Is it feasible to estimate radiosonde biases from interlaced measurements
Abstract. 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 at 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.