CCA_PCR
OfficialEnhance seasonal climate forecasts with CCA and PCR
Authoriri-pycpt
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill improves seasonal climate forecasts by applying Canonical Correlation Analysis (CCA) and Principal Component Regression (PCR) to analyze large datasets and predict seasonal climate patterns.
Core Features & Use Cases
- CCA Analysis: Decompose predictor and predictand fields into orthogonal components and analyze their relationships.
- PCR Analysis: Reduce the dimensionality of predictor fields using principal components, improving computational efficiency.
- Use Case: This Skill can be used to analyze and predict seasonal rainfall patterns using models like CFSv2 or SEAS5 and observed data like CHIRPS.
Quick Start
Run the CCA_PCR skill on the CFSv2 and SEAS5 models with CHIRPS data to predict June-September precipitation over West Africa.
Dependency Matrix
Required Modules
xarraynumpyscipymatplotlib
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: CCA_PCR Download link: https://github.com/iri-pycpt/PyCPT2-Seasonal-Forecast-User-Guide/archive/main.zip#cca-pcr Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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