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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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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

slide2

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
assessment approach
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
cloud scenarios
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)

imager retrieval
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)
imager retrieval1
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
imager results
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

a band retrieval scheme
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.
a band retrieval scheme1
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
a band retrieval results
A-band retrieval results

true

imager

0.6 nm resolution

3D measurements

1D, SNR=250

1D measurements

a band retrieval results1
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

a band retrieval results2
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

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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
slide14
Imager OK for cloud free & simple cloud
  • A-band CRS improves results for cloudy conditions
conclusions
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).