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Interactive first-principles climate physics simulation with explainer

Interactive first-principles climate physics simulation with explainer

by crackalamoo·May 22, 2026·2 points·0 comments

AI Analysis

●●●BangerRabbit HoleBig BrainEye Candy

Layer-by-layer physics simulation teaches climate science better than any textbook video.

Strengths
  • Step-by-step physics layering isolates variables like albedo and greenhouse effects visually.
  • LLM-assisted code generation balanced with manual physics review ensures scientific accuracy.
  • Interactive 3D globe renders complex atmospheric data in an immediately understandable way.
Weaknesses
  • Six-month build time suggests high maintenance burden for keeping simulations accurate.
  • Niche educational focus limits viral potential outside science and dev communities.
Category
Target Audience

Students, educators, and curious developers interested in climate science

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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