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Deep multi-task learning for early warnings of dust events implemented for the Middle East

Earth Sciences

Deep multi-task learning for early warnings of dust events implemented for the Middle East

R. Sarafian, D. Nissenbaum, et al.

This groundbreaking study by Ron Sarafian, Dori Nissenbaum, Shira Raveh-Rubin, Vikyhat Agrawal, and Yinon Rudich introduces a deep multi-task learning model for forecasting dust events in Israel, achieving a remarkable 76% detection rate a full day in advance. Dive into the analysis of local and regional PM₁₀ dynamics and uncover the critical meteorological factors driving dust occurrences.

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Playback language: English
Abstract
This paper proposes a deep multi-task learning approach for forecasting dust events in the Middle East, focusing on Israel. The model simultaneously predicts local PM₁₀ (primary task) and regional PM₁₀ (auxiliary task) using 18 years of meteorological data. Twenty-four hours before an event, the model detects 76% of events, with higher accuracy for winter and spring. Analysis indicates local dynamics drive misclassifications, highlighting the predictive skill of regional meteorology. Interpretability methods reveal lower-tropospheric winds and Aerosol Optical Depth as key features governing dust events.
Publisher
npj Climate and Atmospheric Science
Published On
Mar 23, 2023
Authors
Ron Sarafian, Dori Nissenbaum, Shira Raveh-Rubin, Vikyhat Agrawal, Yinon Rudich
Tags
dust events
PM₁₀ forecasting
multi-task learning
meteorological data
Middle East

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