation maps to the deep neural networks would saturate a pc’s GPU, the researchers additionally developed a patch extraction algorithm that may partition them into 256 x 256 patches. As a substitute of predicting precipitations for the entire maps, the networks can then study to make predictions about these particular patches. Lastly, in addition they developed a loss operate algorithm that improves the standard of the photographs processed by the neural networks, making them much less blurry.

Bakkay and his colleagues evaluated the efficiency of all of the three fashions they developed in a sequence of checks, evaluating the standard of the reconstructions they produced and the accuracy of their predictions. They discovered that whereas all three fashions captured the evolution of precipitation fields properly, the U-Web mannequin, which is a convolutional neural community (CNN) structure, carried out higher than the opposite two fashions.

“The CNN-based method outperforms the RNN-based models,” the researchers wrote of their paper. “It is able to generate high value of precipitation and it can predict the future rainfall contour more accurately. Also, ConvLSTM outperforms SVG-LP, but it tends to blur later frames.”

Sooner or later, the U-Web structure developed by this workforce of researchers could possibly be used to develop simpler instruments to foretell imminent precipitations and rainstorms. As well as, their work might encourage different groups to develop related fashions to foretell excessive climate occasions.


Google claims its ‘nowcast’ short-term weather predictions are more accurate than advanced models


Extra info:
Mohamed Chafik Bakkay, Precipitation nowcasting utilizing deep neural community. arXiv:2203.13263v1 [cs.LG], arxiv.org/abs/2203.13263

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