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Study Overview

Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Summary for S4/5 MAG Meeting#8 15-16 October 2013, ESTEC R.Siddans (RAL), L. Vogel, H. Boesch (Univ. Leicester) K.Weigel, H. Bovensmann (Univ. Bremen) L. Guanter (FUB). Study Overview.

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Study Overview

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  1. Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument:Summary for S4/5 MAG Meeting#815-16 October 2013, ESTECR.Siddans (RAL), L. Vogel, H. Boesch (Univ. Leicester)K.Weigel, H. Bovensmann (Univ. Bremen)L. Guanter (FUB)

  2. Study Overview • Trade-off two current instrument concepts & consolidate requirements • Concept A: 0.39 nm resolution, covering O2 A,B-bands + H2O • Concept B: 0.12 nm resolution, covering O2 A-band • Application areas addressed: • 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) • Requiremenst: 5% (Climate NRT) or 10% • Vegetation fluorescence to be taken into account (FU-Berlin) • No requirement, not driving

  3. Concept B 0.12nm resolution O2-A Band Concept A 0.39nm resolution; 2x better throughput O2-A Band O2-B Band H2O

  4. Fluorescence Input provided by Luis Guanter (FUB) Mainly to be used in simulations by RAL, Leicester, IUP However simulations of fluorescence retrieval performance also provided Currently Fluorescence not a driving application If it were, this could motivate extending coverage of concept B to lower wavelength (stronger Fraunhofer lines more fluorescence) Concept B

  5. OnlyFraunhofer-lines Fraunhoferlines + O2A/H2O • PureFraunhofer line retrieval (GOSAT-like, linear forward model) • Fast, simple, highaccuracy • Can beeasilyimplemented as a pre-processingsteptoestimateFs (prior info. forotherretrievals in O2A) • Fraunhofer + Atmosphericlines (GOME2-like, non-linear forward model)  more difficultimplementation, pronetobiases, highprecision • Highspectralresolutionpreferredtospectralcoverageforfluorescenceretrieval!

  6. Height-resolved Aerosol Application RAL OE scheme developed for previous Eumetsat and ESA studies Retrieves aerosol extinction profile, integrated to layer amounts Wavelength calibration, linear surface albedo dependence, spectral response function width & fluorescence all fitted in retrieval Other instrumental errors quantified by linear mapping Aerosol performance very dependent on view / solar geometry due to variations in aerosol phase function, light path for aerosol light-path for O2 absorption etc. Surface albedo Assumed aerosol type + size (asymmetry, single scatter albedo etc) 0.05 requirement cannot be met under all observing conditions Difficult to concisely summarise performance and optimise inst/L1 requirements without considering many conditions Results presented along S5 orbits based on many individual simulations Use SWIR study scenarios from A. Butz as basis thought NIR study

  7. Weighting function concept B

  8. Retrieval errors for favourable geometry (LZA=60,SZA=60,RAZ=90) With H2O modelled and retrieved (But consider using H2O unrealistic / high risk)

  9. Retrieval Simulation results • Estimated Standard Deviation (ESD) = retrieval precision (random noise) for Free-tropospheric column Concept A (A Band) Concept B Concept A (A+B Bands)

  10. Retrieval Simulation results • 1% error in instrument spectral line-shape (ILS) = spectral response function • If not fitted in retrieval Concept A (A Band) Concept B Concept A (A+B Bands)

  11. Retrieval Simulation results • Error due to 2% shift in spatial response with 2nd order wavelength dependence • (If not fitted / modelled) Concept A (A Band) Concept B Concept A (A+B Bands)

  12. 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

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

  14. Retrieval Simulation results • ARA relaxation, considered as radiometric offset error (assuming High-Lat Dark limit) Concept A (A Band) Concept B Concept A (A+B Bands)

  15. Conclusions (Aerosol) Height resolved aerosol retrievals improve with increasing (finer) spectral resolution, even considering an instrument with fixed total throughput. Dependence on geometry large – no concept compliant over whole swath Concept B clearly preferred Better performance over more of swath Option A only competitive if both O2 A and B bands used Even then sensitivity to instrumental errors larger than concept B Introduces need to model spectral dependence of aerosol optical properties For option B relaxation of ARA requirement (to HL-dark level) seems acceptable (if mapped as offset); This is not the case for concept A. Sensitivity to spectral response function errors seems well mitigated by retrieval. Issue now is spectral dependence in ILS knowledge/stability Still need to consider requirement for high-spatial sampling observations (though expect to be able to mitigate for this application, based on S4)

