How Artificial Intelligence Can Predict Storms and Cyclones
The employment of artificial intelligence in relation to predicting the weather has been long awaited by the masses. While humanity is indubitably quite accurate in meteorology predictions, claiming to be 95% accurate in a 1 day span by minitab, there is always error in longer-distance gaps and changes in the weather by natural disasters, including storms and cyclones. Therefore, artificial intelligence comes in to play to minimize this deficiency.
Researchers have been credited with developing an AI-based algorithm to detect cloud formations that lead to storms, hurricanes and cyclones. This algorithm has been proven successful in a study published by the journal IEEE Transactions on Geoscience and Remote Sensing. Effectively, the algorithm operates by creating a diagram based on the clouds formations, and forecasters use this diagram to recognize potential storms with greater accuracy and speed.
The framework employed for this algorithm program has its stems in Machine-Learning. This program, with immense sensitivity, observes the clouds and detects every single rotational movement they commit, using satellite images. These movements, without the aid of artificial intelligence, go unnoticed a majority of the time because the human eye and our current equipment is insufficient enough to detect them with dependability in real-time scenarios. Artificial Intelligence eliminates this inadequacy with great potential for the future of meteorology.
"The very best forecasting incorporates as much data as possible, there's so much to take in as the atmosphere is infinitely complex. By using the models and the data we have, we're taking a snapshot of the most complete look of the atmosphere," said Steve Wistar, Senior Forensic Meteorologist at AccuWeather in the US.
In the same study by the IEEE Transactions on Geoscience and Remote Sensing journal, 50,000 photographs of comma-shaped clouds were analyzed by this artificial intelligence algorithm. The reason these cloud-shaped clouds were chosen was because they commonly correlate with cyclone formations. The AI, in essence, had to memorize the structure and formation of these clouds to identify them when surrounded by other clouds of different types. Eventually, when looking at completely random assortments of clouds, AI was able to detect the same comma-shaped clouds within these assortments at an astonishing accuracy rate of 99%, at an average of 40 seconds per prediction.
"Because the 'comma-shaped' cloud is a visual indicator of severe weather events, our scheme can help meteorologists to forecast such events," said study lead author Rachel Zheng from Penn State University in the US.
In addition, this same model could assist researchers by pointing out the specific locations that meteorologists must focus on to identify any severe weather possibilities. By observing solely the areas in which comma-shaped clouds emerge, meteorologists can become much more quicker at making accurate weather predictions and offering humanity with the most significant weather reports.
With this research, it can be concluded that the previous difficulties in identifying and preparing for incoming heavy storms can be eliminated by the growth of artificial intelligence. It is likely that this AI algorithm will be commonly used by meteorologists around the world to eliminate the chance of an inaccuracies in weather reporting entirely. Albeit this goal may seem far-fetched, it is one shared by many, and will hopefully be accomplished in the future.