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Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Task 1: Initial trade-off:

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Presentation on theme: "Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Task 1: Initial trade-off:"— Presentation transcript:

1 Rutherford Appleton Laboratory 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

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 (Eumetsat Contract 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 O 3 (trop), BrO, NO 2, CH 2 O 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) S m which 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 Distance (km) IWC 1km regridded 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)

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 Retrieved (cloud/aerosol only) Cost/type Single layer cloud / aerosol fits quite well where possible Mixed layer cloud gives high cost Generally cost selects correct type Aerosol noisy (k ext & r eff )

8 A-band retrieval scheme Extinction coefficient profile retrieval as aerosol assessment Here take scatter type and r eff 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 k ext and r eff 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 true imager A-band retrieval results 0.6 nm resolution 1D measurements 3D measurements 1D, SNR=250

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

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

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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