Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument:
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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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Overview 4535567

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


Overview 4535567

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


Overview 4535567

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


Overview 4535567

  • Imager OK for cloud free & simple cloud

  • A-band CRS improves results for cloudy conditions


Overview 4535567

  • Profile retrieval improves over CRS at 0.6 nm resolution


Overview 4535567

  • 0.18 nm slightly better than 0.6 nm


Overview 4535567

  • 0.06 nm ~ similar 0.18nm


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).


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