Quantifying the value of redundant measurements at GRUAN sites

The potential for measurement redundancy to reduce uncertainty in atmospheric variables has not been investigated comprehensively for climate observations. We evaluated the usefulness of entropy and mutual correlation concepts, as defined in information theory, for quantifying random uncertainty and redundancy in time series of atmospheric water vapor provided by five highly instrumented GRUAN (GCOS [Global Climate Observing System] Reference Upper-Air Network) Stations in 2010–2012. Results show that the random uncertainties for radiosonde, frost-point hygrometer, Global Positioning System, microwave and infrared radiometers, and Raman lidar measurements differed by less than 8%. Comparisons of time series of the Integrated Water Vapor (IWV) content from ground-based remote sensing instruments with in situ soundings showed that microwave radiometers have the highest redundancy and therefore the highest potential to reduce random uncertainty of IWV time series estimated by radiosondes. Moreover, the random uncertainty of a time series from one instrument should be reduced of ~ 60% by constraining the measurements with those from another instrument. The best reduction of random uncertainty resulted from conditioning of Raman lidar measurements with microwave radiometer measurements. Specific instruments are recommended for atmospheric water vapor measurements at GRUAN sites. This approach can be applied to the study of redundant measurements for other climate variables.