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Simulation of impact of inhomogeneity

Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Simulation of impact of scene inhomogeneity PM2. 28 February 2014, Bremen R.Siddans (RAL),. Simulation of impact of inhomogeneity.

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Simulation of impact of inhomogeneity

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  1. Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument:Simulation of impact of scene inhomogeneityPM2. 28 February 2014, BremenR.Siddans (RAL),

  2. Simulation of impact of inhomogeneity • Aim: simulate “pseudo-noise” from perturbation of spectral response (ISRF) caused by inhomogeneous illumination spectral/along-track dimension • Follow Noveltis approach for S4 study: • Use imager-based data to simulate spatial variations • Sensitivity to spatial sample in along-slit / along-track direction modelled: • Convolve with box-car for along-track motion smear (7km) • Convolve sample with telescope point spread function (PSF); Gaussian 60 microns / 2km • Cut-off (multiply) with box-car representing slit width; 90 microns / 3km • Convolve with spectrometer PSF; Gaussian 57 microns / 2km • ? Convolve for polarisation scrambler; Gaussian 150 microns / 5km ? • Convolve with box-car for detector width (20 microns / 1km) • Sum of individual sample responses gives homogenous scene ISRF • Inhomogenous SRF from weighting sub-samples by spatial variations • Motion smear large cf slit width; relevant spatial variations will be dominated by simple linear gradients (was more complex for S4)

  3. Simulation of impact of inhomogeneity • S4 study based on 500m MODIS imagery (for few scenes) • L1 errors estimated by convolving set of monochromatic spectra, tabulated as fn of surface albedo, computed for a single atmosphere / view geometry • 500m resolution not needed given 7km smear / across-track resolution. • Inhomogeneity in scene driven by cloud, but such scenes not relevant for NIR/SWIR application (or aerosol). • 2km resolution MODIS albedo climatology could give spatial variations in surface albedo globally – could generate L1 errors / stats for SWIR grid • 1km resolution AATSR radiances convenient for computing impact for all conditions (including cloud) – again L1 errors / stats computed for SWIR grid • Sample error spectra could be extracted and mapped (if still feasible and not clearly unnecessary) onto H2O (Bremen), NIR/SWIR (Leicester) and cloud/UV retrievals (RAL) e.g. Would intend to provide 1 error spectrum per SWIR grid cell, chosen so variations globally should be representative. • Will aim to do this analysis by end next week. • Linear mapping subject to caveat that error spectra estimated for single atmospheric/viewing conditions (but spectral shape of errors realistic)

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