IoT-Based Empirical Validation of the Open-Meteo Global Weather Model

Abstract

The deployment of affordable Personal Weather Stations (PWS) may expose a set of Citizen IoT Vulnerabilities, including hardware siting bias, sensor faults, and temporal-resolution mismatches, that remain under-documented. This preliminary single-day, Single-Site Case Study explores the cataloguing of those gaps in a tropical Microclimate setting. This paper evaluates a custom ESP32-based IoT station deployed for a single day (4 May 2026) in Curug Sangereng, Tangerang, Indonesia, sampling at 30-second intervals. Using a Fault-Aware Quality Control protocol and an autocorrelation-adjusted effective sample size () to correct for Temporal Pseudo-Replication, the IoT record was benchmarked against Open-Meteo’s 15-minute forecast, BMKG (3-hourly forecast), and Meteostat (hourly archive) datasets. Variance decomposition showed that sub-15-minute fluctuations account for up to 53.9% of total-day wind-speed variance (and up to 67.1% for sub-3-hour fluctuations), compared to under 1.5% and 38% for temperature, pressure, and relative humidity, demonstrating that high-frequency sampling value is variable-dependent rather than universally necessary. Cross-dataset comparisons revealed severe hardware siting biases: Open-Meteo ran  cooler than the IoT station, while BMKG ran warmer than Meteostat () but cooler than IoT (). This sign reversal reflects unshielded rooftop heat bias at the IoT site. Poor wind-speed agreement across all pairs further indicated local anemometer obstruction. These single-day, single-site results are offered as a catalogue of PWS setup pitfalls and a worked diagnostic protocol. They motivate four falsifiable hypotheses for a multi-day confirmatory study rather than establishing general conclusions.

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