Determinism is dead, chaos reigns, and the night is long
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004 | 2025-09-22
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 that embraces chaos rather than striving to chart a single line of truth on a graph by generating dozens or even hundreds of slightly different predictions that together map a full range of possible outcomes.
We’re unpacking FGN, the latest variant of AIFS, and their differing approaches to the same challenge: how can we create a confident forecast of an assuredly uncertain future.
Featured Papers
- Skillful joint probabilistic weather forecasting from marginals, Alet et al.
- AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score, Lang et al.
Chapters
- 00:00 — Intro
- 01:38 — Abstract (Meet the paper!)
- 10:17 — Weather report & reading list
- 19:37 — A lil history (Newton, Blake, Goethe, & voices of dissent)
- 28:43 — Paper time!
Further Reading
- The Solace of Open Spaces by Gretel Ehrlich (especially “On Water”)
- North Woods by Daniel Mason
- Frederick by Leo Lionni
- Hyperion and The Fall of Hyperion by Dan Simmons
- Newton by William Blake
- But how do AI images and videos actually work? by WelchLabs and 3Blue1Brown (video)
- Chaos: Making a New Science by James Gleick
- “From chaos to clarity: Seasonal forecasts for confident risk management” with Marshall, Alden, and Phil Butcher from the HydroForecast team