Features

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Skyborn is a comprehensive Python package for climate data analysis, featuring advanced statistical methods, emergent constraint techniques, and interpolation, plotting, and spectral analysis utilities.

Features#

  • Emergent Constraint Methods: Advanced statistical techniques for reducing uncertainty in climate projections

  • GridFill Interpolation: Sophisticated atmospheric data gap-filling using Poisson equation solvers

  • Windspharm Analysis: Comprehensive spherical harmonic vector wind field analysis

  • Statistical Analysis: Comprehensive statistical and correlation functions

  • Gradient Calculations: Spatial and temporal gradient analysis tools

  • Causality Analysis: Granger and Liang causality methods

  • Visualization: High-quality plotting and visualization capabilities

New in Version 0.3.10: Enhanced GridFill module with xarray interface, comprehensive documentation, and tutorial examples for atmospheric data interpolation.

Quick Start#

Install Skyborn and start analyzing climate data:

pip install skyborn

import skyborn as skb
from skyborn.gridfill.xarray import gridfill_xarray

# Use emergent constraint methods
pdf = skb.gaussian_pdf(mu=0, sigma=1, x=x_values)

# Advanced atmospheric data interpolation (NEW in v0.3.10)
filled_data = gridfill_xarray(atmospheric_data, eps=1e-4)

# Statistical analysis
correlation = skb.pearson_correlation(x_data, y_data)

Reference Implementation#

Our emergent constraint methods are adapted from the work by blackcata: blackcata/Emergent_Constraints

Based on Cox, P. M., et al. (2013). Nature, 494(7437), 341-344.

Indices and tables#