1 / 27

Overview

Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Task 2 plan R.Siddans PM1: RAL, 9 July 2013. Overview. Time scale for project to have impact on industrial contracts is short

chibale
Download Presentation

Overview

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument:Task 2 plan R.SiddansPM1: RAL, 9 July 2013

  2. Overview • Time scale for project to have impact on industrial contracts is short • Need to be clear which requirements are “critical” and to agree plan for consolidating them in Task 2. • Proposal indicated “the number of types of error to be assessed via simulation should be limited to a maximum of around 5 (depending on complexity)”. • Should orient around deadline for impacting industrial contracts and prioritise tasks accordingly. • Proposal identified Task 2.6 to define / refine scope of task, in consultation with ESA. • Initial outline plan iterated within team and circulated to ESA last week. • This covers: • Proposal for geophysical scenarios for T2 • Approach to Task 2.5 (assessment of co-registration errors) • Identification of “crtical” requirements and approach to address them

  3. SWIR Study geophysical scenarios • SWIR study to adopt specific scenarios used in [Butz 2010] defines an ensemble of geographically gridded cloud, aerosol, temperature, trace-gas (CH4,H2O) and surface albedo conditions based on combinations of CALIPSO, MODIS and ECHAM model data. • The ensemble only considers conditions over land, and is available for “days” in Jan, April, July, October. • For consistency with SWIR study we can make use of the same scenarios, adapted where necessary • Also scenario data has now been obtained from A. Butz

  4. Approach for aerosol retrieval • Aim is to present errors so far as possible as global maps, including across-track depedencies for ~equinox conditions. • To align with SWIR can adopt April, constructs as T1. • All will assume single T/p profile (mid-latitude) • Surface albedo from MODIS climatology as in SWIR scenario • Same aerosol profile assumed throughout – Camelot mid-lat polluted case (tbc) •  Assumes no cloud present. •  Assumes RT linear in aerosol amount so layer AOD errors not dependent on actual aerosol profile. • I.e. leads to minimal change cf Task 1 simulations.

  5. Approach for cloud/trace-gases • These to be non-linear retrievals based on “realistic” cloud/aerosol conditions as in earlier Eumetsat study • Starting point can be the SWIR scenarios. However optically thick cloud needs to be added (to the existing thin cirrus). • Propose to add optically thick (>1) cloud with varying profile and area-fraction based on a statistical sampling of the CALIPSO 5km layer product within regions on the map (details tbc). • Cloud-fractions vary over range where gas retrievals (in lower troposphere) possible, i.e. <0.5 (tbc). This will lead to two maps (i) as SWIR study with no thick cloud (ii) map with thick cloud of varying cloud. height, fraction and geometric thickness •  SWIR scenarios extended over sea (othewise fixed properties) • but emphasis over land is justified as trace gases primarily required over land even if retrieval over sea possible. •  Errors in trace gases simulated via AMF errors. No need to define trace-gas profiles explicitly.

  6. Approach for full-physics SWIR retrievals • Full non-linear retrievals simulations based on a selection from the SWIR scenarios. • Selection to obtain a good global representation. • Limit the simulations to scenes over and with thin clouds only • assume that thicker clouds can be identified by screening methods. • Study to focus on characterization and quantification of aerosol/cirrus related biases in the CH4 and CO retrieval. • Key question will be to investigate how well the retrieval can mitigate effects of aerosol or cirrus type for the two NIR instrument concepts any any SWIR band options (?) • Specifically, we will investigate correlations between biases and geo-physical parameters and the expected spatio-temporal distribution of biases. • CH4 proxy retrieval from the CH4/CO2 ratio around 1.6 micron not considered (does not involve NIR).

  7. Approach for H2O • SWIR senarios to be adopted. • Scenarios to be extended over ocean using H2O data from SCIAMACHY

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

  9. Assessment of co-registration errors • Co-registration errors (inter and intra-band) identified as potentially driving. • Previous analysis of inter-band error is limited to work done for Camelot (Veefkind) and an update • MODIS 1km cloud optical thickness data is user to study inter-band co-registration. 1km data is sampled to various FOV sizes (5,10,20km). • The PDF of the absolute difference in effective cloud fraction for particular shifts is studied, then the fraction of pixels meeting requirement (on error in fraction) is given as function of co-alignment error (up to 25%). • Finds that spatial resolution is not too important when co-alignment defined relative to the FOV size • The study does not quantify the impact of co-registration errors at L2. • Intra-band errors also assessed for NO2 using OMI data • Some work GOME-2 error study (for spatial aliasing) gives fairly consistent results.

