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Overview

Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Initial trade-off: Cloud-characterisation for uv-vis R.Siddans MTR: Estec, 1 October 2013. Overview. Initial assessment based on work in the Eumesat Study for then proposed MTG UVN sounder:

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Overview

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  1. Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument:Initial trade-off:Cloud-characterisation for uv-vis R.SiddansMTR: Estec, 1 October 2013

  2. Overview • Initial assessment based on work in the Eumesat Study for then proposed MTG UVN sounder: • R. Siddans, B.G. Latter, B.J. Kerridge, Study to Consolidate the UVS Mission Requirements for the Oxygen A-band (EumetsatContract No. EUM/CO/05/1411/SAT), 2007 • This provided basis for later studies re height resolved aerosol (ACOR, CAPACITY, CAMELOT), but also studied cloud/trace-gas application in some detail. • Basis of approach is to • Define realistic cloud senarios & simulate measurements • Perform imager and A-band spectrometer cloud retrievals • Determine implied errors in the uv-vis trace gases by quantifying air-mass-factor errors stemming from the retrieved cloud representation • Various instrument resolutions tested

  3. Assessment approach • UVS mission – tropospheric trace gas retrievals • O3(trop), BrO, NO2, CH2O give rise to optically thin absorption signatures: DOAS is applicable: • Slant column is fitted to measured spectra • Vertical column estimated by dividing by air-mass factor (AMF) Smwhich is calculated using an RTM (assuming scattering profile) • Errors in modelled scattering only enter via the AMF calculation • Simulate errors due to cloud/aerosol by evaluating error in AMF

  4. Conclusions from Eumetsat Study • Realisitic end to end simulations of modelling scattering profile for uv-vis retrievals based on imager and A-band retrievals conducted in Eumetsat A-band study • These indicate • Imager retrieval functions well for simple cloud layers • AMF adequate in cloud-free (aerosol) & simple cloud cases • A-band cloud-as-reflecting-surface improves AMFs • A-band scattering profile retrieval improves further • A-band instrument needed to mitigate scattering profile errors • High resolution, low error instrument demonstrates superior cloud profile retrieval, however AMFs do not improve significantly • For application to characterise AMFs for uv, then A-band with 0.2-0.6nm resolution, signal to noise ~250 (moderate reqs on other instrumental error sources).

  5. Initial assessment for S5 • Non-linear simulations too time-consuming for T1 • Performed linear simulations for concept A (A-band only) based on simple cloud-as-reflecting boundary (CAB) assumption (like FRESCO) • Cloud fraction and height retrieved assuming known surface cloud “albedo”. • Linear simulations performed for wide range of observing geometry, fraction and height, surface albedo • Errors in these parameters mapped into AMFs for BL, lower troposphere and full troposphere ozone, assuming the CAB assumption correct • Errors mapped as aerosol retrieval except 0.01 albedo mapped instead of albedo spatial variation • Results interpolated to geophysical conditions as per aerosol simulations

  6. “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). • 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

  7. Profile AMFs

  8. Results for cloud height (cloud fraction 0.2) Nominal Signal:Noise

  9. Results for cloud height (cloud fraction 0.2) Signal:Noise =50 (at best)

  10. Results for cloud fraction (cloud fraction 0.2)

  11. Results for 0-2 km AMF (cloud fraction 0.2)

  12. Results for 0-2 km AMF (cloud fraction 0.01)

  13. Results for 0-6 km AMF (cloud fraction 0.2)

  14. Results for 0-12 km AMF (cloud fraction 0.2)

  15. Results for 0-2 km AMF (cloud fraction 0.2)

  16. Results for 0-2 km AMF (cloud fraction 0.01)

  17. Conclusions • Initial simulations for cloud retrieval indicate • Noise not at all critical (SNR=50) sufficient • ARA errors lead to 10s% errors in BL AMF • Would be more critical for NO2 over land • This needs simulating explicitly to confirm (higher albedo in fit window, less Rayleigh different profile shape etc) • Resolution not critical for this application so long as simple cloud representation assumed • May still be worth re-assessing capability of high resolution instrument to enable more complex representation (cloud-top, cloud base, COT) • For T2 depends on relative priority

  18. 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

  19. 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.

  20. 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!

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

  22. Global histograms for scenes with 0.9 > AMF > 0.2 AMF itself Error from 10%increase in area Error due to 1.4km shift Error due to IE varying from 60-65% (Fixed FWHM) Error due to IE varying from 70-75% (Fixed FWHM)

  23. Global histograms for scenes with AMF > 0.5 AMF itself Error from 10%increase in area Error due to 1.4km shift Error due to IE varying from 60-65% (Fixed FWHM) Error due to IE varying from 70-75% (Fixed FWHM)

  24. Global histograms for scenes with AMF > 0.8 AMF itself Error from 10%increase in area Error due to 1.4km shift Error due to IE varying from 60-65% (Fixed FWHM) Error due to IE varying from 70-75% (Fixed FWHM)

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

  26. Global histograms for scenes with 0.9 > AMF > 0.2 AMF itself Error from 10%increase in area Error due to 1.4km shift Error due to IE varying from 60-65% (Fixed FWHM) Error due to IE varying from 70-75% (Fixed FWHM)

  27. Conclusions • 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 2%) • 20% spatial shift causes errors in AMF (relative error in gas column) less than 10% aprox. 90% of the time. • Errors can be larger in certain regions • variation of 90%ile is between 6 and 15% geographically • Currently expect error to scale to 30% shift but will test this explicitly • Also testing statistics for 7km box car to judge impact of the smooth S5 SRF • Smoothness of SRF leads to possibility of mitigating errors by interpolating to known co-registration mismatch… • Effect on NO2 BL AMFs to be tested explicitly

  28. Assessment of co-registration errors

  29. Trace-gas requirements from MRTD • O3: 10 % PBL, 20% free trop; 25% trop column • NO2: 10 % PBL, 20% free trop; 1.3e15 mol/cm2 trop. column • CH2O: 20% PBL, 20% free trop; 1.3e15 mol/cm2 trop. column

  30. Maps for scenes with 0.9 > AMF > 0.2

  31. Maps for scenes with 0.9 > AMF > 0.2

  32. Maps for scenes with 0.9 > AMF > 0.2

  33. Maps for scenes with 0.9 > AMF > 0.2

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