Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS InstrumentMTR, 1st October 2013

Task 2Scattering profile

characterisation for SWIR

Leif Vogel, Hartmut BoeschUniversity of Leicester

Study Sentinel 5 instrument concepts (A and B) w.r.t. aerosol profile characterisation for SWIR species.

Link from aerosol information in NIR to trace gas retrievals in SWIR Simulate global coverage for a single day (April) of realistic S5

sampling applying ECHAM 5 simulation supplied by Butz et al. 1) Instrument noise (based on recent input from ESA).2) Effect of vegetation fluorescence3) Error in spectral response function width (assuming 1% error)4) Spectrally uniform offset in radiance units (assuming 1% of continuum

radiance).5) The ARA requirement

SWIR S5 Products

UoL Task 2 Overview

Target gas Spectral windows

CH4 1.6μm, 2.3μm

CO 2.3μm

Additional information used for aerosol profile

O2-A 0.76μm

O2-B 0.69μm

CO2 1.6μm for proxy retrievals

Approach for Retrieval Simulations

Spectra are simulated using the forward modelling of UoL FP retrieval algorithm

two instrumental setups range of geophysical scenarios

Retrieval sensitivity tests for retrievals w.r.t. scattering profiles, retrieval applies

the same a priori trace gas profiles, temperature profile, surface albedo different setup for aerosol and cirrus a priori Bias given by difference true and retrieved XCH4

The UoL Retrieval Algorithm Measured radiance

spectra are non-linear function of atmospheric parameters

retrieval is performed iteratively by alternating calls to:

Forward Model describes physics of measurement: Multiple-scattering RT Instrument Model Solar Model

Inverse Method estimates state: Rodger’s optimal

estimation technique

XCH4, XCO and its error is computed from retrieved state after iterative retrieval has converged

Names Quantity Notes

CO and CH4 1 Multiplier to a priori profile

H2O, HDO, CO2 1 Multiplier to a priori profile

Temperature 1 Additive offset to a priori profile

Aerosols AOD, height and width

Gauss profile

Clouds AOD, height and width

Gauss profile

Surface Albedo #bands x 2para Albedo at band centre and slope

Typical State Vector

Concept A        Bands NIR (685 – 773 nm)* SWIR 1 SWIR3  NIR 1 NIR 2    

Wavelengths [nm] 685 - 700 750 – 773 1590 - 1675 2305 - 2385

Numbers of pixel 116 177 850 800FWHM ISF 0.39 0.39 0.25 0.25

*) Simulated retrievals do not use full range due to strongly changing surface albedo

         Concept B        

Bands NIR SWIR 1 SWIR3

Wavelengths [nm] 755 – 773 1590 - 1675 2305 - 2385

Numbers of pixel 450 850 870FWHM ISF 0.12 0.23 0.23

Instrumental setup

Instrumental setup

Concept A: NIR1 NIR2 SWIR1 SWIR3

Concept B: NIR SWIR1 SWIR3

Simulated scenarios

One day in April 2015 as described in Butz et al. 2010, Butz et al. 2012 applying ECHAM 5 model simulations (Stier et al 2005)

Stier et al 2005

ECHAM Desaster

Simulated scenarios

ECHAM 5 model simulations as described in Stier et al 2005, Butz et al 2010, Butz et al 2012

(18 layers x 7 aerosol types x ~2700 Observations)

Simulated scenarios

In most cases very large Aerosols particles with subsequent unrealistic low Angstroem coefficients.

•Very strong absorption in the SWIR3 band

•Erroneous relative signal to noise ratios for different wavelength channels

Simulated scenarios Alternative approach:Replacing aerosol types described in Stier et al. 2005 with similar ones described in Kahn et al. 2001 based on aerosol type and radiusCreating a joint aerosol mix per observation with weights depending on respective ECHAM composition per observationApplying original aerosol altitude profile

ECHAM Kahn et al. 2001

Mode Aerosols Base/Mixt.

Aerosols

Nucleation SU Base SU land

Aitken SU, BC, POM Mix 5a SU, acc.DU, BC, Carb

Accumulation SU, BC, POM, SS, DU

Mix 3a SU, SS, BC, Carb

Coarse SU, BC, POM, SS, DU

Mix 4a SU, acc.DU, coarse DU, Carb

Aitken BC, POM Mix 3b BC, Carb, SU, SS

Accumulation DU Base Acc. DU

Coarse DU Base Coarse DU

Simulated scenarios

Forward model:

Simulated scenarios

Dust dominated

Sulphurdominated

Aerosol properties show more realistic optical properties (Angstrom) than in first approach

But range of properties is very large which is expected to be problematic for retrieval

Concept A

Concept B

Example of produced spectra from TN1

Retrieval Setup State vector: Scaling factors for the CH4, CO, H2O, HDO, CO2 vmr profile;

Temperature factors, surface albedo + tilt per band, parameters for Gauss profile for cirrus, parameters for Gauss profile for 2 aerosol types

A priori values: Atmosphere as in simulations Aerosol extinction profile: Gaussian-shaped at height of 2 km a.g.l., width

(FWHM) of 1 km and AOD of 0.05 Cirrus extinction profile: Gaussian-shaped at height of 10 km, width

(FWHM) of 1 km and optical depth of 0.05.

