Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: FP, 25 April 2014, ESTEC NIR cloud retrievals for UV/VIS trace-gases R.Siddans (RAL)

NIR / UV-VIS Application RAL Simulations based on retrieval scheme developed for Eumetsat A- band study Basis of approach is to – Define realistic cloud scenarios & simulate measurements – Perform cloud retrievals used relatively simple cloud model – Determine implied errors in the uv-vis trace gases by quantifying air- mass-factor (AMF) errors from the retrieved cloud representation Eumetsat study concluded – A-band cloud-as-reflecting-surface improves AMFs cf VIS/TIR imager – A-band scattering profile retrieval improves further – For application to characterise AMFs for uv, then A-band with nm resolution, signal to noise ~250 sufficient. – High resolution, low error instrument demonstrates superior cloud profile retrieval, however AMFs do not improve significantly Here focus on concept A only

“AMFs” Here the term AMF refers to the relative sensitivity of the TOA measurement to the given trace gas column, compared to that ignoring the presence of cloud but calculated for a surface albedo (in the DOAS window) which matches the apparent albedo of the scene Modelling of underlying albedo accounts for effect of multiple scattering above cloud. Despite potential for scattering in-cloud the usually means these relative AMFs are in range 0-1 – 0 = column completely obscured by cloud – 1 = effect of cloud negligible AMFs computed for 0-2, 0-6 and 0-12km column, assuming uniform distribution of profile within the layer – Depend on wavelength otherwise not species dependent – Here assess 325nm (O3, HCHO, SO2) and 450nm (NO2) Critical requirements are 10% for NO2 boundary layer column and 20% for tropospheric ozone and formaldehyde

True cloud profiles Cloud scenarios generated to complement the “SWIR scenarios” for A- Butz – basis is simulations of observations for idealised single day, representative of April conditions. ~2.8 degree lat/lon grid of surface albedo, trace-gas profiles, temperature, aerosol and thin cirrus Thick cloud scenarios constructed to represent range of cloud fractions and realistic cloud profiles for each grid cell Only model data can provide full description of cloud vertical profile, so profiles taken from ECMWF model cloud fields However samples selected to match (a) CALIPSO statistics for cloud top height in the given grid cell (b) ECMWF median thick cloud base height corresponding to clouds with the prescribed cloud top. Individual model profiles selected which match the selected (representative) top and base height – so other cloud parameters are randomly selected from a year of ECMWF data for the given location Range of cloud fractions and range of thick cloud heights spanned in groups of 3x3 grid cells...

True AMFs Cloud top height Cloud fraction

Non linear simulations for cloud/uv Cloud retrievals based on 3 basic assumptions, all of which obtain two parameters from the NIR (Schuessler & Loyola 2014) 1.Reflecting boundary (CRB): Fit effective fraction and height 2.Cloud as scattering layer (CAL): Optical depth, height retrieved, Assumed fraction known (OCRA or imager) Base height assumed at surface (worst case) or from truth (best case) 3.CAL with top height and fraction taken from imager (thermal-ir); base height and optical depth retrieved from NIR 1 and 2 are similar to schemes planned for S5P Schemes applied to A-band and B-band separately (to test possible benefit of B-band arising from more similar surface albedo to uv/vis) Varying SNR tests; 50 adequate in A-band, 100 needed to give comparable performance in B-band Accuracy of each assumption in representing uv AMF assessed Sensitivity of AMFs from each retrieval type to instrument errors simulated

True optical thickness True altitude / km

Co-registration requirements These identified as challenging at S4/5 MAG, proposal to relax to 0.3 inter (keep 0.1 intra for NIR)

Inter-band co-registration & SRF errors Error in cloud characterisation from NIR in uv/visible retrieval caused by co-registration errors between bands, coupled to cloud variability. Variability in cloud characterised using AATSR Cloud CCI L1 data Cloud optical thickness (COT) Cloud height (assuming geometrically thin) Typically higher than A-band effective height (cloud effect overestimated) Cloud phase Cloud particle effective radius AMFs computed at 1km resolution, then “averaged” over shifted and perturbed spatial response functions Averaging of sensitivity is radiance weighted, so small change in fraction can give large change in AMF (more photons from cloudy fraction) Statistics generated from 1 year of global data

Spatial Response Functions A relaxation of integrated energy (IE) requirement proposed to the MAG such that IE of the spatial point-spread-function (PSF) within an area of 1 spatial sampling distance (SSD) squared could vary spectrally in the range 68-76% (previously %). SRFs created by convolving box-car and Gaussian functions…no sharp edges!

Histogram of errors from 20% SSD shift

Global histograms for scenes with AMF > 0.5 AMF itself (relative to all scenes) Error due to 1.4km shift Error from 10% increase in area Error due to IE varying from 60-65% (Fixed FWHM) Error due to IE varying from 70-75% (Fixed FWHM)

Geographical variations of 90% ile

Summarised results (450nm boundary layer) 70% Req. Based on 90%ile instrument errors over all cloud scenes for fraction 0.1 or % Req.

Summarised results (325nm 0-6 km column) 70% Req. 30% Req. 30% RSS.

Summarised results (CAL_Z0) 70% Req. 30% Req. 30% RSS.

Conclusions (1) Concept A has sufficient spectral resolution for this application Signal to noise only in range required 10% requirement on NO2 in boundary layer is most demanding. Spectral response function errors are generally small in all cases (less than 1% AMF errors). 5% variation in spatial response function integrated energy between the bands has small effect (10% OK) Differences between retrieval options are relatively small  Sensitivity to instrumental errors very similar.  Variations more in the agreement between retrieved and true AMF (i.e. profile representation errors).  CAL model has better standard deviation than CRB  Bias depends on how good is the assumption made about the cloud vertical thickness Thermal-IR imagery could be used as constraint. Conclusions (Cloud/UV)

Conclusions (1) CAL assumes knowledge of cloud geometric fraction  For GOME / SCIAMACHY / GOME-2 this has been derived using PMDs (which oversample the nominal FOV)  Such observations desirable also for S5 (in few spectral bands, across-track at least)  However recommend to pursue use of co-located imagery from MetImage – will provide more detailed information on cloud structure and TIR constraint on height Differences are also small between results from the oxygen A and B bands  Representation errors smaller for the B band, however sensitivity to instrumental errors is larger. E.g. the B-band RSRA errors can be a few % for boundary layer cf < 1% from A band.  There may be benefit of using both bands (not tested here) Conclusions (Cloud/UV)

Random errors caused by the simplified cloud model in the retrieval: 7-10% depending on cloud model (but difference bigger for high cloud fraction). – Other representation errors apply (e.g. knowledge of trace-gas profile shape, horizontal cloud structure), so result is probably lower limit. Error might be reduced by improving the cloud retrieval (R & D) ARA offset leads to 2-4% errors on 0-2 km NO2 column. ARA multiplicative leads to around 10% errors in the NO2 column. ARA multiplicative errors ~factor 2 smaller for retrievals from the B-band, but then the ARA offset error is larger – overall ARA similar ARA Errors could be mitigated by retrieving cloud fraction from the UV/visible fit window. However then ARA in the UV/visible critical. 20% inter-band co-registration errors lead to 6% 0-2 km AMF errors (10% of the time) – Could also be mitigated by fitting cloud fraction from the UV/visible, assuming the dominant spatial variation is cloud fraction Summary of dominant errors (Cloud/UV)