skyborn 0.3.17

Atmospheric science research utilities

System Requirements

Operating System: 🖥️ Cross-Platform

This package supports Windows, Linux, and macOS. However, it has been primarily developed and tested on Windows.

Note: While the package can be installed on different platforms, some Windows-specific features may not work on other operating systems.

Installation

To install the Skyborn package, you can use pip:

pip install skyborn

or

pip install -U --index-url https://pypi.org/simple/ skyborn

📚 Documentation

Full documentation is available at: Documentation

🎯 Key Features & Submodules

📊 Spatial Trend Analysis & Climate Index Regression

Skyborn provides ultra-fast spatial trend calculation and climate index regression analysis for atmospheric data:

Key Capabilities:

• High-Speed Spatial Trends: Calculate long-term climate trends across global grids

  • Linear trend analysis for temperature, precipitation, and other variables
  • Statistical significance testing
  • Vectorized operations for massive datasets

• Climate Index Regression: Rapid correlation and regression analysis with climate indices

  • NINO 3.4, PDO, NAO, AMO index integration
  • Pattern correlation analysis
  • Teleconnection mapping

Other Applications:

• Climate change signal detection
• Decadal variability analysis
• Teleconnection pattern identification
• Regional climate impact assessment

🌍 Skyborn Windspharm Submodule - Atmospheric Analysis

The Skyborn windspharm submodule provides powerful tools for analyzing global wind patterns through streamfunction and velocity potential calculations:

Key Capabilities:

• Streamfunction Analysis: Identifies rotational (non-divergent) wind components

  • Visualizes atmospheric circulation patterns
  • Reveals jet streams and vortices
  • Essential for understanding weather systems

• Velocity Potential Analysis: Captures divergent wind components

  • Shows areas of convergence and divergence
  • Critical for tropical meteorology
  • Identifies monsoon circulation patterns

Applications:

• Climate dynamics research
• Weather pattern analysis
• Atmospheric wave propagation studies
• Tropical cyclone formation analysis

🔧 Skyborn Gridfill Submodule - Data Interpolation

The Skyborn gridfill submodule provides advanced interpolation techniques for filling missing data in atmospheric and climate datasets:

Key Features:

• Poisson-based Interpolation: Physically consistent gap filling
• Preserves Data Patterns: Maintains spatial correlations and gradients
• Multiple Methods Available:
  • Basic Poisson solver
  • High-precision iterative refinement
  • Zonal initialization options
  • Relaxation parameter tuning

Applications:

• Satellite data gap filling
• Model output post-processing
• Climate data reanalysis
• Quality control for observational datasets

The example above demonstrates filling gaps in global precipitation data, where the algorithm successfully reconstructs missing values while preserving the underlying meteorological patterns.

Performance Benchmarks

🚀 Windspharm Performance

The Skyborn windspharm submodule delivers ~25% performance improvement over standard implementations through modernized Fortran code and optimized algorithms:

Key Performance Metrics:

• Vorticity Calculation: ~25% faster
• Divergence Calculation: ~25% faster
• Helmholtz Decomposition: ~25% faster
• Streamfunction/Velocity Potential: ~25% faster

⚡ GPI Module Performance

The Genesis Potential Index (GPI) module achieves dramatic speedups through vectorized Fortran implementation and native 3D processing:

Performance Highlights:

• 19-25x faster than point-by-point implementations
• Processes entire atmospheric grids in seconds
• Native multi-dimensional support (3D/4D data)

Accuracy Validation:

• Correlation coefficient > 0.99 with reference implementations
• RMSE < 1% for both VMAX and PMIN calculations

📖 Citation

If you use Skyborn in your research, please cite it using the following format:

@software{su2025skyborn,
  author = {Su, Qianye},
  title = {Skyborn: Climate Data Analysis Toolkit},
  year = {2025},
  doi = {10.5281/zenodo.18075252},
  url = {https://doi.org/10.5281/zenodo.18075252}
}

Or in text:

Su, Q. (2025). Skyborn: Climate Data Analysis Toolkit. Zenodo. https://doi.org/10.5281/zenodo.18075252