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prism · methods and QA examples

Provider, product, coverage, units and source status have one canonical home: prism source reference. This page preserves the operational example, reviewed figure and source citations.

Quickstart

Get the stream (CubeDynamics grammar)

import cubedynamics as cd
from cubedynamics import pipe, verbs as v

cube = cd.load_prism_cube(
    variable="ppt",
    start="2020-01-01",
    end="2020-02-01",
    bbox=[-105.4, 39.8, -105.0, 40.2],
    freq="D",
)

pipe(cube) | v.mean(over="time") | v.plot()

Customizing the view

pipe(cube) | v.plot(camera={"eye": {"x": 2.2, "y": 1.6, "z": 1.3}})

Reviewed source-QA plot

PRISM minimum-temperature source QA with a spatial map and temporal summary

This is a checked-in rendering of a real, checksum-controlled PRISM extract, not synthetic example data. The accompanying QA checks provenance, CRS, time ordering, finite coverage, physical temperature bounds, minimum/maximum consistency, and overlap with the requested area of interest.

Reproduce the validation

Run the publication QA workflow from the repository root:

python scripts/run_source_qa.py

The command writes the figure and a machine-readable result to artifacts/source_qa/. See the Phase 1 source-QA report for the exact checks, evidence, and current limitations.

Citation

Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28(15), 2031–2064. https://doi.org/10.1002/joc.1688

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