NetCDF Territorial Classifications and Python Colab Scripts Based on Low-Pass Filtered SbAI Time-Series Signals for Periods Longer Than 2 Years in Southern South America (Argentina, Chile, and Uruguay), Including StandardScaler-Normalized and Non-Normalized Versions. (TS-LP2 and TS-LP2S)
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Fecha
Publicador:
Consejo Nacional de Investigaciones Científicas y Técnicas
Fecha de depósito:
27/05/2026
Fecha de creación:
20/5/2026
Clasificación temática
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Resumen
This notebook implements a complete pipeline for FIR lowpass filtering and spatial classification of SbAI data:
- Applies a FIR lowpass filter (>2 years, 101 taps) to isolate long‑term variability.
- Normalizes and interpolates missing values along the time axis.
- Saves filtered series as NetCDF for reproducibility.
- Performs clustering with KMeans++ using two scaling strategies:
- RobustScaler (TS-LP2).
- StandardScaler with transpose (TS-LP2S).
- Generates classification maps with geographic boundaries.
- Extracts cluster centroids and plots representative time series.
- Computes clustering quality metrics (Silhouette, explained variance, relative reconstruction error).
Información Técnica
The workflow consists of:
1. Mount Google Drive and define the working directory (`base_dir`).
2. Load raw CSV (`SbAI16_era5_roi2_latlon_fecha.csv`).
3. Apply FIR lowpass filter (>2 years, 101 taps).
- Save output in `netcdf_sbai_025_lowpass_fir.nc`.
4. Classification:
- Stack filtered series into pixel × time matrix.
- Apply scaling:
- RobustScaler → `clasificacion_TS-LP2.nc`.
- StandardScaler (transpose) → `clasificacion_TS-LP2S.nc`.
- Run KMeans++ (n_clusters=5, n_init=10).
- Save cluster maps and metrics.
5. Visualization:
- Maps of clusters with canvas boundaries (`canvas.gpkg`).
- Closest series to centroids plotted with publication‑ready formatting.
6. Metrics:
- Silhouette index.
- Explained variance (R²).
- Relative reconstruction error.
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