The following GRUAN-relevant paper was published at AMTD:
“Is it feasible to estimate radiosonde biases from interlaced measurements?” By Kremser et al.
The paper is open for discussion until 28 March 2018.
Is it feasible to estimate radiosonde biases from interlaced measurements?
Kremser, S., Tradowsky, J. S., Rust, H. W., and Bodeker, G. E.
by Atmospheric Measurement Techniques (AMT) - The paper is now accessible and open for interactive public discussion until 28 March 2018.
Upper-air measurements of essential climate variables (ECVs), such as temperature, are crucial for climate monitoring and climate change detection. Because of the internal variability of the climate system, many decades of measurements are typically required to robustly detect any trend in the climate data record. It is imperative for the records to be temporally homogeneous over many decades to confidently estimate any trend. Historically, records of upper-air measurements were primarily made for short-term weather forecasts and, as such, are seldom suitable for studying long-term climate change as they lack the required continuity and homogeneity. Recognizing this, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) has been established to provide reference-quality measurements of climate variables, such as temperature, pressure and humidity, together with well characterized and traceable estimates of the measurement uncertainty. To ensure that GRUAN data products are suitable to detect climate change, a scientifically robust instrument replacement strategy must always be adopted whenever there is a change in instrumentation. By fully characterizing any systematic differences between the old and new measurement system a temporally homogeneous data series can be created. One strategy is to operate both the old and new instruments in tandem for some overlap period to characterize any inter-instrument biases. However, this strategy can be prohibitively expensive measurement sites operated by national weather services or research institutes. An alternative strategy that has been proposed is to alternate between the old and new instruments, so-called interlacing, and then statistically derive the systematic biases between the two instruments. Here we investigate the feasibility of such an approach specifically for radiosondes, i.e. flying the old and new instruments on alternating days. Synthetic data sets are used to explore the applicability of this statistical approach to radiosonde change management.
Kremser, S., Tradowsky, J. S., Rust, H. W., and Bodeker, G. E.: Is it feasible to estimate radiosonde biases from interlaced measurements?, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-6, in review, 2018.