Engr Evaluation of IoT-Based Thermodynamic Monitoring for Predictive Weather Analysis in Tropical Microclimates

Abstract

Accurate short-term weather forecasting in tropical regions demands high-resolution tracking of rapidly changing thermodynamic variables. This study evaluates a low-cost IoT weather monitoring system over 60 days, using an ESP8266 microcontroller coupled with a DHT22 sensor to measure temperature (°C), relative humidity (%), dew point (°C), and heat index (°C) every 5–10 seconds, generating >44,000 data points. Analysis showed temperature, dew point, and heat index exhibited excellent stability (CV < 1%), while relative humidity varied moderately (CV ? 2–3%). Pearson correlation revealed strong interdependence: temperature–heat index (r = 0.98), dew point–heat index (r = 0.93), and relative humidity–dew point (r = 0.91). A rule-based classification identified Moderate-Cloudy conditions 95–97% of the time, validating real-time microclimatic assessment. By integrating high-frequency measurement with derived thermodynamic parameters, this system provides robust predictive insights, offering a scalable, low-cost alternative to conventional weather stations. Applications include precision agriculture, disaster mitigation, and climate-resilient urban planning, showcasing the engineering potential of compact IoT-based monitoring systems.