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ERA5 SMI

ERA5 SMI

by leandroavl
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Python
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    TDQS

    A3.7/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool targets a distinct data source or processing step: inspect_netcdf for inspection, the fetch tools are differentiated by time resolution (hourly vs monthly mean) and data type (pressure levels, single levels, land soil moisture), and extract_point_timeseries is the only output-writing tool. There is no meaningful overlap between tool purposes.

    Naming Consistency4/5

    The naming follows a consistent snake_case verb_noun pattern, with fetch_era5_* clearly grouped by data type and time resolution. Minor deviation: tools without 'hourly' are implicitly monthly mean (e.g., fetch_era5_pressure_levels), which is not explicitly reflected in the name, and inspect/extract use generic verbs rather than the fetch_ prefix.

    Tool Count5/5

    Seven tools cover the full data-preparation workflow for the ERA5 SMI pipeline: inspection, downloading multiple ERA5 variants, and extracting point timeseries. The count is well-scoped, not bloated or sparse, and each tool has a clear role.

    Completeness4/5

    The tool surface covers the main data types (pressure levels, single levels, land soil moisture) in both hourly and monthly resolutions, plus inspection and extraction. Minor gaps: there is no tool for directly listing available variables or fetching ERA5-Land hourly soil moisture, but these are not critical given the stated SMI focus.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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