Abstract
The precision of atmospheric probe data acquisition is vulnerable to temperature asynchrony, largely due to the fact that temperature fluctuations can alter sensor response times and measurement outcomes, resulting in asynchronous and biased data collection. To assess the performance of electrical resistance (ER) probes across different scenarios, we employed the ratio of the measured resistance to that of a reference sample as our evaluation metric. Through both theoretical analysis and targeted experiments simulating temperature deviations, we have shown that temperature perturbations can induce substantial variations in the resistance of the test material. More specifically, the discrepancy in the measured values due to temperature asynchrony escalates with the extent of the temperature fluctuation, whereas the impact of such asynchrony diminishes at higher temperatures. In practical settings, incorporating dual thermocouples within the probe for real-time data correction presents significant challenges. Consequently, this study proposes a method for postprocessing raw data, which involves detecting and substituting anomalous values, influenced by temperature, with the median value. This approach ensures that the processed data faithfully represent the typical variations and trends observed in the probe readings, effectively reducing the adverse effects of temperature asynchrony on the accuracy of data acquisition.