Update documentation and add architectural approach document

collab/proposals/gis-weather-proposal.md

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+# GIS and Weather Data Processing Container Proposal
+
+## Proposal Summary
+Create specialized Docker containers for GIS data processing and weather data analysis to support CTO-mode R&D activities, particularly for infrastructure planning and balloon path prediction for your TSYS Group projects.
+
+## Business Rationale
+As GIS and weather data analysis become increasingly important for your TSYS Group projects (particularly for infrastructure planning like solar farms and building datasets, and balloon path prediction), there's a need for specialized containers that can handle these data types efficiently while maintaining consistency with existing infrastructure patterns. The containers will support:
+- Self-hosted GIS stack for privacy and control
+- Processing public datasets (NOAA, European APIs, etc.)
+- ETL workflows for both technical and business data processing
+- Integration with MinIO for data output to business systems
+
+## Technical Approach
+- Follow the same disciplined container architecture as the documentation tools
+- Use layered approach with base and specialized containers
+- Implement same security patterns (non-root user, UID/GID mapping)
+- Maintain consistent naming conventions
+- Use same operational patterns (wrapper scripts, etc.)
+- Include PostGIS, DuckDB, and optional GPU support
+- Implement MinIO integration for data output
+- Support for prototyping on workstations and production on large servers
+
+## Technology Stack
+- **GIS Tools**: GDAL, Tippecanoe, DuckDB with spatial extensions
+- **Database**: PostgreSQL/PostGIS client tools
+- **Formats**: Shapefiles, Parquet, GRIB, GeoJSON
+- **Weather**: cfgrib, xarray, MetPy
+- **ETL**: Pandas, Dask, custom workflow tools
+- **APIs**: NOAA, European weather APIs
+- **Visualization**: Folium, Plotly, command-line tools
+
+## Benefits
+- Consistent environment across development (workstations) and production (large servers)
+- Proper file permission handling across different systems
+- Isolated tools prevent dependency conflicts
+- Reproducible analysis environments for GIS and weather data
+- Integration with documentation tools for CTO mode workflows
+- Support for both technical (GIS/Weather) and business (COO) ETL workflows
+- Scalable architecture with optional GPU support
+- Data output capability to MinIO buckets for business use
+
+## Resource Requirements
+- Development time: 3-4 weeks for complete implementation
+- Storage: Additional container images (est. 3-6GB each)
+- Compute: Higher requirements for processing (can be isolated to CTO mode)
+- Optional: GPU resources for performance-intensive tasks
+
+## Expected Outcomes
+- Improved capability for spatial and weather data analysis
+- Consistent environments across development and production systems
+- Better integration with documentation workflows
+- Faster setup for ETL projects (both technical and business)
+- Efficient processing of large datasets using DuckDB and Parquet
+- Proper data output to MinIO buckets for business use
+- Reduced technical debt through consistent patterns
+
+## Implementation Timeline
+- Week 1: Base GIS container with PostGIS, DuckDB, and data format support
+- Week 2: Base Weather container with GRIB support and API integration
+- Week 3: Advanced processing containers with Jupyter and visualization
+- Week 4: Optional GPU variants and MinIO integration testing
+
+## Approval Request
+Please review and approve this proposal to proceed with implementation of the GIS and weather data processing containers that will support your infrastructure planning and balloon path prediction work.