analyzing-aorc-precipitation

name

analyzing-aorc-precipitation

description

Retrieves and processes AORC precipitation data for HEC-RAS/HMS models. Handles spatial averaging over watersheds, temporal aggregation, DSS export, and Atlas 14 design storms. Use when working with historical precipitation, AORC data, calibration workflows, design storm generation, rainfall analysis, SCS Type II distributions, AEP events, 100-year storms, or generating precipitation boundary conditions for rain-on-grid models. Triggers: precipitation, AORC, Atlas 14, design storm, rainfall, SCS Type II, AEP, 100-year, rain-on-grid, hyetograph, temporal distribution, areal reduction, calibration, historical precipitation.

Analyzing AORC Precipitation

Purpose: Navigate precipitation workflows for HEC-RAS/HMS models using AORC historical data and Atlas 14 design storms.

This skill is a NAVIGATOR - it points you to the primary sources containing complete workflows and API documentation. For implementation details, always refer to the primary sources below.

Primary Sources (Read These First!)

1. Complete API Reference and Workflows

ras_commander/precip/CLAUDE.md (329 lines - AUTHORITATIVE SOURCE)

Contains:

• Complete module organization (PrecipAorc, StormGenerator)
• Full API reference with all methods
• Step-by-step AORC workflow (retrieval, spatial averaging, temporal aggregation, export)
• Step-by-step Atlas 14 workflow (query, generate, apply ARF, export)
• Multi-event workflows
• Performance characteristics
• Dependencies and installation

THIS IS THE PRIMARY DOCUMENTATION - use it for all detailed questions.

2. AORC Demonstration Notebook

examples/900_aorc_precipitation.ipynb

Live working example showing:

• AORC data retrieval from cloud storage
• Spatial averaging over watersheds
• Temporal aggregation to HEC-RAS intervals
• Export to DSS and CSV formats
• Integration with HEC-RAS unsteady flow files

3. Atlas 14 Single-Project Workflow

examples/720_atlas14_aep_events.ipynb

Complete design storm workflow:

• Query Atlas 14 precipitation frequency values
• Generate SCS Type II temporal distributions
• Apply areal reduction factors
• Create HEC-RAS plans for multiple AEP events
• Batch execution and results processing

4. Atlas 14 Multi-Project Batch Processing

examples/722_atlas14_multi_project.ipynb

Advanced batch processing:

• Process multiple HEC-RAS projects simultaneously
• Standardized AEP suite (10%, 2%, 1%, 0.2%)
• Automated plan creation across projects
• Parallel execution with result consolidation

Quick Start

AORC Historical Data (30 seconds)

from ras_commander.precip import PrecipAorc

# Retrieve hourly AORC data for watershed
aorc_data = PrecipAorc.retrieve_aorc_data(
    watershed="02070010",  # HUC-8 code or shapefile path
    start_date="2015-05-01",
    end_date="2015-05-15"
)

# Spatial average over watershed
avg_precip = PrecipAorc.spatial_average(aorc_data, watershed)

# Aggregate to HEC-RAS interval
hourly = PrecipAorc.aggregate_to_interval(avg_precip, interval="1HR")

# Export to DSS for HEC-RAS
PrecipAorc.export_to_dss(
    hourly,
    dss_file="precipitation.dss",
    pathname="/PROJECT/PRECIP/AORC//1HOUR/OBS/"
)

Atlas 14 Design Storm (30 seconds)

from ras_commander.precip import StormGenerator

# Get 24-hr, 1% AEP (100-year) precipitation
precip = StormGenerator.get_precipitation_frequency(
    location=(38.9, -77.0),  # lat, lon
    duration_hours=24,
    aep_percent=1.0
)

# Generate SCS Type II distribution
hyetograph = StormGenerator.generate_design_storm(
    total_precip=precip,
    duration_hours=24,
    distribution="SCS_Type_II",
    interval_minutes=15
)

# Export to HEC-RAS DSS
StormGenerator.export_to_dss(
    hyetograph,
    dss_file="design_storm.dss",
    pathname="/PROJECT/PRECIP/DESIGN//15MIN/SYN/"
)

When to Use This Skill

Use when you need to:

1. Retrieve historical precipitation - AORC data for calibration and validation
2. Generate design storms - Atlas 14 AEP events (10%, 2%, 1%, 0.2%, etc.)
3. Process precipitation spatially - Watershed averaging, areal reduction factors
4. Aggregate precipitation temporally - Match HEC-RAS/HMS timesteps
5. Export to HEC-RAS/HMS - DSS files, CSV time series, or direct HDF integration
6. Identify storm events - Extract individual storms from AORC record
7. Apply temporal distributions - SCS Type II, IA, III for design storms

