Imagine traveling to a resort several states away for much-deserved rest and relaxation. You get settled in your hotel room, turn on the television, and much to your surprise, you see – YOU!
You didn’t record the weather update. This is actually your first time visiting the area. But someone – or something – used your identity, put words in your mouth, threw some graphics behind you, and created a money-making opportunity.
But did you approve of that? Are you being compensated fairly? And was the weather forecast accurate?
If this seems far-fetched, artificial intelligence (AI) is already replacing talent in several industries. Pop star avatars like Miquela get millions of views on her AI-generated music videos. Even KFC’s late founder, Colonel Sanders, goes viral these days with a new look.
If you’re an overworked meteorologist consumed by concern that an avatar will soon replace you, Baron has your back. If you’re a corporate decision-maker overwhelmed by weather technology that feels too complicated, Baron also has your back.
The key to staying relevant and valued as a meteorologist in the AI world is leveraging its technology to improve your work efficiency and efficacy, thereby continually proving your worth to your audience and your employer.
Staying Balanced in a Brazen AI World
AI is a buzzword that's seemingly everywhere, from the voice assistants in our living rooms to complex algorithms driving business decisions. While the decision to embrace or reject it may have passed, we can still find a balance that benefits the meteorologist and minimizes technological vulnerabilities.
The weather industry is a vast community of scientists, researchers, accomplished communicators, and the world's most powerful computers. Despite the escalation in AI advancements, meteorology is still not an exact science. And it will likely be many years—if not decades—before unilateral trust is established in a machine for every weather situation or solution.
Recently, the AI hype has revolved chiefly around the generative type, which is now used to write emails, compose images, and create videos. While this subset of AI is new and attractive to a communicator, there is much more we can leverage from AI that supports the entire meteorological enterprise.
Several weather companies and governmental agencies have focused their AI efforts on long-range forecasts or climate modeling. Impressive strides have been made in the accuracy of large-scale features, such as ensemble forecasts of tropical cyclones. Advancements have also been revolutionary in the computing capacity of big data, leading to improvements in spatial and temporal resolution.
Baron’s mission in AI is to leverage machine learning (ML) and large language models (LLM) to reduce the cognitive burden on a meteorologist during short-term critical weather situations when identifying, forecasting, and communicating threats is most urgent. We can accomplish this by delivering smarter data, building better tools, and cultivating meaningful partnerships that keep humans at the helm while leveraging AI to save lives and protect assets. This has been the company’s mission for 35 years.
What Machine Learning Makes Easier
Small changes in complex meteorological processes can make weather less predictable. This is often referred to as the “butterfly effect” or chaos. Experienced forecasters can mitigate this deficiency by recalling how similar patterns behaved. But their memories and bandwidth are not infinite.
A machine can now be taught how to perform a task by learning and adapting to its environment without following explicit instructions. Machine learning permeates our everyday lives, such as when shows are suggested for us on Netflix or when our bank alerts us to potential fraud. Baron uses machine learning to improve real-time weather detection, provide more reliable short-term forecasts, and enhance a meteorologist’s presentation.
Baron ClearScanTM was developed in the past five years to provide cleaner radar imagery to its customers. It leverages machine learning technology to automatically remove non-precipitation radar returns, adapting to changes in the landscape from new interference or other anomalous phenomena.
The Baron Flash Flood Risk is an example of how machine learning can improve the short-term detection of flash flooding. A historical dataset of soil-moisture evolution and runoff is matched with recent rainfall rates and short-term modeling to pinpoint where an extreme event is likely. AI and ML are also used to increase the speed and accuracy of short-term radar reflectivity and severe weather forecasts.
Hand Tracking from Baron takes storm tracking to a whole new level. It uses machine learning and computer vision technologies to improve accuracy and responsiveness at the Chroma key. When combined with Baron’s automated storm tracks and alerts, it empowers broadcasters with more confidence when their expertise matters the most.
These are just a few examples of how advancements in AI can be leveraged behind the scenes to improve the everyday work lives of meteorologists and weather decision-makers.
The Future of Faster Detection and Dissemination
The pressure on a meteorologist to perform when lives are at stake is intense. AI can reduce the burden of the more complicated tasks behind the scenes and allow the communicator to focus more on their delivery. A good performance won’t cut it if the content behind the delivery is inaccurate, late, or even misleading.
Situational awareness is critical to making the best weather decision for your audience when seconds matter the most. AI and LLMs can detect potential threats faster, simulate what could happen sooner, and suggest words or phrases to communicate the risk more effectively. This would lessen the heavy lifting of technology and give back some of the meteorologist’s time to focus more on communicating.
LLMs can also simultaneously reduce the “time to market” of critical weather information through multiple platforms. For example, social media posts for weather alerts can be auto-generated with carefully crafted words that communicate the impacts to the user, not just the semantics. Content can also be automatically translated into multiple languages to reach wider audiences in real-time.
While AI might not be essential to a meteorologist’s on-air life just yet, it undeniably holds a pivotal place in the future of forecasting. As with any powerful tool, understanding its risks and benefits is critical to harnessing its potential. Baron will collaborate with industries, governments, and employers to establish ethical frameworks and robust AI solutions. By doing so, we can guide future development in a way that uplifts meteorologists rather than undermines them.
