Gap reference list

Gap reference list

This is the full list of gaps identified in GAIA-CLIM to date, with a SMART analysis of each gap. Details of how and when such SMART remedies may be addressed for each gap are included in the full descriptions. The gaps each have unique gap identifier numbers of the form: X.XX, and full descriptions can be found at gaia-clim.eu/wiki/gX.XX; or by following the links in the Gap Identifiers below. We encourage user-input on the gaps, so if you can update our knowledge on existing gaps, or you would like to suggest the inclusion of additional gaps, please contact us using the "feedback" link next to the relevant gap, or the feedback button in the given gap description. We encourage you to use our template for external input which can be found here. You can also provide feedback using the contact form, using the 'GAID feedback' category.

Gap identifier

Gap name

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G1.02

Unknown suitability of measurement maturity assessment

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G1.03

Missing evaluation criteria for assessing existing observing capabilities

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G1.04

Lack of a comprehensive review of current non-satellite observing capabilities for the study of ECVs in atmospheric, ocean and land domains

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G1.05

Lack of unified tools showing all the existing observing capabilities for measuring ECVs with respect to satellite spatial coverage

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G1.06

Lack of a common effort in metadata harmonization

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G1.07

Need for a scientific approach to the assessment of gaps in the existing networks measuring ECVs.

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G1.08

Evaluation of the effect of missing data or missing temporal coverage of fully traceable data provided by ground-based networks

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G1.09

Limited availability of quantitative CO profiles

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G1.10

Insufficiently traceable uncertainty estimates

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G1.11

Traceable uncertainty estimates from baseline and comprehensive networks

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G1.12

Propagate uncertainty from well-characterized locations and parameters to other locations and parameters

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G1.13

Uncoordinated lidar and microwave radiometer water vapor  measurements

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G1.14

Currently limited aircraft measurements in Eastern Europe

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G1.15

Northern Hemisphere bias in NDACC and PANDORA network sites distribution

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G2.01

24/7 operation of aerosol lidar

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G2.02

Lidar incomplete altitude coverage

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G2.03

Incomplete collocation of sun and moon photometers with day and night time aerosol lidars

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G2.04

Missing continued intercomparison with reference systems

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G2.05

Lack of rigorous aerosol lidar error budget availability

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G2.06

Need of multi-wavelength Raman lidar

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G2.07

Need for assimilation experiments using lidar measurements

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G2.08

Reducing water vapour lidar calibration uncertainties using a common reference standard

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G2.09

Continuous water vapour profiles from Raman lidars limited during daytime

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G2.10

Tropospheric O3 profile data is limited

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G2.11

Lack of rigorous tropospheric O3 lidar error budget availability

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G2.12

Lack of rigorous temperature lidar error budget availability

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G2.13

Missing microwave standards maintained by National/International Measurement Institutes

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G2.14

Lack of a comprehensive review of the uncertainty associated with MW absorption models used in MWR retrievals

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G2.15

Lack of unified tools for automated MWR data quality control

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G2.16

Missing agreement on calibration best practices and MWR instrument error characterization

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G2.17

Lack of a common effort in homogenization of MWR retrieval methods

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G2.18

Better agreement needed on systematic versus random part of the uncertainty in FTIR measurements and how to evaluate each part

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G2.19

Line of sight and vertical averaging kernel are only approximations of the real 3D averaging kernel of a FTIR retrieval

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G2.20

Too large spectroscopic uncertainties in FTIR H2O and CH4 products

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G2.21

Current spectroscopic databases contain uncertainties specifically effecting TCCON retrievals of CH4 and CO2

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G2.22

FTIR cell measurements carried out to characterize ILS have their own uncertainties

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G2.23

Possible SZA dependence in the FTIR CH4 retrievals during polar vortex overpasses

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G2.24

Lack in in-situ calibration of CH4 and CO2 FTIR measurements

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G2.26

Uncertainty in O3 cross sections used in the spectral fit for DOAS, MAX-DOAS and Pandora data analysis

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G2.27

Random uncertainty in total column O3 retrieved by UV-vis spectroscopy dominated by instrumental imperfections impacting on the spectral fit calculations

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G2.28

Uncertainty in a priori profile shape for AMF calculations for zenith sky ozone retrievals

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G2.29

Uncertainty in the vertical averaging kernels used for DOAS total column ozone retrievals