2 Aerosol types: Due to the wide range of simulated aerosols which are not captured by individual Kahn mixtures, two simulated aerosols were chosen:

A) Large Angstroem Coefficients (high sulfate component)B) Small Angstroem Coefficients (high dust component)

Cirrus type: as in simulations

Aerosol + cirrus parameters differ from simulations (except cirrus type)

Quality-Filtering Retrievals

  Concept A Concept B  CH4 CH4

  ConvergedSoundings 594 (23%) 881 (35%)

  FilteredSoundings 405 (16%) 639 (25%)

Only converged retrievals are used: Number of converging iteration steps ≤ 12Number of diverging iteration steps ≤ 5

Additional post-processing quality filter: Χ2 < 1 per spectral bandCH4 error < 0.4%Retrieved AOD < 0.2Retrieved AOD+COD < 0.3 Surface albedo at O2 bands < 0.7 (removes snow and ice)

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Total numberConvergedFiltered

Effect of the filter

Results: Concept A

 CH4 Converged Filtered

Bias (%)0.013 +/- 0.955

0.036 +/- 0.342

Precision (%)0.112 +/- 0.055

0.103 +/-0.052

Impact of scattering error on trace gas retrieval

unfiltered filtered

CH

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Converged retrievals do not show obvious dependency on location

Concept A

Results: Concept ADegrees of freedom inferred from the diagonal elements

of averaging kernel matrix (Rogers, 2001)

Maximum number of DoF for aerosol and cirrus = 3 (optical depth, altitude, width) per type

Aerosol type 1 DoF ~ 2

Aerosol type 2 DoF ~1 - 2

Cirrus clouds DoF ~ 1 - 2

Mean DoF (AOD+COD): 4.63 (4.83)

Aerosols poorly retrieved

Distribution of retrieved vs. true AOD mirrors the wide range of aerosol mixtures retrieved with two opposing types and high possibly aerosol load

Good correlation between retrieved and true COD

Results: Concept ADependency on albedo

Results: Concept A

Dependency of CH4 bias on aerosol

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Results: Concept B

 CH4 Converged Filtered

Bias (%)-0.307 +/- 1.263

-0.171 +/- 0.683

Precision (%)0.140 +/- 0.083

0.124 +/- 0.068

Impact of scattering error on trace gas retrieval

unfiltered filtered

CH

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Converged retrievals do not show obvious dependency on location

Concept B

Results: Concept BAerosol type 1 DoF ~ 2

Aerosol type 2 DoF ~1 – 2

In comparison to Concept A, no obvious change in DoF and distribution for both aerosol types

Cirrus clouds DoF ~ 1 - 2

Total DoF ~ 4 – 5.5

− Distribution is slightly skewed to lower values in comparison to Concept A

Good information content for retrieving aerosol and cirrus parameters

Distribution of retrieved vs. true AOD mirrors the wide range of aerosol mixtures retrieved with two opposing types, although not as extrem as Concept A

Good correlation between retrieved and true COD

Results: Concept B

Dependency of CH4 bias on aerosols

Results: Concept BDependency on albedo

  Concept A Concept B  CH4 CH4

  ConvergedSoundings 594 (23%) 881 (35%)Bias (%) 0.013 +/- 0.955 -0.307 +/- 1.263

Precision (%) 0.112 +/- 0.055 0.140 +/- 0.083  Filtered

Soundings 405 (16%) 639 (25%)Bias (%) 0.036 +/- 0.342 -0.171 +/- 0.683

Precision (%) 0.103 +/-0.052 0.124 +/- 0.068

Comparison of concepts A & B

Results obtained for concept A and B show that

The number of converged retrievals and retrievals that passed quality filter is higher for concept B

variability of simulated aerosols vs. the two opposing types used in the retrieval and the lesser constrain by the missing O2-B band.

Mean bias and standard deviations for CH4 is larger for concept B

Both concepts have similar precision

These conclusions hold after applying the filter

Con

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AC

once

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Main conclusions (so far):

Concept A may yield less biased results at higher precision, although the true AOD was resolved at lesser accuracy. The additional O2-B band may therefore yield important aerosol information However, in total the differences between concepts are not very big.

However, additional errors may be introduced (see RAL study)

Still to be assessed for these scenarios:1) Effect of vegetation fluorescence, from which the O2-B band is more

affected relatively 2) Error in spectral response function width (assuming 1% error)3) Spectrally uniform offset in radiance units (assuming 1% of continuum

radiance).4) The ARA requirement

Technical note will be provided ….