Core Concepts (Brief)

AORC Dataset

• Coverage: CONUS (1979-present), ~800m hourly resolution
• Format: Cloud-optimized Zarr on AWS S3 (anonymous access)
• Provider: NOAA Office of Water Prediction
• Use Case: Historical calibration, storm event analysis

NOAA Atlas 14

• Coverage: CONUS, Hawaii, Puerto Rico
• Data: Precipitation frequency estimates (depth-duration-frequency)
• Access: NOAA HDSC PFDS API (JSON)
• Use Case: Design storm generation for AEP events

Temporal Distributions

• SCS Type II: Standard for most of US (peak at 12hr of 24hr storm)
• SCS Type IA: Pacific maritime climate (peak at 8hr)
• SCS Type III: Gulf Coast and Florida (peak at 13hr)

Areal Reduction Factors (ARF)

• < 10 sq mi: ARF ≈ 1.0 (use point values)
• 10-100 sq mi: ARF = 0.95-0.98
• > 100 sq mi: ARF < 0.95 (significant reduction)

Common Workflows (High-Level)

Calibration with AORC

1. Retrieve AORC for historical storm event
2. Apply spatial average over watershed
3. Aggregate to model timestep
4. Run HEC-RAS/HMS model
5. Compare modeled vs observed flow/stage

Details: See ras_commander/precip/CLAUDE.md "AORC Workflow" section

Design Storm Analysis

1. Query Atlas 14 for design AEP
2. Generate temporal distribution (SCS Type II)
3. Apply areal reduction (if needed)
4. Export to HEC-RAS/HMS
5. Run model for design event

Details: See ras_commander/precip/CLAUDE.md "Atlas 14 Workflow" section

Multi-Event Suite

1. Define AEP range (50% to 0.2%)
2. Loop through events and generate design storms
3. Batch run HEC-RAS models
4. Generate flood frequency curves

Details: See examples/104_Atlas14_AEP_Multi_Project.ipynb

API Quick Reference (Navigate to CLAUDE.md for Details)

PrecipAorc Methods

Data Retrieval:

• retrieve_aorc_data() - Download AORC time series for watershed
• get_available_years() - Query available data years (1979-present)
• check_data_coverage() - Verify spatial and temporal coverage

Spatial Processing:

• spatial_average() - Calculate areal average over watershed
• extract_by_watershed() - Extract data for HUC or custom polygon
• resample_grid() - Aggregate AORC grid cells to coarser resolution

Temporal Processing:

• aggregate_to_interval() - Aggregate to HEC-RAS/HMS intervals (1HR, 6HR, 1DAY)
• extract_storm_events() - Identify and extract individual storm events
• calculate_rolling_totals() - Compute N-hour rolling precipitation totals

Output Formats:

• export_to_dss() - DSS format for HEC-RAS/HMS
• to_csv() - CSV time series for HEC-HMS
• to_netcdf() - NetCDF for further analysis

StormGenerator Methods

Design Storm Creation:

• generate_design_storm() - Create Atlas 14 design storm hyetograph
• get_precipitation_frequency() - Query Atlas 14 point precipitation values
• apply_temporal_distribution() - Apply standard temporal patterns (SCS Type II, etc.)

Spatial Processing:

• apply_areal_reduction() - Apply ARF for large watersheds
• interpolate_point_values() - Interpolate Atlas 14 values to grid
• generate_multi_point_storms() - Spatially distributed design storms

Output Formats:

• export_to_dss() - HEC-RAS DSS precipitation
• export_to_hms_gage() - HEC-HMS precipitation gage file
• to_csv() - Tabular hyetograph (CSV)

Full method signatures and parameters: See ras_commander/precip/CLAUDE.md

Example Patterns

AORC Storm Catalog Generation

from ras_commander.precip import PrecipAorc
from ras_commander import init_ras_project
from ras_commander.hdf import HdfProject

# Initialize project
ras = init_ras_project("path/to/project", "6.6")

# Get project bounds from geometry HDF
geom_hdf = ras.project_folder / f"{ras.project_name}.g09.hdf"
bounds = HdfProject.get_project_bounds_latlon(
    geom_hdf,
    buffer_percent=50.0  # 50% buffer ensures full coverage
)