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G2.30

Lack of uncertainty quantification for Pandora O3 measurements

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G2.31

Lack of understanding of the information content of MAX-DOAS tropospheric O3 measurements

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G2.32

Better characterization of the different MAX-DOAS tropospheric O3 retrieval methods needed

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G2.33

Lack of in-depth understanding of random and systematic uncertainties of MAX-DOAS tropospheric O3 measurements

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G2.34

Uncertainties of ZTD for GNSS-PW, given by a 3rd party without full traceability

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G2.35

Sites with high/low albedo and hot spot monitoring

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G3.01

Incomplete knowledge of spatiotemporal atmospheric variability at the scale of the measurements and their co-location

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G3.02

Limited quantification of the impact of different co-location criteria on comparison results

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G3.03

Missing generic and specific standards for co-location criteria in validation work

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G3.04

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

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G3.05

Representativeness uncertainty assessment missing for higher-level data based on averaging of individual measurements

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G3.06

Missing comparison error/uncertainty budget decomposition including errors/uncertainties due to sampling and smoothing differences

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G4.01

Lack of traceable uncertainty estimates for NWP and reanalysis fields & equivalent TOA radiances - relating to temperature

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G4.02

Lack of traceable uncertainty estimates for NWP and reanalysis fields & equivalent TOA radiances - relating to humidity

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G4.07

Error correlations for reference sonde (GRUAN) measurements.

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G4.08

Estimates of uncertainties in ocean surface emissivity models.

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G4.09

Estimates of uncertainties in land surface microwave emissivity atlases.

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G4.10

Estimates of uncertainties in land surface infrared emissivity atlases.

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G4.11

Geographical sampling of reference in-situ data

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G5.01

Access to data in multiple locations with different user interfaces constitutes a barrier to usage and makes use in general difficult

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G5.02

Access to and use of reference and satellite data provided in different data formats and structures (e.g. granularity of data) prevents easy exploitation

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G5.03

No common source for co-located data exists which prevents use of reference data to validate reference measurements to each other and to evaluate satellite data.

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G5.06

Extraction, analysis and visualization tools to exploit the potential of reference measurements are currently only rudimentary. This in particular includes tools to display uncertainty of the comparison results due to differences in sampling and so called smoothing error.

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G5.07

Incomplete development and/or application and/or documentation of an unbroken traceability chain of Cal/Val data manipulations for atmospheric ECV validation systems prevents progress in the characterization of satellite products.

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G5.09

A readily accessible online tool to perform radiative transfer calculations to transfer reference measurements of ECVs including their uncertainty estimates into the space measurement space is missing to enable characterisation of satellite measurements.

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G5.10

Characterisation of different types of uncertainty has not been systematically addressed per ECV, e.g., for some ECVs a full measurement uncertainty chain has been established, but smoothing uncertainty has not been considered or vice versa. This prevents and potentially delays inclusion of various instrument/ECV combinations into the Virtual Observatory.

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G5.11

Non operational provision of reference measurement data and some (L2) satellite products may prevent use in Copernicus operational product monitoring

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G6.01

Dispersed governance of high-quality measurement assets leading to gaps and redundancies in capabilities and methodological distinctions

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G6.02

Geographically dispersed observational assets reduce their utility for satelite calibration/validation.

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G6.03

Lack of dedicated funding for targeted observations to coincide with satellite overpass

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G6.04

Mixed level of user experience with using uncertainty information

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G6.05

Future support for GRUAN-processor

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G6.06
a) Lack of dedicated funding for fast-delivery of targeted observations for satellite calibration/validation
b) Lack of structural funding for station maintenance, data acquisition and initial analysis

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G6.07
Different data policies in different networks harms the use of complementary data from different networks.

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G6.08
a) INSPIRE Application of INSPIRE Implementing Rules to atmospheric and any other 3D/4D-data is not straightforward w.r.t. dimensionality, quality etc.
b) INSPIRE   Where do data of one Member State end up which acquired in another Member State and/or is derived from satellite?

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G6.09 Responsibility for observations in developing countries (Africa - Asia - S. America)

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G6.10 An unlimited growth of data portals, metadata standards and formats might make data discovery and access increasingly difficult.

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G6.11 The possible gradual loss of island radiosonde stations.

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