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O 2 A-band

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O 2 A-band

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  1. 9:00    Welcome, introductions (JL)9:15    Study overview (RS)Consolidation of Requirements by Application 9:40     Height-resolved aerosol (RS)10:10   Cloud-characterisation for UV/visible trace-gas retrievals (RS)(10:40  Coffee) 11:00   Full physics NIR/SWIR Retrievals of Methane and CO (LV)11:30   Water vapour retrievals (KW)12:00   Study conclusions & recommendations (RS)12:30   Discussion 13:00  Close Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument:Final Presentation AgendaESTEC 25 April 2014

  2. Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument:FP, 25 April 2014, ESTECStudy OverviewR.Siddans (RAL), L. Vogel, H. Boesch (Univ. Leicester)K.Weigel, H. Bovensmann (Univ. Bremen)L. Guanter (FUB)

  3. O2 A-band Sun-normalised radiance • O2 A-band contains optically thick molecular absorption lines • Effective scattering altitude of photons (viewed in nadir) varies with wavelength • O2 mixing ratio known therefore measurements can be used to infer scattering profile. • Amount of information depends critically on spectral resolution but also on random and systematic instrumental and forward model errors 0.06nm resolution 0.3 nm resolution Wavelength / nm

  4. Study Overview • Study to consolidate requirements for NIR band considering application areas: • Height resolved aerosol from O2-A (and B) bands (RAL) • Requirement: 0.05 on layer optical depths (NB free-troposphere) • Scattering correction for DOAS retrievals UV/VIS/NIR (RAL) • Requirements: e.g. O3: 20% trop.column; NO2: 10% PBL • Scattering correction for full-physics retrievals from SWIR+NIR joint retrieval (Univ Leicester) • Requirements: CH4: 2% column; CO: 25% • Water vapour (column) retrievals (Univ Bremen) • Requirements: 5% (Climate NRT) or 10% • Vegetation fluorescence to be taken into account (FU-Berlin) • No requirement, not driving • Two main tasks: • Task 1: Initial trade-off: • Focus on trade-off between two options for NIR band • Task 2: Refinement of NIR band specificiations: • Consolidation of requirements for selected option

  5. Early priority to trade-off two current instrument concepts: • Concept A: 0.39 nm resolution, covering O2 A,B-bands + H2O • Concept B: 0.12 nm resolution, covering O2 A-band • Initial trade-off did not result in strong case for MAG to recommend concept A or B • Following industrial contract, only option A currently considered

  6. S5 Specs (concept A) vs GOME & S5P issues: • Need for B-band/H2O coverage • Downlinked spectral range

  7. Consortium Retrieval Schemes • RAL height-resolved aerosol scheme • OE scheme largely developed for Eumetsat and ESA studies (MTG UVS, Capacity, Camelot) • Retrieves aerosol extinction profile, integrated to layer amounts • Instrumental errors quantified by linear mapping • Non-linear version used to test impact of realistic cloud distributions • Leicester OCO full physics retrieval scheme • Retrieves CO2, CH4 from SWIR band, jointly fitting NIR band, and internally representing aerosol as either extinction profile or parameterised profile shape. • Bremen Optimal Estimation DOAS (BESD) scheme • Devloped for full physics SWIR retrievals, to be applied for H2O from NIR, exploiting O2-B for implicit scattering correction.

  8. Geophysical Scenarios • Study based on retrieval simulations conducted for globally representative conditions, based on same scenarios used in SWIR study (A. Butz) • These provide (over land), on 2.8 x 2.8 degree grid: • Profiles of T, H2O • Aerosol profiles (amount + optical models) • Surface spectral albedo • Cirrus optical depth + height • April scenario used exclusively here • SWIR simulations complemented by • Simulated S5 view / solar geometry along orbits, sampled onto the grid • Profiles of optically thick cloud (for cloud/uv application) • Vegetation fluorescence parameters • Scenarios extended over sea by interpolation

  9. Instrumental Errors • Error budgets required for each application as basis for consolidation of requirements. • These largely based on retrieval simulations for key instrumental errors: • Instrument noise (based on the model described above). • The absolute radiometric accuracy (ARA) requirement as specified in the MRTD, and a proposed relaxation. • The impact of scene inhomogeneity (perturbation in the effective spectral response function of the instrument via inhomogeneous illumination of the slit) • Errors in the knowledge of the spectral response function • The relative spectral radiometric accuracy required (RSRA) and/or the effective spectral radiometric accuracy (ESRA). • Inter and intra-band co-location errors (where relevant). • Inter-band variation in the spatial response function shape (SIE). • Errors determined either via full non-linear retrieval simulation (from perturbed measurements) or “linear mapping”: Δx = G Δy • Other limiting “scene dependent” errors (uncertainties not driven by the instrument, e.g. accuracy of spectroscopic parameters, assumed atmospheric profile shapes etc) are estimated either by retrieval simulation or estimated from other sources (previous studies, literature).

