Roadmap index

plumax — Roadmap & Architecture¶

Mathematical models for plume simulation, methane retrieval, source identification, and emission estimation.

This page is the index for the architecture roadmap. The detail for each tier lives in its own file so they can grow independently as design decisions land. The high-level overview (philosophy, tier table, principles) stays here; each tier page expands the math, module layout, validation strategy, and open questions.

The Data-Driven Modeling Cycle¶

Every tier in plumax follows the same loop:

┌─────────────────────────────────────────────────────────────────┐
│   (1) Simple Model                                              │
│       ↓                                                         │
│   (2) Model-Based Inference                                     │
│       ↓                                                         │
│   (3) Model Emulator          ← skip if model is cheap          │
│       ↓                                                         │
│   (4) Emulator-Based Inference                                  │
│       ↓                                                         │
│   (5) Amortized Inference (Predictor)                           │
│       ↓                                                         │
│   (6) Improve  ───────────────────────────────────────────────┐ │
│       ↑         upgrade model / data / emulator / posterior   │ │
│       └───────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

• Step 1 gives you a generative story — a known mathematical structure you can simulate from.

• Step 2 gives you ground truth inference — slow but exact, used to validate everything downstream.

• Step 3 makes Step 2 tractable at scale — replace the expensive forward model with a fast surrogate.

• Step 4 is Step 2 again, but now running in seconds instead of hours.

• Step 5 collapses the inference loop entirely — the predictor learns the posterior map directly.

• Step 6 closes the loop — every component is independently upgradable, with the previous step as ground truth.

Tier overview¶

The build order is roughly: Prerequisites → Tier I → RTM stack (parallel) → Tier II → Tier III → Tier IV → Tier V. RTM is independent of transport tier, so it can be developed in parallel by a different person without coordination cost. Tier V depends on at least Tier I being usable end-to-end (per-event posteriors are the input), but does not need Tiers II–IV — it can launch with Tier I posteriors and absorb richer ones later.

Architectural principles¶

Status snapshot (2026-04-29)¶

Module-level status is tracked per tier. Quick overview:

• Tier I — Gaussian: ✓ plume + puff forward models, ✓ MAP/MCMC inversion. Emulator + amortized predictor not yet started.

• Tier II — Lagrangian: ☐ not started. No gauss_flows-style trajectory module yet.

• Tier III — Eulerian FV: 🚧 les_fvm advection/diffusion/dynamics implemented; assimilation cost/control/solve scaffolding present. 4D-Var loop not wired end-to-end.

• RTM stack: 🚧 hapi_lut LUT generator + Beer–Lambert in place; radtran instrument/SRF/forward modules present; matched_filter detection pipeline in place. Optimal-estimation retrieval not wired; neural RTM not started.

• Tier IV — Coupled: ☐ not started. Awaiting Tier I + RTM completion before assembling the coupled forward.

• Tier V — Population & forecasting: 🚧 standalone methane_pod library is feature-complete (intensity catalog, POD catalog, paradox simulator, NUTS fitter, synthetic-data validation). Missing: cross-tier adapter that turns Tier I–IV per-event posteriors into TMTPP mark likelihoods; real-data CSV ingestion; multi-satellite fusion. See Tier V index.

The module references above (les_fvm, hapi_lut, methane_pod, …) live in upstream source repositories outside this journal.

See each tier page for module-level breakdown.