| dc.description.abstract | Rainfall measurements are susceptible to various sources of uncertainty that can affect measurement accuracy. Factors such as instrument calibration, weather conditions, and measurement methods contribute to measurement uncertainty. The uncertainty of rainfall measurements is typically determined through a calibration process following standard procedures such as ISO/IEC 17025. The conventional approach for evaluating measurement uncertainty is the law of uncertainty propagation (LPU), which involves complex mathematical calculations. However, an alternative method is needed to assess measurement uncertainty apart from LPU. This research focuses on the use of Python-based Monte Carlo method to determine and analyze measurement uncertainty in rainfall gauges. The Monte Carlo method involves repeated random simulations by combining probability distributions for the input and output of measurements in the rainfall gauge. The results of the study show that the Monte Carlo method implemented using Python provides accurate determination of measurement uncertainty in rainfall gauges. Additionally, the accuracy of the standard measuring instrument in the calibration process is identified as a significant factor influencing the overall measurement uncertainty. | en_US |
