NetCDF Territorial Classification and Python Colab Scripts Based on Band-Pass Filtered SbAI Time-Series Signals Between 0.1 and 0.5 Cycles per Year in Southern South America (Argentina, Chile, and Uruguay). (TS-BP-2-10)

Abstract

This notebook implements a complete pipeline for FIR bandpass filtering and spatial classification of SbAI data:

- Applies a FIR bandpass filter (1.5–10 years, 127 taps) to isolate mid‑frequency variability. - Normalizes and interpolates missing values along the time axis. - Saves filtered series as NetCDF for reproducibility. - Performs clustering with KMeans++ using a two‑stage scaling strategy: - RobustScaler to mitigate outliers. - StandardScaler to normalize temporal profiles. - Generates classification maps with geographic boundaries. - Extracts both cluster centroids and representative series closest to centroids. - Computes clustering quality metrics (Silhouette, explained variance, relative reconstruction error). - Runs an Elbow Method test to evaluate optimal number of clusters using: - Inertia (SSE). - Explained variance (R²). - Relative reconstruction error (%).