  10. Assessment of co-registration errors

  11. Inter-band co-registration errors: Approach for T2 • For NIR-SWIR need to consider if this addressed in SWIR study & not duplicated effort. • For CH4 variations in surface height important • Cloud less important as screening will be strict. • For NIR/UVVIS, cloud is dominant issue • Approach will be to simulate tropospheric AMFs based on realistic cloud distributions (integrating 1km resolution AMFs over the 7x7km S5 FOV) and estimate error in AMF due to shifting the AMFs. • No retrieval simulations, just AMF errors • In proposal referred to use of cloud from from CloudSat/CALIPSO • However difficult / impossible to get reliable data at sub-5km resolution. • DARDAR provides ice cloud properties at 1km resolution • Liquid cloud only at 5km resolution (and probably driving structure) • Most cloud L2 product are potentially problematic because thresholds used to discriminate cloud/aerosol may introduce spurious fine-scale structure. MODIS L2 heights only at 5km resolution.

  12. Inter-band co-registration errors: Approach for T2 • However, can use the L2 CCI-Cloud retrievals: • Run at 1km resolution using ORAC (AATSR+MODIS) • All scenes retrieved without cloud-mask (cloud identified post-hoc). • For this purpose can take retrieved cloud optical depth, height, size, without considering cloud mask. • “Effective thin liquid cloud” will accommodate aerosol in cloud-free scenes in way which is consistent with imager radiances • Cloud/aerosol properties will be consistent with spatial variation of image radiances at 1km spatial resolution. • Imager provides limited information on cloud vertical structure but this not critical for this purpose. • Statistics for various co-registration errors to be generated in geographical regions, focussing over land (driving requirements and to sample small scale convective cloud regions). • Based on AATSR data to match local solar time of S5

  13. Intra-band co-registration errors • Generally assume linear drift of co-registration within fitted band. • For height resolved aerosol: • Cloud not relevant as needs to be strictly screened. • Can map impact for 3 scenes with variable land albedo used in S4 • Only for high-res A-band option. • For cloud/UV-VIS. • Simulate spectra based on 1km CCI cloud data and linearly map error spectra from co-regristration. • Generate stats in terms of UVVIS trace-gas AMFs • Only for low-res O2 A-band • For SWIR • Depends on SWIR study activity • For H2O (which uses nearby O2 B-band) • Test by merging clouded and non clouded spectra provided by the cloud fields used for cloud/UV-VIS application above

  14. 6 3 5 • Scenes for which error spectra provided

  15. Other errors to be assessed • In task 2 will simulate set of generic errors in any case, taking baseline values as defined by XLS provided by B. Veihelman (as in T1) • Instrument noise (based on recent input from ESA). • Error in spectral response function width (assuming 1% error) • Spectrally uniform offset in radiance units (assuming 1% of continuum radiance). • Key issues where requirements were found to be challenging to meet were identified at the S4/5 MAG meeting 22-23 April • These taken as basis for indentifying priorities for Task 2

  16. Consolidation of ESRA • Previously defined requirements on Relative Spectral Radiometric Accuracy (RSRA) have been found difficult to meet. • Effective relative Spectral Radiometric Accuracy has now been adopted: • ESRA is the error at L2 determined using a retrieval gain matrix to propagate a particular L1 error spectrum; this is required to be smaller than 50% of the corresponding L2 user requirement. • Industry expected to apply provided gain matrices to demonstrate compliance • In addition a relaxed RSRA requirement is retained (for NIR 0.25%, relaxed from 0.05%). • Gain matrices have been provided for most species by relevant groups for ESA and industry to use to assess compliance. • For most species only one gain matrix is supplied. • Concerns raised over how representative this approach is, in particular for the NIR band aerosol and cloud retrievals.

  17. Consolidation of ESRA • For the NIR band, RAL have provided a set of gain matrices for aerosol column amounts, for the high-resolution band option (older instrument parameters). • No matrices have been provided for water vapour, cloud or sensitivity of SWIR to NIR. • NB gain matrix approach is unsuitable for cloud retrievals as • (a) the cloud retrieval is highly non-linear and so unlikely to be well represented by a single gain matrix • (b) there are no user requirements on cloud properties (only on the trace-gas retrievals which rely on the cloud retrieval).