  16. NIR / UV-VIS Application • RAL Simulations based on retrieval scheme developed for Eumetsat A-band study • Basis of approach is to • Define realistic cloud scenarios & simulate measurements • Perform cloud retrievals used relatively simple cloud model • Determine implied errors in the uv-vis trace gases by quantifying air-mass-factor (AMF) errors from the retrieved cloud representation • Eumetsat study concluded • A-band cloud-as-reflecting-surface improves AMFs cf VIS/TIR imager • A-band scattering profile retrieval improves further • For application to characterise AMFs for uv, then A-band with 0.2-0.6nm resolution, signal to noise ~250 sufficient. • High resolution, low error instrument demonstrates superior cloud profile retrieval, however AMFs do not improve significantly

  17. “AMFs” • Here the term AMF refers to the relative sensitivity of the TOA measurement to the given trace gas column, compared to that ignoring the presence of cloud but calculated for a surface albedo (in the DOAS window) which matches the apparent albedo of the scene (internal closure). • (So far) cloud assumed to be geometrically thin so no light-path enhancement in the cloud modelled (this happens near the top of vertically extended cloud) • I.e. Effect of cloud generally overestimated (errors too) • Whatever the errors in cloud, the cloud “albedo” effect assumed to be accounted for so relative cloud AMFs generally vary from 0-1 (cloud really only obscures). • 0 = column completely obscured by cloud • 1 = effect of cloud negligible

  18. Retrieval Simulation results • “FRESCO” style retrieval (fraction + height of reflecting boundary) • Concept A (A-band) with max SNR=50 • True cloud fraction = 0.2 • True cloud height = median Calipso opaque cloud-top

  19. Retrieval Simulation results • “FRESCO” style retrieval (fraction + height of reflecting boundary) • Concept A (A-band) with max SNR=50 • True cloud fraction = 0.2 • True cloud height = median Calipso opaque cloud-top • Abs.radiometric accuracy significant error for this cloud retrieval approach

  20. Co-registration requirements • These identified as challenging at S4/5 MAG, proposal to relax to 0.3 inter (keep 0.1 intra for NIR)

  21. Previous assessment of co-registration errors • Based on work done for Camelot (Veefkind) and an update • MODIS 1km cloud optical thickness data is user to study inter-band co-registration. 1km data is sampled to various FOV sizes (5,10,20km). • The PDF of the absolute difference in effective cloud fraction for particular shifts is studied, then the fraction of pixels meeting requirement (on error in fraction) is given as function of co-alignment error (up to 25%). • Finds that spatial resolution is not too important when co-alignment defined relative to the FOV size • Fraction of pixels meeting given requirement level is quite linear with relative error in FOV size • The study does not quantify the impact of co-registration errors at L2.

  22. Inter-band co-registration & SRF errors • Error in cloud characterisation from NIR in uv/visible retrieval caused by co-registration errors between bands, coupled to cloud variability. • Variability in cloud characterised using AATSR Cloud CCI L1 data • Cloud optical thickness (COT) • Cloud height (assuming geometrically thin) • Typically higher than A-band effective height (cloud effect overestimated) • Cloud phase • Cloud particle effective radius • AMFs computed at 1km resolution, then “averaged” over shifted and perturbed spatial response functions • Averaging of sensitivity is radiance weighted, so small change in fraction can give large change in AMF (more photons from cloudy fraction) • Statistics generated from 1 year of global data

  23. Spatial Response Functions • A relaxation of integrated energy (IE) requirement proposed to the MAG such that IE of the spatial point-spread-function (PSF) within an area of 1 spatial sampling distance (SSD) squared could vary spectrally in the range 68-76% (previously 70-75%). • SRFs created by convolving box-car and Gaussian functions…no sharp edges!

  24. 0-2 KM Relative AMFs for box-car cloud Nominal SRF ~ Box car SRF

  25. Histogram of errors from 20% SSD shift

  26. Global histograms for scenes with AMF > 0.2 AMF itself Error due to 1.4km shift Error from 10%increase in area Error due to IE varying from 60-65% (Fixed FWHM) Error due to IE varying from 70-75% (Fixed FWHM)

  27. Geographical variations of 90% ile

  28. Conclusions (NIR/UV) • Initial simulations for cloud retrieval indicate • Noise not at all critical (SNR=50 sufficient) • ARA errors lead to ~10% errors in BL AMF (inc NO2) • Resolution not critical so long as simple cloud representation assumed • Is there any expectation that more complex cloud (profile) retrieval is needed ? (In this case higher-resolution band could be justified) • 5% changes in IE of SRF or 10% change in SRF width generally lead to low errors in cloud characterisation of UV/VIS from NIR (AMF errors rarely exceed 3%) • 20% spatial shift causes errors in AMF (relative error in gas column) less than 10% approx. 90% of the time; Would be 83% of the time for 30% shift (scaling) • Errors can be larger in certain regions • variation of 90%ile of error due to 20% shift is between 6 and 18% geographically • Smoothness of SRF leads to possibility of mitigating errors by interpolating if co-registration known • Degradation to 30% co-registration not at all desirable but not affect the majority of scenes and it may be possible to mitigate some of those affected

