What does 'sensor fusion' refer to in the context of RAWS?

In the context of RAWS, sensor fusion specifically refers to the process of integrating data from multiple sensors to enhance the accuracy and reliability of the information gathered. This practice is crucial in systems where various types of sensors measure different environmental parameters or phenomena, such as temperature, humidity, wind speed, and other meteorological variables. By combining the outputs from these diverse sensors, RAWS can improve the overall situational awareness and provide more accurate real-time data.

Sensor fusion allows for a more comprehensive understanding of the environment, leveraging the strengths of each sensor type and compensating for individual limitations. This integrated approach reduces the ambiguity and uncertainty present in single-source data, thus leading to better decision-making and analysis.