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Overview

Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Task 1: Initial trade-off: Cloud-characterisation for uv-vis R.Siddans PM1: RAL, 9 July 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:Task 1: Initial trade-off:Cloud-characterisation for uv-vis R.SiddansPM1: RAL, 9 July 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. • Will form basis of new work in T2 of this study. • 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. Cloud Scenarios • Cloud scenarios based on ground-based radar/lidar data, analyed by CloudNet project (Hogan et al) • Wind fields used to synthesise orbit x-sections from station data provided as fn of time. • Similar data now available from CloudSat/CALIPSO • DARDAR project (Hogan and Delanoe) IWC 1km regridded Distance (km)

  5. Imager retrieval • Based on ORAC, currently used on ATSR, AVHRR, MODIS, SEVIRI etc: • GRAPE, GlobAerosl, CCI-Clouds, CCI-Aerosol, Eumetsat OCA etc: • Uses optimal estimation & retieves • Optical depth at 0.55 µm • Effective radius. • Cloud-top height. • Assumes single, homogenous cloud/aerosol layer of particular type (liquid/ice cloud, maritime / continental / biomass… aerosol) • Cost function can be used to identify type or where single layer assumption not valid (possibly)

  6. Imager retrieval • ORAC currently uses 0.55, 0.67, 0.87, 1.6, 11 and 12µm • only vis/near-ir for aerosol • Extended here to 9 "FDHSI" channels for both cloud & aerosol • 0.55, 0.64, 0.809, 1.63, 3.92, 8.71, 10.8, 11.9 and 13.4 µm • Added aerosol layer height (from ir) to state vector • ORAC Based on RT look-up-tables • Here based on full on-line RT with RAL FM2D to ensure consistency with measurement simulations & facilitate use of additional channels • Here each scene is analysed with 4 particle models: • Liquid cloud, ice cloud, desert aerosol, maritime aerosol • The retrieval with lowest cost function is selected

  7. Imager results true • Single layer cloud / aerosol fits quite well where possible • Mixed layer cloud gives high cost • Generally cost selects correct type • Aerosol noisy (kext & reff) Retrieved (cloud/aerosol only) Cost/type

  8. A-band retrieval scheme • Extinction coefficient profile retrieval as aerosol assessment • Here take scatter type and reff from imager retrieval • Assume spatial resolution 10km cf imager 1km • Divide scenes into cloudy & clear fraction • Cloudmask: Threshold reflectance > 0.2 • In each fraction find type from imager retrieval which is associated with most measured photons. • Then take radiance weighted mean kext and reff for this type as imager representation of cloud/clear fraction. • A-band retrieval run assuming imager type on whole scene if homogenous or "cloudy" fraction if mixed.

  9. A-band retrieval scheme • Standard scheme represents cloud as a scattering profile (CSP) • 2nd scheme implemented: • emulates the approach GOME Operational total column • Cloud as reflecting surface (CRS approach) • Assume cloud is Lambertian surface at elevated atlitude • Assume cloud fraction and type from imager • (operationally fraction from PMDs, type assumed) • Retrieve • Cloud top height • A priori 5±10 km. • Cloud reflectance • A priori 0.05±1 • only applied to 0.6 nm resolution measurements

  10. A-band retrieval results true imager 0.6 nm resolution 3D measurements 1D, SNR=250 1D measurements

  11. A-band retrieval results true imager • Cloud representation reasonable even from low resolution A-band but improves with resolution. 0.6nm resolution 0.06nm resolution

  12. A-band retrieval results true imager • At 1cm-1 much better representation of thin ice cloud + cost OK • SNR 2500 better than 250 3 cm-1 resolution 2km retrieval 1km, SNR=250 1km retrieval

  13. AMFs • AMFs are computed for 3 column amounts • Total • Tropospheric (0-12km) • Boundary- layer (0-2km) • Sub-column AMFs would be used in combination with external info to constrain other layer or profile shape (e.g. model or other wavelengths) • Calculated by perturbing absorber amount & taking ratio of apparent optical depth to actual, vertical optical depth of absorber • AMFs are first at 1km spatial resolution from • True field • Retrieval representation of field (using cloud fraction for sub-pixel representation) • AMF of 10km pixel is given by radiance weighted mean

  14. Imager OK for cloud free & simple cloud • A-band CRS improves results for cloudy conditions

  15. Profile retrieval improves over CRS at 0.6 nm resolution

  16. 0.18 nm slightly better than 0.6 nm

  17. 0.06 nm ~ similar 0.18nm

  18. Conclusions • 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).

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