Ground-based microwave radiometers (MWR) provide continuous and unattended retrievals of atmospheric temperature and humidity profiles, as well as of vertically-integrated total column water vapour (TCWV) and cloud liquid water (TCLW). Despite the significant scientific advancements allowed by MWR observations over the last forty years, current operational MWR retrievals are still lacking a traceable uncertainty estimate.

Retrieving tropospheric ozone from passive remote sensing observations is difficult because almost 90% of the total column ozone resides in the stratosphere. Pioneering studies have demonstrated that information on tropospheric ozone can be extracted using the so-called MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) technique. The information content of such measurements, however, remains to be thoroughly explored.

Pandora is a relatively new UV-VIS instrument for measuring total ozone and also ozone profiles in a similar way as MAX-DOAS instruments. So far only a few studies exist which describe measurement uncertainties or measurement validation. As a relatively inexpensive and automated instrument, there is a strong potential that a network of Pandora instruments could have a substantial role in the satellite validation in the future. A metrologically rigorous uncertainty quantification for the Pandora instrument is therefore needed.

The uncertainties in the ozone slant columns retrieved with DOAS data analysis fitting procedures are predominantly caused by instrumental imperfections and by issues introduced within the analysis routines. Such uncertainties are often random and therefore can be estimated statistically from, e.g., the least-squares fit procedure. However, the fitting uncertainties derived from such analysis typically result in unrealistically small uncertainties and can lead to an underestimate by up to a factor of two.

The uncertainty in the ozone absorption cross-sections is one of the main systematic error sources in the remote sensing of atmospheric ozone using UV-visible spectroscopy techniques. It is a structured random effect in that even though the uncertainty can be considered as primarily a systematic error source, the actual error is dependent on atmospheric temperature which varies across the annual cycle and with synoptic conditions.

This gap addresses the need for sustained calibration of the FTIR remote sensing data (essentially columns with some vertical information that enables to separate partial columns) for CO2, CH4 (and CO). This can be done by comparing the FTIR data with co-located or nearby in-situ soundings of the same species that are calibrated to community standards, in this case the WMO standards. At present, however, there is not enough capacity to provide such in-situ data.

There is no clear agreement yet within the FTIR community on the distinction and characterisation of the random and systematic components of the uncertainty in FTIR measurements. As a consequence, no common approach is available on how to evaluate these components appropriately leading to a degree of heterogeneity in the global FTIR network.

The traceability of ground-based microwave radiometer (MWR) estimates and their uncertainty requires the traceability of MWR calibration to SI standards. Currently, no SI standard is available for MWR at any national/international measurement institute. Thus, full SI-traceability of ECVs from MWR is currently not feasible. However, at least one national measurement institute is currently developing SI standards for MWR. It is expected that SI-traceable standards for MWR will be available in the next few years.

Temperature lidars provide important information for trend detection in the middle atmosphere (connected to trends in the ozone layer). These are measured using lidar systems that often also measure the ozone layer. The lidar technique to measure temperature is sensitive to the presence of aerosol, which is an important contribution to the error budget. In addition, lidar techniques exist to measure temperature profiles in the troposphere using the pure-rotational Raman (PRR) technique that can be used in the presence of aerosol.