  18. Consolidation of ESRA • Within this study, task of deriving requirements for certain errors which dominate the RSRA budget (diffuser structure, spectrally structured stray-light) is replaced by a task to consolidate the ESRA approach. • Tasks involved are: • Provide gain matrices for H2O (IUP) • Provide gain matrices to map NIR band errors to error in SWIR CH4 (Leicester, if not covered by SWIR study) • For each NIR application except cloud/UV, assess representativeness of the gain matrices for selected cases compared to the range of geophysical scenarios (RAL, IUP, Leicester) • Comparing linearly mapped errors for some “generic” errors (e.g. spectrally uniform radiometric offset, spectral shift, 1% spectral response function width), based on gain matrices for the specific geophysical/geometric scenario to the selected conditions. • Optimise gain matrices provided to smallest possible set to capture necessary sensitivities

  19. Consolidation of ESRA • For the application of deriving cloud parameters for UV species it is suggested to derive RSRA requirements for this application, considering the comprehensive set of geophysical/geometric conditions and mapping to the tropospheric AMF level. • ESRA/gain approach is not considered feasible for this NIR application but it is also not expected that RSRA requirements derived from it should be too demanding.

  20. Absolute Radiometric Accuracy (ARA) (MR-LEO-UVN-160) • At the MAG the requirement was relaxed to apply only over a given signal level; The appropriateness of this relaxation will be addressed using the “generic” simulation results for spectrally uniform radiometric errors.

  21. High Spatial Sampling Data (MR-LEO-SYS-39) • Recently completed S4 Study considered impact of scene inhomogeneity on A-band aerosol retrieval from inhomogeneity in ground scene. • Noveltis simulated impact of inhomogeneity using 3 cloud-free scenes • RAL linearly mapped provided error spectra onto profile retrieval • Error was very large, but mitigation approach based on HSS data leads to effect being negligible • Conclusion not directly transferrable to S5 as number of HSS samples will be significantly fewer (6 for S4) • Performance of mitigation for reduced number of HSS samples could be important to assess, but this can only be done if Noveltis contribution to the study can be arranged. • Noveltis contacted and open to conducting such a task, but funds would need to be found… • Noveltis have already simulated S5 SWIR in context of S4 study so should be well set-up to do necessary work.

  22. Spectral Variation of System Integrated Energy (SIE) MR-LEO-UVN-80 and MR-LEO-UVN-87 • A relaxation of requirements was proposed to the MAG (and provisionally accepted) such that integrated energy of the spatial point-spread-function (PSF) within an area of 1 spatial sampling distance (SSD) squared should vary spectrally in the range 68-76% (previously 70-75%). • Requirements on this can be consolidated using the approach and data-set to consolidate spatial co-registration errors (section 3 above).

  23. S5 ISRF Knowledge MR-LEO-UVN-142, MR-LEO-SWIR-140 • The S4 support study indicated 1% width would lead to large errors for aerosol profile retrieval. The potential to mitigate this error by retrieving response function width from flight data (radiance and/or irradiance) will be investigated. • It is recognised that the current formulation of requirements potential sensitivities or realistic errors very satisfactorily. There is no clear way to improve on this formulation within this study however [ideas welcome!]. • Possibly lessons can be learnt from GOME-2 slit function characterisation experience – RAL to consider this.

  24. NIR Signal:noise • The current formulation of SNR in terms of a simple shot-noise model leads to non-compliances where the signal is low (in strong O2 absorption). A more realistic noise model is to assume a signal independent noise contribution: • SNR = a*L/sqrt(a*L+b) • Here we will assume a consistent with the current requirements in the continuum and estimate performace for a range of values of b, to provide requirements on beta. • This will be performed for each application (aerosol, cloud/uv,SWIR, H2O).

  25. NIR Polarisation sensivitiy • Could be covered by ESRA/gain approach. Specific simulations could be performed if ESA provide error spectra. • New polarised radiance simulations are beyond the scope of this study.

  26. Other errors in XLS from B. Veihelman 14 May 2013 • SSD: No action: assume as indicated (7km) • Temporal co-registration: Argue from spatial + wind-speed assumption • Sampling ratio: No action: Assume as indicated (3 for NIR) • Spectral knowledge on L1b: Current requirement is “0.01 (tbc)” for NIR channel. Wavelength calibration assumed to be retrieved in L1-2 (at least for aerosol application). Requirement at L1B can probably be relaxed. Will consider this but detailed retrieval simulations not obviously useful. • ISRF shape: • Not priority: Can test impact of changing ISRF from concept A applied to concept B for same noise model, but already clear effect is minor.

  27. Schedule MTR (ESTEC) 1 Oct; PM2 (Bremen) 25 Jan

More Related