Model Evaluation Tools — Analysis Lab · consolidated app gallery
Nine self-contained prototypes for exploring, visualizing, and interpreting
MET-style verification output — seven views, the STAT Ingest bridge that
parses real MET output, and the MET Pipeline Explorer that recomputes MET’s
verification live from real data and checks it against MET’s own .stat.
All run the same shared verification math
(lib/met-stats.mjs), are dependency-free, and work fully offline.
dist/<name>.html — it is fully inlined and opens straight from file://, no server, no network.apps/<name>/index.html versions are ES-module apps and must be served over HTTP (browsers block file:// module imports). From the worktree root run
python3 -m http.server 8000 then open http://127.0.0.1:8000/apps/<name>/index.html (or this gallery at http://127.0.0.1:8000/index.html).Documented MET verification math run entirely in the browser: live 2×2 contingency table, full categorical + continuous suites, a per-stat MET-fidelity formula table, a SQL-like query panel, and drag-drop CSV.
Direct-manipulation explorer with explicit Metric / Dimension / Filter / Facet vocabulary driving three cross-filtered linked views (brushable line plot, sortable table, faceted small multiples). Aggregates the correct ratio-of-sums way.
A gallery of four interactive verification visuals: a Performance Diagram (CSI contours + bias rays), a Reliability + Sharpness panel with Brier decomposition, a draggable threshold scrubber, and MODE-style object verification.
A cohesive 4-step diagnostic journey (Overview → Error vs lead → Categorical skill → Reliability) with a per-view provenance panel, shareable URL-hash sessions, and a deterministic rule-based narrator grounded in the on-screen numbers.
A pannable/zoomable Canvas error field over a synthetic gridded domain with forecast/obs/error layers, named region masks, per-mask continuous + categorical MET stats, hover readout, and a brush box. Shows ratio-of-sums vs mean-of-ratios on screen.
Four coherent ensemble diagnostics over one synthetic dataset — rank (Talagrand) histogram, spread-skill, probabilistic reliability + sharpness + Brier decomposition, and CRPS — all driven by a single under→calibrated→over dispersion knob.
An orbitable, tiltable 2.5D Canvas height-field rendering a verification metric over a lead × threshold × region cube, with scrubbable slicing planes, a synced 2D slice panel, hover-to-raw-counts, and a 12-metric live switcher.
.stat ingestion
The keystone parser: header-driven, version-tolerant ingestion of real MET .stat / per-type .txt / MODE output (CTC, CTS, SL1L2, CNT, PCT, PSTD, PRC, RHIST, ECNT, ORANK) feeding the shared lib. Paste or drop a real file; parsed PCT bins reproduce the paired PSTD Brier decomposition exactly. Ingested strings rendered injection-safe.
Walks the grid_stat/point_stat workflow stage by stage, flagging which run in the browser, which need an offline pre-encode (GRIB2/prepBUFR decode), and which stay server-side. The browser-computable stages run live on a real AIGFS-vs-URMA case — threshold → 2×2 contingency → partial sums → CNT/CTS scores → FSS — each checked against MET’s own .stat (counts bit-identical). Built for MET users to see exactly what’s portable to the client.