1. GAIA-CLIM Gap Analysis
The why of the gaps assessment exercise
Martine de Mazière
Royal Belgian Institute for Space Aeronomy (BIRA-IASB)
Peter Thorne
Maynooth University, Ireland
Michiel van Weele
Royal Dutch Meteorological Institute (KNMI)

2. Talk outline GAIA-CLIM project overview
Why is GAIA-CLIM considering gaps?
How is GAIA-CLIM undertaking the gaps assessment exercise?
Where are we in the process?

3. GAIA-CLIM project Three year project (3/2015 - 2/2018)
18 partners, coordinator P. Thorne
€6 million H2020 EO
Objectives
to improve identification and use of non-satellite measurements to characterise, calibrate and validate satellite measurements
to ensure that best metrological practices are followed
Makes use of statistical, modelling and data assimilation tools
Principal outcomes
a Virtual Observatory tool
documentation of gaps  remedies w/prioritisation

4. GAIA-CLIM project’s domain
Those ECVs and additional
variables for which capabilities will be
classified in a system of systems approach
and mapped in GAIA-CLIM. Bolded
variables will, in addition, be further
analysed in terms of measurement
uncertainty mapping under WPs 2-5. The
full list of GCOS ECVs is available at
x.php?name=EssentialClimateVariables.
Note that three atmosphere and three land
variables in italics are considered under
the QA4ECV project, which GAIA-CLIM
will realize synergies with. The three
atmosphere parameters that overlap will be
considered in GAIA-CLIM only in so far
as aspects not already covered under
QA4ECV exist. For any areas of overlap
GAIA-CLIM will make use of QA4ECV
outcomes to avoid any redundancy of effort.

5. BK Scientific

6. GAIA-CLIM scientific components
identify the geographical capabilities and gaps in the existing non-satellite observing systems at the European and at the global scale –
 system of systems approach
 statistics and model-based
improve metrological characterisation of measurements
quantify comparison uncertainties due to co-location mismatches
assess reference data using global assimilation systems
Virtual Observatory for visualisation and access to reference data (co-location database)

7. Some tangible outcomes to date

8. 1. Non-satellite measurement maturity assessment and metadata
System of systems: three fundamental measurement quality tiers are defined
Extension of CORE-CLIMAX CDR maturity assessment to deal with measurement maturity
for >40 candidate high-quality networks:
Measurement maturity assessment completed
Discovery (WIGOS/ISO19115) and measurement (ESA CCI -CF / WIGOS) metadata collected
The strands for assessing
measurement maturity herein are as follows:
• Metadata
• Documentation
• Uncertainty characterisation
• Public access, feedback, and update
• Usage
• Sustainability
• Software (optional)

9. 1. Non-satellite measurement maturity assessment and metadata
Service available via CNR partner in restricted mode presently
Service to be integrated into the Virtual Observatory
Paper to be drafted in Q on the tiered system, maturity assessment approach and results.

10. 2. Metrological traceability of reference data
1st: measurement uncertainty quantification
2nd: production of traceability chains for
various lidars, MWR, FTS, UV/vis, MAX-DOAS / Pandora, and GNSS-PW,
at level of
physical model of the measurement
&
data product
3rd ongoing: make traceability chains more interactive

11. Example of traceability chain See also gaia-clim.eu/
FTIR processing chain
BIRA, Belgium
Meteo station measurements (surface temperature, wind speed, surface pressure, RH, solar sun intensity)
LINEFIT
Raytracing tool LOS calculation
FTIR HBr cell spectra measurements
GEOMS FTIR
template, TAV
Solar spectroscopy
Spectroscopic data (HITRAN)
Quality filtering conditions on retrieval output: quality of fit, DOF’s, ...
ILS
Temperature Pressure
NCEP
GEOMS HDF
creation routine
Retrieval software
Retrieval quality filtering
Retrieval output
GEOMS FTIR DATA
FTIR
Absorption spectra
Measurement Quality Filtering
FTIR
Absorption spectra
Retrieval strategy (microwindow, regularization strength, interfering species, …)
Ephemerides
apriori
SNR
(SZA, AZIM)
(WACCM v6,
sonde, aircraft,…)
Uncertainty input (co)variance matrices (NCEP data, WACCM data, SZA, …)
FTIR Physical model chain
Solar light Spectrum (uncalibrated) Intensity vs wavenumber
For atmospheric gas column/ profile estimation
Key
Solar Radiance
Solar tracker
Raw Voltage or Ampere signal signal from light source detector
Low pressure gas cell (HCL, HBr, N2O,…) in light beam
Fast Fourier Transform
Solar light Spectrum with cell (uncalibrated) Intensity vs wavenumber
Main process
Interferometer Laser
Detectors
Interferogram: Intensity vs path difference
Decision process
Instrument/ Physical items
Raw Voltage or Ampere signal form laser light
Solar light Spectrum without cell (uncalibrated) Intensity vs wavenumber
Lamp
For Instrument line shape estimation
Non-data numerical object
Terminal object
Physical Quanity
Lamp Spectrum with cell (uncalibrated) Intensity vs wavenumber
Dataset
Subprocess/ subroutine
Lamp Spectrum without cell (uncalibrated) Intensity vs wavenumber

12. 3. Quantification of co-location mismatch uncertainty
Apply 2D/3D observation operators set up with observation metadata (measurement geometry, geolocation, date/time, SZA,…),
onto atmospheric fields at appropriately fine resolution,
to simulate the observations,
their space/time mismatches
and their difference.
𝑚 1 − 𝑚 2  𝑢 𝑢 𝜎 2

13. Established in OSSSMOSE: Observing System of Systems Simulator
Published by Verhoelst et al. in AMT, 2015

14. 4. GRUAN data processor
Takes a GRUAN Vaisala RS92 radiosonde measured profile, which includes a metrologically traceable uncertainty estimate at every point in the profile.
Converts the profile and its uncertainty into an equivalent TOA radiative profile and uncertainty
Enables a comparison between the satellite and GRUAN measurement both at level-2 and level-1b.

15. Principal Outputs (1/2) Virtual observatory
allowing users to explore (visually, metadata) and download co-locations between satellite and non-satellite measurements
initial version will be available in November at GAIA-CLIM 2nd users workshop (Brussels, Nov )

16. Principal Outputs (2/2) Gaps Assessment and Impacts Document (GAID)
Cf. call text:
research is needed to assess gaps in remote observation availability and suitable approaches for defining virtual observation constellations.
Appropriate calibration and validation of data is to be assessed, charting the campaigns that will be needed to cover the climate change monitoring needs in years to come from remote sensing data gathered over land, water and icy surfaces.

17. GAID
A living document with 5 scheduled versions during project lifetime
& associated interactive Web version
Updates reflect internal (from underlying WPs) and external (from user workshops and an initial user survey) inputs (essentially bottom-up approach)
Document led by KNMI (Michiel van Weele)
see more details in his talk
Starting in March 2017 from GAID : create a set of recommendations for future (research, …) work to address the identified gaps
Led by NUIM (P. Thorne)  see more details in his talk

18. Summary
GAIA-CLIM is concerned with improving utility of non-satellite observations to characterise satellite data (focusing on a selection of atmospheric, ocean and land ECVs)
In response directly to the call for proposals we included an important outreach WP (led by M. De Mazière) that includes the assessment of gaps  to lead to recommendations at project end. Gaps are restricted to domain of project.
Gap assessment is a living process and we are hoping to learn from ConnectinGEO (and we hope vice-versa) to improve our process and to look at the gaps in the wider context of GEOSS.

19. Thanks for your attention
@gaiaclim