When we talk about “weather data,” we’re often dealing with big data—a large volume of information that spans huge amounts of time and geographical areas. As valuable as this data is for forecasting weather, the tricky bit is figuring out how to utilize it without over-saturating or over-complicating things. This is where AI and machine learning come in, and they may be revolutionary to the way we understand weather.
Machine learning is a somewhat broad concept, but in basic terms, it consists of a system that learns from examples, weighing each input and deducing how they work together. Ideally, the more examples you feed it, the better it gets in giving an accurate output.
In the weather forecasting services industry, machine learning has begun to play a role, and StormGeo has become a leading part of this development. For example, we are starting to apply machine learning to a wide range of challenges, specifically within wind prediction. Here on the R&D team at StormGeo, we’re working on systems that, after feeding them years of historical wind data for an area, can calculate how much power will be generated from that wind, if any damage will be caused and even further—the costs expected from that damage. For many industries, wind is the number one cause of damage. Having ten days to prepare for high winds can mean the difference between major and minor repairs.
Machine learning can assist with other forecasts as well, including temperature, wave height and precipitation. Someday, we hope to employ AI for radar imagery—detecting storm centers and high precipitation all over the world. While (human) meteorologists have typically held this responsibility, we’re now seeing that no matter how knowledgeable or experienced a meteorologist is, he or she can’t beat the machine—there are just too many variables to take into account.
Maintaining consistency can also be a challenge for humans. Given the same problem, two different meteorologists may give different answers. With no room for a subjective human experience, machines won’t do that. An AI system will put out consistent (albeit not necessarily correct) predictions every time.
If this is all sounding a little to ‘robot apocalypse’ to you, don’t worry—there is room for us to coexist. While we don’t necessarily need a meteorologist to calculate the numbers, we do still need one to explain the weather data—telling the story of what it all means. Together with AI, our goal is to be able to tell our clients or the general public what the weather will mean for them. For example, rather than telling a shipping vessel what the wave height will be, we can calculate what that wave will do to their specific vessel. Put simply, AI removes a level of human analysis and knowledge needed to make sense of the numbers.
AI is constantly changing; continuously being honed and applied to different industries. It’s used in self-driving cars, education, medical diagnoses, and now, weather forecasting. Over the past few years, the amount of computing power has drastically increased, enabling our R&D team to actually employ these techniques in StormGeo services. As a Data Scientist, it’s an exciting time to be riding the first waves of influence AI is having on the weather industry.