Speaker 1: Bloomberg Audio Studios, podcasts, radio news.
Speaker 2: I'm Stephen Carroll and this is Here's Why, where we
Speaker 2: take one news story and explain it in just a
Speaker 2: few minutes with our experts here at Bloomberg.
Speaker 1: Severe weather is making its way east after deadly storms
Speaker 1: ravage the South.
Speaker 2: It's a fact that we will see due to the
Speaker 2: climate development, more events, more severe event The weather, as
Speaker 2: always has a huge impact. Most shopping is still done
Speaker 2: in person. I'm singlehandedly powering the gardening section of retail
Speaker 2: sales data. Frankly, it's the center of so many conversations,
Speaker 2: but also a key economic driver. A cold snap can
Speaker 2: drive up energy demand. Sunny days can send shoppers out
Speaker 2: to buy new clothes. Then there are extreme weather events
Speaker 2: that can damage infrastructure, disrupt supply chains, and spark mass
Speaker 2: of insurance claims. As weather gets more volatile in many
Speaker 2: parts of the world, knowing what's on the way is
Speaker 2: even more important. Technology has helped to improve the accuracy
Speaker 2: of forecasts. So what does the explosion and artificial intelligence
Speaker 2: mean for the science. Here's why AI is changing how
Speaker 2: we predict the weather. Our weather reporter Mary Hoy joins
Speaker 2: me now for more, Mary, before we get to the
Speaker 2: effect of AI. Can you just bring us up to speed.
Speaker 2: At how good have we gotten at being able to
Speaker 2: predict the weather?
Speaker 1: We've gotten very, very good. We've made incremental but tremendous
Speaker 1: progress in the past decades since the very first computer
Speaker 1: based weather forecast was made in nineteen fifty and that's
Speaker 1: all thanks to various technological and scientific advancements of scientists
Speaker 1: have made around the world. That's allowed us to divide
Speaker 1: the world into smaller grids and then also divide the
Speaker 1: air above us into more and more layers, and all
Speaker 1: of that means that we can look at the weather
Speaker 1: in more detail, which then increases our ability to see
Speaker 1: further into the future. So you know, a five day
Speaker 1: forecast now is as accurate as a three day forecast
Speaker 1: was in two thousand. That's about a day for every
Speaker 1: decade of technological and in scientific progress. So that's a lot
Speaker 1: of advancement. And as the weather gets more volatile and extreme,
Speaker 1: will be depending on this weather forecasting capabilities.
Speaker 2: Well, I'm interested in that point. Actually, the increase in
Speaker 2: volatile weather events seems to be something that we're observing
Speaker 2: at pace. How does that affect weather forecasting? Does it
Speaker 2: make it more difficult.
Speaker 1: So weather forecasting is just fundamentally tricky. The atmosphere is complicated,
Speaker 1: it's chaotic, it's uncertain, So every forecast has an element
Speaker 1: of uncertainty. And that's because of incomplete observations, so not
Speaker 1: being able to see exactly everything that's going on in
Speaker 1: the atmosphere, and also just approximations that weather models have
Speaker 1: to make. And now extreme weather makes that slightly trickier
Speaker 1: and even more challenging because extreme weather, I suppose by definition,
Speaker 1: they just have more variation variability, and they've also been
Speaker 1: relatively rare, so that means less data and fewer observations
Speaker 1: over time, and so we just understand these events less
Speaker 1: well and have done less research into them. And then
Speaker 1: these conditions can change really quickly. A hurricane, for example,
Speaker 1: given warmer waters can rapidly intensify, and that quick change
Speaker 1: can catch forecasters off guard. So all of that is
Speaker 1: making it slightly trickier, even though weather forecasting itself has
Speaker 1: never been an easy exercise.
Speaker 2: Well, let's get to artificial intelligence. Then, how are forecasters
Speaker 2: using AI? How can it help this process?
Speaker 1: There are lots of steps to creating a weather forecast,
Speaker 1: and AI can be applied to some of those steps,
Speaker 1: or maybe all of those steps. We can take the
Speaker 1: first step gathering realms and reams of data, because to
Speaker 1: forecast future weather, you need to know what the present
Speaker 1: weather's like, and that requires getting a lot of observations,
Speaker 1: whether that's temperatures or air pressure, humidity and the like.
Speaker 1: So AI can help by helping us gather more data
Speaker 1: from a quantified sense, but also more types of data.
Speaker 1: Whereas current weather models typically are restricted to meteorological observations,
Speaker 1: AI models can now allow us to expand it to
Speaker 1: say maintenance logs for power grids, or even news articles,
Speaker 1: just really gathering more and more information to inform models
Speaker 1: with to then create perhaps hopefully more accurate forecasts.
Speaker 2: How widespread is the use of AI and forecasting currently.
Speaker 1: So a key intergovernmental weather forecasting agency called the European
Speaker 1: Center for Medium Range Weather Forecasting or ECMWF. They've taken
Speaker 1: their AI model into operations earlier this year and they're
Speaker 1: running it side by side with their existing traditional physics
Speaker 1: based the model, and they're soon to launch another iteration
Speaker 1: of that AI model into operations too. So there's lots
Speaker 1: going on in this world. So the ECMWF. They're also
Speaker 1: running AI models from different providers, including China's Huawei the
Speaker 1: tech giant, Google's graph Cast model, Microsoft's Aurora model, and
Speaker 1: China's own Weather Bureau itself is also testing about a
Speaker 1: dozen AI weather models, so a lot of experimentation going on.
Speaker 1: There were probably going to see AI models and traditional
Speaker 1: weather models working in tandem in the years to come,
Speaker 1: rather than seeing AI can completely replace these traditional numerical
Speaker 1: weather model forecasting methods.
Speaker 2: Yeah, I'm curious if there are risks to using AI
Speaker 2: for weather forecasting. Is there things that technology might miss
Speaker 2: if we see a greater implementation of it.
Speaker 1: The way a lot of these AI models are trained
Speaker 1: right now is on historical climate data from decades and
Speaker 1: decades observations in analyzes. But the thing is that the
Speaker 1: past is never perfect predictor of the future, and so
Speaker 1: if we're about to see in a warming climate, more
Speaker 1: extreme weather that previously is not reflected in these historical
Speaker 1: data sets, then there is a risk that AI weather
Speaker 1: models will say underestimates maybe the intensity of a typhoon
Speaker 1: or a tropical cyclone, even as it improves the forecasts
Speaker 1: of a storm's track, for example. So there are some
Speaker 1: risks there, and it's definitely an open and ongoing feel
Speaker 1: of the research.
Speaker 2: So what's the next big development we should be watching
Speaker 2: out for in this area?
Speaker 1: This is less one single big development but rather trend
Speaker 1: and I'd be looking for kind of a shifting distribution
Speaker 1: right of roles in this whole global weather enterprise. Are
Speaker 1: public weather agencies which we've long depended on for weather forecasts,
Speaker 1: are they going to play a slightly different role now
Speaker 1: that private companies, tech firms and also small players increasingly
Speaker 1: jumping into this world of creating and providing weather forecasts
Speaker 1: at a more niche level. So it'll be interesting to
Speaker 1: see how that division of labor between the public forecasters
Speaker 1: and more private players will play out.
Speaker 2: Okay, Mary Hoy, our weather reporter. Thank you very much
Speaker 2: for joining us. For more explanations like this from our
Speaker 2: team of three thousand journalists and analysts around the world,
Speaker 2: go to Bloomberg dot com slash explainers. I'm Stephen Carroll.
Speaker 2: This is here's why. I'll be back next week with more.
Speaker 2: Thanks for listening.