  10. Absolute Radiometric Accuracy (ARA) (MR-LEO-UVN-160) • MRTD: • At the MAG the requirement was relaxed to apply only over a given signal level (High-latitude dark at 755nm); • Here ARA mapped as 2 components: • Gain consistent with MRTD (3%) • Offset consistent with proposedrelaxation

  11. Absolute Radiometric Accuracy (ARA) (MR-LEO-UVN-160)

  12. Relative Spectral Radiometric Accuracy (RSRA) • RSRA in MRTD (MR-LEO-UVN-180) requires spectral features in NIR < 0.05% for “low” resolution option, with widths between 0.1 and 7.5nm. • This is very difficult to meet (also true for other bands) • RSRA requirement now replaced by • Effective spectral radiometric accuracy (ESRA), which requires the error at L2 to be smaller than half the user requirement for a given species, to be assessed by linearly mapping instrumental errors via a retrieval gain. • Gain Matrices provided for most L2 species prior to studies • For spectral regions not covered by gain / ESRA, 0.25% RSRA requirement applies. • Gain for height-resolved aerosol from high-resolution option already provided • Gain for SWIR species to NIR band provided by SWIR study • Approach in this study: • Provide gain matrix for NIR H2O, so ESRA could be applied • Assess adequacy of 0.25% RSRA requirement for cloud/UV • NB cloud very non-linear so ESRA approach not suitable

  13. Spectral response function errors • Following approach used in UV+SWIR studies, various perturbations to spectral response function constructed, consistent with requirement that shape be known to within 1% of peak value • Nominal function is box-car convolved with Gaussian, with widths chosen to match the industry provided function • Perturbations • 1% width • 1% asymmetric stretch of slit function, scaled to given maximum deviation from nominal of 1% of the peak • Displace “ghost” of the spectral response – add response function to itself with displacement of +/- 0.5,1 and 1.5 times the FWHM • Scaled to 1% of peak and adjusted to give no change in centre of mass • Analogous to errors from stimulus ghosts in GOME-2 slit-function measurement approach

  14. Spectral response function errors All functions

  15. Scene inhomogeneity -> ISRF error • Aim: simulate “pseudo-noise” from perturbation of spectral response (ISRF) caused by inhomogeneous illumination spectral/along-track dimension • Follow Noveltis approach for S4 study: • Use imager-based data to simulate spatial variations • Sensitivity to spatial sample in along-slit / along-track direction modelled: • Convolve sample with telescope point spread function (PSF); Gaussian 60 microns / 2km • Apply box-car representing slit width; 90 microns / 3km • Convolve with spectrometer PSF + detector width (2km) • Convolve with box-car for along-track motion smear (7km) • Sum of individual sample responses gives homogenous scene ISRF • Inhomogenous SRF from weighting sub-samples by spatial variations • Motion smear large cf slit width; relevant spatial variations will be dominated by simple linear gradients (was more complex for S4) • Impact on spectral response limited to varying gradient in top of “box car” convolved Gaussian – L2 error assessed as error in spectral response fn.

  16. Scene inhomogeneity: S4/Noveltis scene 1

  17. Worst case from Noveltis scenes for S4 (inc. Cloud) • MRTD:

  18. S5 vs S4 slit function perturbations S5 S4

  19. Scene inhomogeneity: S4/Noveltis scene 1

  20. Histograms of relative gradient over 3km After 7x7 km averaging • Based on global AATSR data (Jan, Apr, July, Oct 2008) Cloud-free: 99 %ile 0.87μm All scenes: 99 %ile 0.87μm All scenes Land Sea Cloud-freeLand Sea

  21. Fluorescence • L. Guanter (FUB) provided • Information needed to simulate impact of fluorescence in other schemes (global distribution of fluorescence intensity + representative spectral source function) • Also provided simulations of S5 performance for fluorescence based on scheme demostrated for GOME (Joiner, Guanter et al 2013) • Basic conclusions: • Fluorescence does not impact other retrievals, as long as it is accounted for in L2 scheme • Good prospects for useful fluorescence retrievals from S5, but points to modifying downlinked spectral range

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