precip_analyze_atlas14-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 precip-analyze-atlas14-variance-gpt-cmdr-ras-commander --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.
What it does
Analyzes spatial variability of NOAA Atlas 14 precipitation estimates within HEC-RAS model domains using intelligent extent-based downloading. It 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. It uses NOAA CONUS NetCDF with HTTP byte-range requests to reduce data transfer. The skill exposes workflows for quick variance checks, full variance analysis, and report generation, plus grid and point access helpers.
How it works
The skill's documentation describes these main components:
- Quick check: Atlas14Variance.analyze_quick(project.g01.hdf) to obtain a statistics dict, notably range_pct.
- Full analysis: Atlas14Variance.analyze(geom_hdf=..., durations=[...], return_periods=[...], extent_source=..., variance_denominator=..., output_dir=...). It computes per-event variance across specified durations and return periods.
- Uniform rainfall decision: Atlas14Variance.is_uniform_rainfall_appropriate(results) returns a message/decision based on computed variance.
- Report generation: Atlas14Variance.generate_report(results_df=..., output_dir=..., project_name=..., include_plots=True) creates a set of files (e.g., variance_statistics.csv, variance_summary.csv, variance_by_duration.png, variance_heatmap.png).
- Grid and point access: Atlas14Grid.get_pfe_from_project(...) fetches grid-based PFE data for a given extent_source (2d_flow_area or project_extent) and returns arrays; Atlas14Grid.get_point_pfe(...) performs a single location lookup.
- Data source and structure: NOAA CONUS NetCDF at the specified URL provides multi-duration and multi-return-period data with a scale factor of 0.01 to inches, supporting HTTP byte-range requests for efficient subsetting.
When to use it
Use Atlas14Variance when you need to assess whether uniform rainfall is appropriate for rain-on-grid modeling, especially for large model domains where variance is likely, or when performing multi-event analysis and engineering reporting.
What it can touch
- The skill uses Atlas14Grid and Atlas14Variance APIs, including functions and data structures that interact with NOAA CONUS NetCDF data over HTTP. It requires internet access to NOAA servers for grid and point data retrieval. It may produce outputs like CSVs and PNG plots via generate_report().
Caveats
- CONUS coverage only: data do not cover Hawaii, Alaska, Puerto Rico, or offshore areas. For non-CONUS locations, alternatives like StormGenerator are referenced.
- Internet required: no offline mode; caching is in memory and cleared via Atlas14Grid.clear_cache().
- Return periods mapping uses a specific indexing (100-year event is index 5 in the ari array).
- Dependencies and installation notes: requires ras-commander and associated Python packages (h5py, numpy, pandas, geopandas, fsspec, etc.).
# 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 precip_analyze_atlas14-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 precip-analyze-atlas14-variance-gpt-cmdr-ras-commander --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.