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Interactive first-principles climate physics simulation with explainer

Interactive first-principles climate physics simulation with explainer

by crackalamoo·Apr 8, 2026·1 point·0 comments

AI Analysis

●●●BangerRabbit HoleEye CandyBig Brain

Layer-by-layer climate physics you can actually watch unfold in 3D.

Strengths
  • First-principles approach isolates each physics process so you see causal relationships.
  • Six months of development with manual physics review prevents LLM overfitting.
  • Interactive 3D globe visualization makes abstract climate concepts immediately tangible.
Weaknesses
  • AI-assisted development means physics accuracy depends on author's manual verification.
  • Educational niche limits audience compared to general-purpose visualization tools.
Category
Target Audience

Students, educators, and curious people wanting to understand climate physics

Similar To

PhET Simulations · Climate Reanalyzer · NASA Eyes

Post Description

A 3D visualizer of earth's climate in the browser. Introduces physics step by step so you can watch each process unfold as a piece of the overall climate.

I built this over 6 months, almost entirely with AI, mostly Opus 4.6 in Claude Code. SF weather made no sense to me (Barely any seasons? September is the warmest month?) and I wanted to understand it better myself. This is a polished version of the app I'd want for myself, adding physics layer by layer to isolate the impact of each piece, and using an LLM to analyze and explain the data.

The models know more about math, physics, and software than I do — but especially on the physics side, they have terrible intuition. Claude can "get the error relative to observations down to 4 °C" just fine, except it'll totally hack and overfit the physics along the way. Subagents to subjectively verify "the physics is sound, no overfitting" didn't really work either. So I had to review the physics code manually.

The entire model is first principles; no machine learning or using observed data at all, except fundamental constants like the radiation of the sun and an elevation map. But after a while, it started to feel like "machine learning in slow motion": instead of an ML model training its parameters, Claude and I were choosing parameters by hand. Some amount of tuning parameters (within a physical range of uncertainty) to match observations is inevitable.

The in-app LLM layer has a tool to evaluate arbitrary math expressions over the simulated data using an AST, which was also pretty fun to build.

Repo: https://github.com/crackalamoo/building-earth

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