atlas14-spatial-variance
Analyze spatial variability of NOAA Atlas 14 precipitation frequency estimates within HEC-RAS model domains using intelligent extent-based downloading. Helps determine whether uniform rainfall assumptions are appropriate for rain-on-grid modeling by calculating min/max/mean/range statistics within 2D flow areas or project extents. Uses NOAA CONUS NetCDF with HTTP byte-range requests for 99.9% data reduction compared to traditional state-level ZIP downloads. Primary sources: - ras_commander/precip/CLAUDE.md (lines 118-629) - Complete workflows - ras_commander/precip/Atlas14Grid.py - API referen
npx skills add majiayu000/claude-skill-registry --skill atlas14-spatial-variance-gpt-cmdr-ras-commander-31a1410a --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Atlas 14 Spatial Variance Analysis **User-invocable skill for assessing precipitation spatial variability in HEC-RAS models** ## Primary Sources (Read These First) **Complete Workflows** (lines 540-629): - `ras_commander/precip/CLAUDE.md` - Lines 118-197: Module documentation (Atlas14Grid, Atlas14Variance) - Lines 540-629: Complete Atlas 14 Grid Workflow (4 steps) - Lines 522-538: Performance metrics **API Reference**: - `ras_commander/precip/Atlas14Grid.py` (404 lines) - `get_pfe_from_project()` - Main entry point for HEC-RAS integration - `get_pfe_for_bounds()` - Direct bounding box query - `get_point_pfe()` - Single point lookup - `ras_commander/precip/Atlas14Variance.py` (322 lines) - `analyze()` - Full variance analysis - `analyze_quick()` - Rapid assessment (100-yr, 24-hr) - `is_uniform_rainfall_appropriate()` - Decision support - `generate_report()` - Export with plots **Working Example**: - `examples/725_atlas14_spatial_variance.ipynb` - Direct bounds queries - Point lookups - HEC-RAS project integration - Visualization **Quick Reference**: - `.claude/rules/hec-ras/precipitation.md` (lines 104-126) --- ## Quick Reference ### Typical Workflow (3 Steps) ```python from ras_c
- Primary Sources (Read These First)
- Quick Reference
- Typical Workflow (3 Steps)
- Direct Grid Access
- Point Query (No Project)
- When to Use This Skill
- Use Atlas14Variance When:
- Decision Criteria
- Common Workflows
- Workflow 1: Quick Assessment
- Workflow 2: Full Analysis
- Workflow 3: Report Generation
- Workflow 4: Custom Grid Analysis
- Key Parameters
pip install ras-commander # All required deps included pip install fsspec>=2023.0.0 or pip install --upgrade ras-commander
What does the atlas14-spatial-variance skill do?
Analyze spatial variability of NOAA Atlas 14 precipitation frequency estimates within HEC-RAS model domains using intelligent extent-based downloading. Helps determine whether uniform rainfall assumptions are appropriate for rain-on-grid modeling by calculating min/max/mean/range statistics within 2D flow areas or project extents. Uses NOAA CONUS NetCDF with HTTP byte-range requests for 99.9% data reduction compared to traditional state-level ZIP downloads. Primary sources: - ras_commander/precip/CLAUDE.md (lines 118-629) - Complete workflows - ras_commander/precip/Atlas14Grid.py - API referen
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill atlas14-spatial-variance-gpt-cmdr-ras-commander-31a1410a --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.