Proof-of-Concept: Vertical Wind Profile Reconstruction from Ground-Based Optical Sensors Using Machine Learning

Vertical wind profiles are critical for weather forecasting, aviation safety, and atmospheric research, yet remain sparsely observed due to the high cost of radiosondes (€100–200 per launch) and wind profiler radars (€100,000–1,000,000). We present a proof-of-concept demonstrating that ground-based optical measurements from low-cost amateur radio sensors can predict upper-air wind speeds across multiple atmospheric layers using machine learning. Two monitoring stations (DG2MCM-15 and DG2MCM-16) near Kempten, Germany (47.73°N, 10.32°E, 686 m ASL) measured infrared sky temperature (MLX90614, €15), multi-channel spectral radiance (AS7341, €8), and RGB photometry every 5 minutes during 15–21 February 2026. Random Forest regression models trained on 21 coincident samples with EUMETSAT Meteosat Third Generation (MTG) Atmospheric Motion Vectors achieved training R²=0.75–0.91 for wind speeds in the lower troposphere (1–3 km), middle troposphere (3–6 km), upper troposphere (6–9 km), and jetstream (9–12 km) layers. Feature importance analysis revealed that infrared-derived cloud base height was the strongest predictor for upper tropospheric winds (28% importance), while the spectral aerosol Ångström exponent ranked among the top features (10% overall), validating the hypothesis that atmospheric optical properties encode information about vertical structure and synoptic forcing. Surface wind speed contributed minimal predictive power (4%), indicating decoupling between 10 m winds and upper-air flows. Test set performance showed severe overfitting (R²_test = −2.59) due to small sample size, confirming that operational deployment requires larger training datasets. However, the proof-of-concept successfully demonstrates feasibility: total hardware cost of €250 per station enables dense observational networks impossible with traditional instrumentation. A follow-up 6-month validation study (Paper 2b) is planned to assess generalizability across seasons and weather regimes.