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. Upper-air measurements of essential climate variables (ECVs), such astemperature, are crucial for climate monitoring and climate change detection.Because of the internal variability of the climate system, many decades ofmeasurements are typically required to robustly detect any trend in theclimate data record. It is imperative for the records to be temporallyhomogeneous over many decades to confidently estimate any trend.Historically, records of upper-air measurements were primarily made forshort-term weather forecasts and as such are seldom suitable for studyinglong-term climate change as they lack the required continuity andhomogeneity. Recognizing this, the Global Climate Observing System (GCOS)Reference Upper-Air Network (GRUAN) has been established to providereference-quality measurements of climate variables, such as temperature,pressure, and humidity, together with well-characterized and traceableestimates of the measurement uncertainty. To ensure that GRUAN data productsare suitable to detect climate change, a scientifically robust instrumentreplacement strategy must always be adopted whenever there is a change ininstrumentation. By fully characterizing any systematic differences betweenthe old and new measurement system a temporally homogeneous data series canbe created. One strategy is to operate both the old and new instruments intandem for some overlap period to characterize any inter-instrument biases.However, this strategy can be prohibitively expensive at measurement sitesoperated by national weather services or research institutes. An alternativestrategy that has been proposed is to alternate between the old and newinstruments, so-called interlacing, and then statistically derive thesystematic biases between the two instruments. Here we investigate thefeasibility of such an approach specifically for radiosondes, i.e. flying theold and new instruments on alternating days. Synthetic data sets are used toexplore the applicability of this statistical approach to radiosonde changemanagement.

Is it feasible to estimate radiosonde biases from interlaced measurements?