The science of meteorology has taken tremendous strides in the past two decades and even allow … [+]
The science of meteorology has taken tremendous strides in the past two decades thanks to a confluence of several inputs: improved computing power; better modeling of data; more observational data points ranging from the device in your hand to the satellites orbiting earth; and advanced data science applications. As recently as two decades ago, providing an accurate forecast three to four days out was considered innovative. Today a five-day forecast is accurate about 80 percent of the time. Most weather experts are predicting even more extended accuracy by 2030 with the application of artificial intelligence for numerical weather prediction output. But beyond improving accuracy, here are a few other forecasting trends to watch in 2023.
Hyper-relevant Forecasting
Just like other sets of analytics have become more tailored, or localized to the user, weather intelligence is bringing forecast relevancy to an individual organization or entity. A business can determine which risks are most significant to their operations, such as wind gusts, lightning, heavy rains, and ice accretion, and then be alerted when those risk thresholds are met. While there’s growing use among utilities, municipalities and other infrastructure decision makers, hyper-relevant forecasting is growing in other sectors. For example, by combining weather data with purchasing trends and consumer demand data, one grocery chain learned that even a small change in temperature can result in a significant shift in what people buy. The store improved its revenues by modeling this impact and managing inventory accordingly. Even sports teams are applying hyper-relevant forecasting for everything from daily stadium operations to food and beverage decisions and strategic game plays.
Tech-trained Meteorologists
In today’s business environment, it takes more than meteorological skills to report, forecast, and analyze weather data in a way that can be processed and integrated by multiple stakeholders. The weather industry employs a wide variety of technical positions including software engineers, data scientists, app developers and others to ingest, process and manage and create detailed, complex weather information and making it relevant and useful for critical decisions. Now, meteorologists are realizing the benefits of having those data and computer science skills themselves. In fact, many college programs are helping meteorology students gain valuable data science skills through additional coursework. The University of Washington now offers a data science option that gives atmospheric science students skills like computer programming, data management, visualization and machine learning and other universities are likely to follow. While the U.S. market is becoming saturated with operational forecasters, having advanced computer and data science skills will broaden opportunities for meteorologists as technology use expands.
Risk Communication
Forecasting is no longer just about what the weather will do. Meteorologists are also increasingly asked to communicate the impact of the weather. Going back to the hyper-relevant forecasting, risk communicators are meteorologists with deep industry expertise that enables them to identify weather risks and make plans to prevent or mitigate weather risks tailored to a specific industry or organization. This is becoming a more valuable role for businesses with the average costs associated with extreme weather events in the United States steadily increasing and having costly impacts on infrastructure, facilities, human livelihoods, and health. The increase in extreme weather events makes it even more imperative for risk communicators to be a part of weather response planning for businesses and the communication starts well before an event. The process typically involves assessing risks, modeling, and analyzing weather risks in real-time, helping guide communications and response preparedness before and during a weather event, and delivering analysis during post-event planning sessions.
Joining Weather Data with Industry Data Sets
With more than three-quarters of global organizations planning to increase or maintain investments in big data over the next two to three years, there are opportunities to increase revenue and reduce costs using weather data with additional insights. The convergence of data science and weather forecasting is happening across many industries, but shipping is a great example of this convergence at work. It’s important for shipping companies to compute optimal shipping routes for safety, fuel efficiency and ultimately to maximize profitability. To identify the optimal routes, it starts with taking forecast technology and data, and then merging it with customer-specific data to provide tailored insights, like a ship’s position or type, vessel efficiency, fuel consumption and carbon emissions to gain a more complete assessment of the ship’s voyage.
Meteorologists have always played an important role in keeping the public, businesses and municipalities prepared for changing weather conditions. But with the increase in computing power, applied data science techniques and the joining of weather data with other industry data sets, I believe we are entering a new era for the science. My predictions for 2023 are just the tip of many innovative things evolving in meteorology.
