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Bias has a pretty well-known definition in the world of AI/ML programming. But today’s paper asks us to expand that definition and consider how cultural, organizational, and human forces can intersect with development. The authors of “Identifying and Categorizing Bias in AI/ML for Earth Sciences” a…
Machine learning dominates the conversation, but what will happen to the centuries-old physical equations that built our understanding of the atmosphere? How are the two approaches at odds? How might they coexist? Today’s paper, NeuralGCM, sheds light on how physics-based and AI approaches might b…
While staring into uncertainty might sound abyssal and frightening, ensemble models have proven (to us, at least) that this isn’t the case. In today’s episode, we’re exploring two papers with different approaches to ensemble forecasting. This "choir" approach to weather prediction is one …
Where does the end begin? Where does the end end? And what the HELL is happening in the middle? These are some of the questions we tackle in this episode as we explore Aardvark Weather, an end-to-end forecasting system. End-to-end models are super cool, and likely to be the future of weather forec…
How far into the future can we actually predict the weather? Typically, this question is rhetorical, a slight (or smite⚡) against meteorologists everywhere after someone’s wedding gets rained out. But in this inaugural episode of Weathering, we’re asking in earnest—what is the horizon for accurate …
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