# Generate storm catalog
catalog = PrecipAorc.get_storm_catalog(
    bounds=bounds,
    year=2020,
    inter_event_hours=8.0,     # USGS standard for storm separation
    min_depth_inches=0.75,     # Minimum significant precipitation
    buffer_hours=48            # Simulation warmup buffer
)

# Returns DataFrame with:
# storm_id, start_time, end_time, sim_start, sim_end,
# total_depth_in, peak_intensity_in_hr, duration_hours, rank

Complete workflow: See examples/900_aorc_precipitation.ipynb

Atlas 14 Multi-Event Suite

from ras_commander.precip import StormGenerator

# Define AEP suite
aep_events = [10, 4, 2, 1, 0.5, 0.2]  # 10%, 4%, 2%, 1%, 0.5%, 0.2%

for aep in aep_events:
    # Query Atlas 14
    precip = StormGenerator.get_precipitation_frequency(
        location=(38.9, -77.0),
        duration_hours=24,
        aep_percent=aep
    )

    # Generate design storm
    hyetograph = StormGenerator.generate_design_storm(
        total_precip=precip,
        duration_hours=24,
        distribution="SCS_Type_II"
    )

    # Export to DSS
    dss_file = f"design_storm_{aep}pct.dss"
    StormGenerator.export_to_dss(hyetograph, dss_file)

Complete multi-project workflow: See examples/722_atlas14_multi_project.ipynb

Dependencies

Required:

• pandas (time series handling)
• numpy (numerical operations)
• xarray (for AORC NetCDF data)
• requests (Atlas 14 API access)

Optional:

• geopandas (spatial operations on watersheds)
• rasterio (AORC grid processing)

Installation:

pip install ras-commander[precip]  # Includes all precipitation dependencies
# OR
pip install xarray rasterio geopandas

Navigation Map

When you need...

API Documentation

→ Read ras_commander/precip/CLAUDE.md (329 lines, complete API reference)

AORC Workflow Example

→ Open examples/900_aorc_precipitation.ipynb (live working code)

Atlas 14 Single Project

→ Open examples/720_atlas14_aep_events.ipynb

Atlas 14 Multi-Project Batch

→ Open examples/722_atlas14_multi_project.ipynb

Method Signatures and Parameters

→ Read ras_commander/precip/CLAUDE.md "Module Organization" section

Use Cases and Performance

→ Read ras_commander/precip/CLAUDE.md "Common Use Cases" and "Performance" sections

Data Source Details

→ Read ras_commander/precip/CLAUDE.md "Data Sources" section

Key Design Principles

1. Primary Sources First: Always refer to ras_commander/precip/CLAUDE.md for authoritative API details
2. Example Notebooks as References: Use notebooks to understand workflows in practice
3. No Duplication: This skill does NOT duplicate workflows - it NAVIGATES to them
4. Multi-Level Verifiability: All outputs reviewable in HEC-RAS/HMS GUI
5. Lazy Loading: Optional dependencies only loaded when needed

Performance Notes (Brief)

AORC Data Retrieval:

• Speed: ~1-5 minutes per year of hourly data
• Storage: ~10-50 MB per year (hourly, single watershed)
• Caching: Local cache recommended for repeated analyses

Atlas 14 Queries:

• Speed: < 5 seconds per query (API access)
• Rate Limiting: NOAA PFDS has request limits (respect usage guidelines)
• Caching: Automatic caching of API responses

Details: See ras_commander/precip/CLAUDE.md "Performance" section

See Also

Within Repository:

• ras_commander/precip/CLAUDE.md - Complete precipitation API reference (PRIMARY SOURCE)
• ras_commander/CLAUDE.md - Parent library context
• ras_commander/dss/AGENTS.md - DSS file operations
• examples/900_aorc_precipitation.ipynb - AORC demonstration
• examples/720_atlas14_aep_events.ipynb - Atlas 14 single project
• examples/722_atlas14_multi_project.ipynb - Atlas 14 multi-project

Related Components:

• ras_commander.RasUnsteady - Unsteady flow file management
• ras_commander.dss.RasDss - DSS file operations
• .claude/rules/python/path-handling.md - Spatial data handling patterns

Usage Pattern

1. Understand the workflow: Read ras_commander/precip/CLAUDE.md for complete details
2. See it in action: Open relevant example notebook (examples/24_*.ipynb, examples/103_*.ipynb, etc.)
3. Implement: Copy patterns from notebook, adapt to your project
4. Verify: Check outputs in HEC-RAS/HMS GUI

This skill is a lightweight index - detailed content lives in primary sources.