Dust storms are a major ecological concern and have a significant impact on the climate. They pose a health hazard and can cause respiratory problems. The particles of dust travel freely from country to country and continent to continent and can spread pathogens, contributing to the outbreak of pandemics. However, an early warning for waves of dust could prevent vulnerable populations from respiratory problems and save crops from destruction.
A study by Dr. Ron Sarafian, Dori Nissenbaum, and Prof. Yinon Rudich from the Earth and Planetary Sciences Department at the Weizmann Institute of Science brings a breakthrough in dust-storm forecasting. The study, published in npj Climate and Atmospheric Science, was written in collaboration with Dr. Shira Raveh-Rubin, also from the same department at Weizmann.
The researchers hoped to use knowledge garnered in the field of computer vision to predict when, where and how badly dust storms will strike. The researchers thought that an artificial neural network would be able to “learn” the patterns governing the spread of storms, just as these networks have learned to recognize videos of various animals or objects. However, their hopes were only partially realized.
A regular image is comprised of just three primary colors, with a fair amount of overlap between them. Meteorological “images,” however, are made up of no fewer than 60 variables: temperature data, humidity, windspeed and so on. While computerized vision systems rely on machine learning based on archives of millions of images, there were precious few images available for an artificial neural network tasked with identifying dust storms: Israeli researchers have at their disposal just 60,000 of these meteorological “movies,” after collecting detailed data from satellites and ground stations for around two decades.
To their surprise, the researchers discovered that forecasting could be improved by making life harder for the artificial neural network. They tasked the network not only with learning when a dust storm was expected to reach a certain point but also to handle an auxiliary problem: keeping track of the much larger area in which the dust is dispersed.
Using this approach, the network had access to a much larger collection of data, from which it could also learn about the physical and meteorological circumstances by which dust is spread. The researchers showed that during the dust-heavy winter and spring, they could successfully forecast more than 80 percent of dust storms 24 hours ahead of time, and around 70 percent, 48 hours ahead of time.
Most of the incidents that the system did not predict were storms that developed rapidly in a localized area, which makes it difficult to collect regional data that can help predict them. The network trained on data from Israel can, with a few adjustments, forecast dust storms elsewhere in the Middle East and even across the world. Moreover, they have created an architecture that could help predict other rare events that are linked to meteorological data, such as extreme rainfall or flash floods.
The breakthrough in dust-storm forecasting is essential to protect vulnerable populations from respiratory problems and save crops from destruction. Dust storms are a significant ecological concern, and they have a significant impact on the climate. The researchers have shown that using an artificial neural network that tasks the network with learning when a dust storm was expected to reach a certain point and keeping track of the much larger area in which the dust is dispersed can help predict dust storms.