Land surface emissivity atlases in the infrared region (3-17 μm) are required for the validation of infrared satellite sounding measurements over land. Work is underway, outside of the GAIA-CLIM project, to develop dynamic atlases of spectral emissivity in this part of the spectrum, based on measurements from polar-orbiting hyper-spectral infrared observations and using a rapidly updating Kalman Filter. However, these new dynamic atlases need to be validated to ensure the estimates have robust uncertainties associated with them.

There is a lack of traceable uncertainties associated with the contribution of land surface microwave radiative transfer to Top of the Atmosphere (TOA) brightness temperatures for microwave imaging and sounding instruments. The land surface emission exhibits significant spatial and temporal variability, particularly in snow- and ice-covered regions.

Several passive microwave missions (operating in the 1-200 GHz range) make measurements in spectral regions where the atmosphere is sufficiently transmissive so that the surface contributes significantly to measured radiances. The calibration/validation of microwave satellite data to reference standards is hampered, for some instruments and channels, by a lack of traceable estimates of the uncertainties in the modelled ocean surface contribution.

Numerical Weather Prediction (NWP) models are already routinely used in the validation and characterisation of Earth Observation (EO) data. However, a lack of robust uncertainties associated with NWP model fields and related top-of-atmosphere (TOA) radiances prevent the use of these data for a complete and comprehensive validation of satellite EO data, including an assessment of absolute radiometric errors in new satellite instruments.

A data validation study is meant to check the consistency of a given dataset with respect to a reference dataset within their reported uncertainties. As such, the uncertainty budget of the data comparison is crucial. Besides the measurement uncertainties on both data sets, the discrepancy between the two datasets will be increased by uncertainties associated with data harmonization manipulations (e.g. unit conversions requiring auxiliary data, interpolations for altitude regridding) and with co-location mismatch, i.e.

Level-3 data are, by definition, constructed by averaging asynoptic level-2 data over certain space-time intervals, so as to arrive at a (regularly) gridded data product. However, the (global) sampling pattern of the sounder(s) that produced the original level-2 data is never perfectly uniform, nor are revisit times short enough to guarantee dense and homogeneous temporal sampling of e.g. a monthly mean at high horizontal resolution.

This gap concerns the need for a more detailed characterisation of the actual spatiotemporal smoothing and sampling properties of both satellite-based EO measurements and ground-based in-situ or remote-sensing measurements.

The impact of a particular choice of co-location criterion is only rarely studied in the scientific literature reporting on satellite validation results. However, without some quantification of the impact of the co-location criterion that was adopted, it is virtually impossible to assess the contribution of natural variability to the total error budget of the data comparisons. As such, this gap impacts significantly the potential interpretation of the data comparison result in terms of data quality.

The atmospheric concentration of nearly all ECVs varies in space and time at the scale of the individual measurements, and at the scale of their co-location in the context of data comparisons (e.g., for the purpose of satellite validation, data merging, and data assimilation). However, the amplitude and patterns of these variations are often unknown on such small scales. Consequently, it is impossible to quantify the uncertainties that result from sampling and smoothing properties of the measurements of the variable, structured atmospheric field.

Molecular spectroscopy provides the primary link between radiance and atmospheric gas composition. Full knowledge of the spectroscopic properties of a measurement could, in theory, provide a route to formal traceability for that measurement. The exact nature of the influence of spectroscopic uncertainties on the derived ECV products will vary according to the spectral region being measured and the specific details of the measurement technique being employed – and a series of related gaps have been identified.