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Listen "Model Behavior ≠ Meteorological Behavior: What “Weird Storms” Teach About Predictive Forecasting"
Episode Synopsis
In this episode, we plunge headfirst into calamity from a stormy weather event. Using everything from wandering caribou herds to turbulent typhoons, we delve into how scientists truly forecast the atmosphere and why that process is more chaotic than my sleek weather apps being refreshed in a frenzy.
I explain why contemporary forecasts depend on ensembles—multiple parallel model runs that chart a comprehensive range of possibilities—rather than a singular, overly confident declaration of “it will snow tomorrow.” The episode is also a peek backward at the 2019 Amman “snowstorm that never happened,” when a forecast came to nothing except a downpour of disappointment, closed shops, and some dip in optimism regarding our institutions as a whole. That kicks off a discussion about forecasting and image repair strategies. For image repairs, I am getting into the specifics of crisis communication, looking at what organizations do when a forecast or a quarterly projection or an algorthimic model goes sour. I look at why the reason saying “we meant well” fails to qualify it as an appeal, and why accepting responsibility acknowledging uncertainty is in fact a strength. It’s a matter of building credibility.
I will compare physics-based forecasting techniques in tandem with most recent AI systems, asking why the AI systems fail when it comes to uncommon or exceptionally severe storms—particularly typhoons that make unexpected U-turns as if they left something behind. We dive into the reasons behind AI's tendency to create a misleading aura of confidence, and how that deception can turn perilous when crucial real-world choices: evacuations, closures, and disaster preparedness, all hinge on it.
If you’ve ever questioned the discrepancies in weather predictions, the lack of trust that result from faulty projections, or how extreme storms continually put human beings and technology in a quandary, this segment points out the whole scope of atmospheric ambiguity, with confidence intervals, institutional anxieties, and even the delightful bakery-themed bread runs.
I explain why contemporary forecasts depend on ensembles—multiple parallel model runs that chart a comprehensive range of possibilities—rather than a singular, overly confident declaration of “it will snow tomorrow.” The episode is also a peek backward at the 2019 Amman “snowstorm that never happened,” when a forecast came to nothing except a downpour of disappointment, closed shops, and some dip in optimism regarding our institutions as a whole. That kicks off a discussion about forecasting and image repair strategies. For image repairs, I am getting into the specifics of crisis communication, looking at what organizations do when a forecast or a quarterly projection or an algorthimic model goes sour. I look at why the reason saying “we meant well” fails to qualify it as an appeal, and why accepting responsibility acknowledging uncertainty is in fact a strength. It’s a matter of building credibility.
I will compare physics-based forecasting techniques in tandem with most recent AI systems, asking why the AI systems fail when it comes to uncommon or exceptionally severe storms—particularly typhoons that make unexpected U-turns as if they left something behind. We dive into the reasons behind AI's tendency to create a misleading aura of confidence, and how that deception can turn perilous when crucial real-world choices: evacuations, closures, and disaster preparedness, all hinge on it.
If you’ve ever questioned the discrepancies in weather predictions, the lack of trust that result from faulty projections, or how extreme storms continually put human beings and technology in a quandary, this segment points out the whole scope of atmospheric ambiguity, with confidence intervals, institutional anxieties, and even the delightful bakery-themed bread runs.
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