  29. H2O Application • Bremen Optimal Estimation DOAS (BESD) scheme • Developed for full physics SWIR retrievals, applied to H2O from NIR, exploiting O2-B for implicit scattering correction. • Follows from heritage of SCIA retrievals using AMCDOAS (Noel et al 2005) • End-to-end non-linear simulations based on SWIR-study scenarios • Includes unknown aerosol and cirrus properties which are fit using effective scattering profile parameters, exploiting proximity of O2-B Band to the H2O features • Over land (relatively high surface reflectance) NIR has sensitivity to PBL, potentially adding information to that from IASI-NG

  30. Retrieval performance (in presence of unknown cirrus and aerosol) meets requirements, including most demanding 5% level, under most conditions (excluding mountains). Effect of ARA gain minor (offset to be tested) • Results over sea also possible but with reduced sensitivity to PBL

  31. H2O NIR vs SWIR NIR for April only -2.28%, σx = 8.02% • Only Concept A offers H2O which is compliant with requirements but does not add (over land) to SWIR

  32. NIR / SWIR Application • Measured radiance spectra are non-linear function of atmospheric parameters • retrieval is performed iteratively by alternating calls to: • Forward Model describes physics of measurement: • Multiple-scattering RT • Instrument Model • Solar Model • Inverse Method estimates state: • Rodger’s optimal estimation technique • XCH4, XCO and its error is computed from retrieved state after iterative retrieval has converged

  33. Typical State Vector

  34. Instrumental setup Concept A: NIR1 NIR2 SWIR1 SWIR3 Concept B: NIR SWIR1 SWIR3

  35. Quality-Filtering Retrievals Only converged retrievals are used: • Number of converging iteration steps ≤ 12 • Number of diverging iteration steps ≤ 5 Additional post-processing quality filter: • Χ2 < 1 per spectral band • CH4 error < 0.4% • Retrieved AOD < 0.2 • Retrieved AOD+COD < 0.3 • Surface albedo at O2 bands < 0.7 (removes snow and ice)

  36. Concept A Concept B All soundings 10 0 0 10 converged soundings 0 0 10 10 filtered soundings 0 0 10 10

  37. Concept A Concept B • Bias is similar for both concepts • Concept B shows a higher random error over Asia

  38. Summary of Results

  39. Conclusions (NIR/SWIR) Comparing all converged retrievals: • The number of converged retrievals and retrievals that passed quality filter is higher for concept B • variability of simulated aerosols vs. the two opposing types used in the retrieval and the lesser constrain by the missing O2-B band. • Mean bias and standard deviations for CH4 is larger for concept B • Concept A has higher precision • Concept B has greater coverage • These conclusions hold after applying the filter Comparing converged retrievals present in both concepts: • Mean biases become very small for both concepts • Comparing only standard deviation of biases, concept A performs marginally better (more pronounced when filter is applied) • Precision of concept A is better by ~ 30%, but again, difference in absolute numbers is small • Differences cannot be unambiguously assigned to single parameter • Different numbers of soundings pass the filter for both concepts; therefore direct comparison is difficult, but the general trend remains Comparing converged and filtered retrievals present in both concepts: • Only soundings remain for which simulations and retrievals (aerosol properties) are most similar • Differences between concepts become negligible

  40. Summary: Concept A vs Concept B • Height-resolved aerosol retrieval to meet 0.05 AOD requirement is very challenging (and not demonstrated by previous mission). • Requirement cannot be met for whole swath (resolution not high enough) but can be for relatively low sun/view (AMF=4) • Concept B has clearly better performance than concept A unless risk of combining oxygen A+B bands accepted • (Concept A class of observations available for decades from GOME/SCIA but not yet exploited in this way) • Concept A is the only instrument offering NIR H2O • This may be valuable over land as in principle adds to information from IASI • But similar / better information over land from SWIR • NIR offers H2O over sea as well, but with limited near-surface sensitivity • Concepts A & B seem comparable for full-physics SWIR retrievals of CH4: Concept B leads to more convergent retrievals but the extra results have higher errors (still mainly compliant however). • Both concepts offer fluorescence; Concept B more noisy (extension of coverage down to 748 nm would be preferred)

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