ADR-0013 — Sparse upper-air interpolation: model first guess + observed corrections¶
Date: 2026-09-02 · Status: accepted (backed by the prototype-8 study)
Context¶
Upper-air observations (TEMP soundings, AMDAR) are few and badly distributed; maps are displayed one vertical level at a time. Pure spatial interpolation must invent values over huge empty areas. The platform already ingests the corresponding model fields (sub-project 5).
Decision¶
For sparse networks, the analysis of choice is model first guess + corrections (successive-correction / optimal-interpolation family): take the model field at the requested level and validity, compute the innovations (observation − first guess at the stations), spread them spatially (IDW or Barnes) and add them to the field. Measured on a known truth with 5 clustered stations: RMSE 1.36 K versus 4.40 (IDW), 4.93 (Barnes), 6.77 (RBF) — and 2.37 for the raw model field.
For dense networks (SYNOP, METAR), plain IDW is adequate and is provided
as the in-house meteo:IDWInterpolation process / rendering transformation.
Rejected alternatives¶
| Alternative | Why rejected |
|---|---|
| RBF thin-plate as the sparse default | Worst extrapolation of the panel with clustered stations (RMSE 6.77 K) despite its reputation on sparse data |
| Barnes with a wide radius | Honest but flat far from data (4.93 K); no use of known physics |
| Ordinary kriging | A variogram cannot be estimated from a handful of stations |
| Observations-only display (no interpolation) | Remains available (point styles), but does not answer the "field" need |
Consequences¶
- Production implementation = a process combining a coverage (first guess from
the mosaics) and the observation features — natural follow-up of the
meteo-wpsmodule; recorded in the building blocks as future work. - The study script and metrics are versioned (
poc/08-processing/study/) and rerunnable to re-evaluate with other station layouts.