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Processing — vector → raster interpolation and in-house WPS

Goal: turn point observations into continuous fields. Two very different situations, two answers:

  • dense networks (SYNOP, METAR): an in-house IDW process, exposed both as a WPS process and as an SLD rendering transformation (colored surface computed on the fly from the observation layer);
  • sparse, badly distributed networks (TEMP, AMDAR — a handful of stations, one vertical level at a time): a comparative study on a known synthetic field, concluded by ADR-0013.

Artifacts: poc/08-processing/ (study, tests) and poc/02-geoserver-image/meteo-wps/ (process module).

In-house process module (meteo-wps)

  • meteo:IDWInterpolation — Inverse Distance Weighting of a point attribute onto a grid (data, valueAttr, power, plus the rendering-transformation contract outputBBOX/outputWidth/outputHeight).
  • Registered through the standard GeoTools SPI (META-INF/services/org.geotools.process.ProcessFactory → a factory extending AnnotatedBeanProcessFactory under the meteo namespace).
  • The style obs-idw-temperature (managed as code, like every style) invokes the process as a <Transformation> and paints the result with a kelvin color ramp; it is attached as an additional style of obs_latest_air_temperature (EXTRA_LAYER_STYLES).
GetMap …&layers=meteo:obs_latest_air_temperature&styles=obs-idw-temperature

IDW surface from the latest observations

Sparse-network study (TEMP/AMDAR case)

Protocol (study/compare.py, runnable in a plain Python container): a known synthetic truth field is sampled at 5 stations clustered in one corner; four reconstructions are scored against the truth (RMSE, same color scale):

Method RMSE (K)
IDW p=2 4.40
Barnes 2-pass 4.93
RBF thin-plate 6.77
model first guess + corrections 1.36
(raw first guess, for reference) 2.37

Truth

IDW

Barnes

RBF

First guess + corrections

Readings: pure spatial methods hallucinate everywhere the stations are not — and RBF, often praised for sparse data, extrapolates worst with clustered stations. Starting from an (imperfect) model field and correcting it with the observed innovations beats everything by a wide margin — and even improves on the raw model. Hence ADR-0013.

Verification (tests poc/08-processing/tests/)

01 The process is described and listed by WPS:

DescribeProcess

02 The rendering transformation produces the colored surface on two pods (and the point rendering stays available as the default style):

Rendering

03 Process, style and attachment survive a pod replacement:

Restart

Deviations and pitfalls observed (rendering transformations)

  • Process factories are discovered through the GeoTools SPI, not through Spring beans: ship a META-INF/services/org.geotools.process.ProcessFactory entry (a bean alone is silently ignored).
  • AnnotatedBeanProcessFactory names processes after the class name minus "Process" (IDWInterpolationProcess → meteo:IDWInterpolation).
  • Implement invertQuery — without it the renderer pushes an unbounded envelope down to PostGIS (POLYGON((-Infinity … parse error).
  • In invertQuery, request all properties (setPropertyNames(null)): the renderer only fetches attributes referenced by symbolizers, and the process's value attribute is a literal it knows nothing about — the symptom is an empty collection while the plain layer renders fine.
  • IDW is global: the data query deliberately keeps all features (documented performance caveat for very large networks).