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)

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?

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

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 https://www.wmo.int/pages/prog/gcos/inde 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.

BK Scientific

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)

Some tangible outcomes to date

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)

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 Q4 2016 on the tiered system, maturity assessment approach and results.

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

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

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  𝑢2 1 + 𝑢2 2 + 𝜎 2

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

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.

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. 21-24)

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.

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

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.

Thanks for your attention www.gaia-clim.eu @gaiaclim