G3.04

G3.04    Limited characterization of the multi-dimensional (spatiotemporal) smoothing and sampling properties of atmospheric remote sensing systems, and of the resulting uncertainties

Gap detailed description

Remotely sensed data are often considered as column-like or point-like samples of an atmospheric variable, e.g., WP2 assumes column and vertical profile measurements of ozone, water vapor etc. at the vertical of the station. This is also the general assumption for satellite data, which are assumed to represent the column or profile at the vertical of the satellite field-of-view footprint in case of nadir sounders, and atmospheric concentrations along a vertical suite of successive tangent points in the case of limb and occultation sounders. In practice, the quantities retrieved from a remote sensing measurement integrate atmospheric information over a tri-dimensional airmass and also over time. E.g., ground-based zenith-sky measurements of the scattered light at twilight integrate stratospheric UV-visible absorptions (by ozone, NO2, BrO etc.) over several hundreds of kilometers in the direction of the rising or setting Sun (Lambert et al., 1997). A satellite limb measurement will actually be sensitive to the atmosphere along the entire line-of-sight towards the photon source, depending on the specific emission, absorption, and scattering processes at play (e.g. von Clarmann et al., 2009). Similarly, in-situ measurements of atmospheric profiles cannot be associated with a single geo-location and time stamp, due for instance to balloon drift (e.g. Seidel et al. 2011). In a variable and inhomogeneous atmosphere, this leads to additional uncertainties not covered in the 1-dimensional uncertainties reported with the data (e.g. Lambert et al., 2011).  A prerequisite for quantifying these additional uncertainties of multi-dimensional nature is not only a quantification of the atmospheric variability at the scale of the measurement (cfr. G3.01), but also a detailed understanding of the smoothing and sampling properties of the remote sensing system and associated retrieval scheme.  Pioneering work on multi-dimensional characterization of smoothing and sampling properties of remote sensing systems and associated uncertainties was initiated during the last decade (e.g., in BELSPO/ProDEx projects SECPEA and A3C and in the EC FP6 GEOmon project), but in the context of integrated systems like Copernicus and GCOS, appropriate knowledge of smoothing and sampling uncertainties, still missing for several ECVs and remote sensing measurement types, has to be further developed and harmonized.

Activities within GAIA-CLIM related to this gap

Addressing this gap is a major objective of WP3, in both tasks T3.1 and T3.2. Results have already been obtained for total ozone columns, and work is ongoing for ozone, temperature, and humidity profiles, and for aerosol columns and profiles. All non-satellite instruments targeted within GAIA-CLIM are addressed. Regarding satellite data, only a selection of current missions are explored. Results will be made available in D3.4 and D3.6, and through the ‘Virtual Observatory’ (T3.3, D3.5 and D3.7). In the long term, this gap will require continued efforts to fully characterize the spatiotemporal smoothing and sampling properties of both new ground-based instruments and upcoming satellite sensors.

Gap remedy(s)

Remedy #1

Specific remedy proposed

Detailed modelling of the measurement process, including multi-D radiative transfer, to quantify the 4-D measurement sensitivity. An example are multi-D averaging kernels for retrieval-type measurements. If appropriate, the results from these detailed calculations can be parametrized for easy and efficient use when calculating the resulting errors and uncertainties for large amounts of data. This uncertainty calculation is done by combining the quantification of the measurement sensitivity with knowledge on the spatiotemporal variability of the atmospheric field.

Measurable outcome of success

Publications and technical notes describing for every instrument and measurement type the full 4-D measurement sensitivity, and the errors and uncertainties resulting from the assumption that a measurement can be associated with a nominal geo-location and time. 

Achievable outcomes

This remedy requires a significant technical and organizational effort from the instrument teams, for which dedicated, though still relatively low (per instrument), resources are required, in particular for code modifications and additions. Addressing this gap for all instruments and ECVs that are part of e.g. the Copernicus system does represent a medium cost (i.e. between 1 and 5 M euro). Moreover, this remedy relies on the willingness of the measurement community to invest time and expertise. 

Relevance

This approach represents the most comprehensive remedy to this gap, and also contributes to remedy gaps G3.02, G3.03, and G3.06. 

Timebound

The time scale for full implementation of this remedy is of the order of years.

Remedy #2

Specific remedy proposed

When Remedy #1 is out of reach, a similar estimate of the multi-D measurement sensitivity can be made in a more pragmatic way based on the measurement principle and physical considerations (e.g. Lambert et al. 2011), or they can be estimated with empirical methods.

Measurable outcome of success

As for Remedy #1

Achievable outcomes

As opposed to Remedy #1, this more pragmatic approach does not require direct involvement by the measurement teams and the cost estimate is significantly lower.

Relevance

While not as comprehensive and accurate a solution as Remedy #1, it would in many cases already allow a good characterization of the (uncertainties resulting from) the measurement smoothing and sampling properties, and therefore be of great value in the context of validation and to close gap G3.06. 

Timebound

Development and first exploitation of these pragmatic “observation operators” can typically be done in less than a year with an investment of 1 FTE.

Gap risks to non-resolution