S5P cloud products

Sebastián Gimeno García, Ronny Lutz, Diego Loyola

German S5P Verification Meeting 1Bremen, 28-29 November 2013

www.DLR.de • Chart 1 > Vortrag > Autor • Dokumentname > Datum

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Outline

General overview

OCRA adaptation to S5P

Cloud model – OCRA/ROCINN-CAL,-CRB

ROCINN CRB: CA variable vs. CA fixed

Cloud inhomogeneity effects

Conclusions

Outlook

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S5P cloud information primarily needed for accurate trace gas retrieval

Influence of clouds on gas retrieval (1D):

albedo effect shielding effect Multiple scattering effect

Overview

+ others: e.g. multiple cloud layering system, …

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S5P cloud information primarily needed for accurate trace gas retrieval

Influence of clouds on gas retrieval (3D):

neighbouring pixel effect in-pixel inhomogeneity effects

Overview

+ others: e.g. effect of scene variability on spectral calibration, …

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OCRA adaptation to S5P – Input Data

OCRA for GOME,

SCIAMACHY and GOME-2

uses the PMD UVN data with

a resolution of ~10x40km2

OCRA for TROPOMI will use

the UVN radiance data with

a resolution of 7x7km2

The initial S5P cloud-free

composites will be based on

OMI data with a resolution of

13x24km2 at nadir

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OCRA adaptation – OMI cloud-free composite

UV cloud-free for July

VIS cloud-free for July

UV cloud-free for January

VIS cloud-free for January

Monthly composite of cloud-free reflectances in UV-2 and VIS OMI channels

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OCRA adaptation – OMI cloud fraction results

CF comparison:

OCRA OMTO3 OMDOAO3

Global pattern good represented by all products

Scan angle dependency

Comparison with OMI official cloud products:

OMCLDO2 OMCLDRRongoing …

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OCRA adaptation – OMI cloud fraction results (2)

Clear correlation between all CF products

OCRA shows slope in mean differences

OMDOAO3 delivers larger CFs than the other two products

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Cloud fraction (CF) is retrieved using a RGB color space approach → OCRA

Cloud parameters (CTH, COT) are retrieved in the Oxygen A-band using regularization theory → ROCINN

CRB: Clouds are treated as Reflecting Boundary (Lambertian equivalent reflectors)

CAL: Clouds are treated As homogeneous Layers

Photon cloud penetration is allowed

Multiple scattering is accounted for

Modeled radiance contains information below the cloud layer

Retrieved CTH expected to be closer to the geometrical CTH

Cloud Model – OCRA/ROCINN-CAL/CRB

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Cloud Model – OCRA/ROCINN-CAL/CRB (2)

Intra-cloud correction

Loyola et al., JGR 2011

Surface

Lambertian Cloud

CAL: Cloud As scattering Layer | CRB: Cloud as Reflecting Boundary

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Cloud Model – OCRA/ROCINN-CAL/CRB (3)

Comment from a reviewer of the S5P Cloud ATBD:

„To treat clouds as simple reflectors … is far to simple and might work for large pixels averaging over more than 2000 Km , but is very likely not working for the interpretation of much finer spatial resolution TROPOMI measurements.“

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Cloud Model – OCRA/ROCINN-CAL/CRB (4)

100000 independent spectra were simulated using the ROCINN CAL forward model (VLIDORT) covering the whole ROCINN CAL state space (1% noise added):

SH in [0, 2] km SA in [0, 1] CTH in [0, 15] km COT in [0, 125] CGT in [0.5, 14.5] km SZA in [0, 85] ° VZA in [0, 75] ° CF in [0, 1]

CRB retrievals of CAL spectra: effects due to different cloud models

Relative difference: CTHCFXX

XXX

ref

ref ,;)(

*100:)(

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Cloud Model – OCRA/ROCINN-CAL/CRB (5)

- CRB retrieved cloud “top” height is systematically smaller than the

geometrical cloud top height. - Discrepancy increases as cloud optical depth decreases.

Global Mean Lambertian Model

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ROCINN-CRB: CA variable versus CA fixed

100000 independent spectra were simulated using the ROCINN CRB forward model (VLIDORT) covering the whole ROCINN CRB state space (1% noise added):

SH in [0, 2] km SA in [0, 1] CH in [0, 15] km CA in [0, 1] SZA in [0, 85] ° VZA in [0, 75] ° CF in [0, 1]

Cloud albedo (CA) was set to 0.8 in the cloud property retrieval

Results show the impact of fixing CA to 0.8 in CRB in comparison with a variable CA (not CRB vs. CAL!)

Relative difference: CTHCFXX

XXX

ref

ref ,;)(

*100:)(

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ROCINN-CRB: CA variable versus CA fixed (2)

ROCINN CRB with fixed CA (=0.8):

underestimates CF if actual CA is lower than 0.8 overestimates CF if actual CA is higher than 0.8 overestimates CTH if actual CA is lower than 0.8 underestimates CTH if actual CA is higher than 0.8 the larger the SA, the larger the CTH underestimation

CF rel. diff. vs.cloud albedo

CTH rel. diff. vs. cloud albedo

CTH rel. diff. vs. surface albedo

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MoCaRT (Monte Carlo Radiative Transfer) Model reflectivities

Cloud inhomogeneity effects

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Conclusions

OCRA CF algorithm has been adapted for S5P/TROPOMI

preliminary results for OMI look very promising

OCRA algorithm is computationally very efficient

good agreement with existing algorithms (OMTO3, OMDOAO3): OCRA CFs correlate with both

ROCINN CRB (LER) evaluation:

ROCINN CRB underestimates CTH (as expected) CTH discrepancies increase with decreasing CA/COT

Setting CA to a fixed value (CA_ref=0.8) leads to a complex two-regime (below and above CA_ref) dependency of {CTH, CF} on cloud albedo (cloud optical thickness) and surface albedo

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Outlook

Comparisons of OCRA with official OMI cloud products (OMCLDO2, OMCLDRR) ongoing

Case studies with synthetic spectra

OCRA ROCINN-CRB/CAL 3D effects

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Thank you for your attention!

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Information theory analysis

Degree of freedom of the signal (DFS) ~ 2

Only two independent parameters can be retrieved in the O2 A-band

CTH and COT are retrieved with ROCINN in the O2 A-band

OCRA/ROCINN --- CAL

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ROCINN CRB verification

100000 independent spectra were simulated using the ROCINN CRB forward model (VLIDORT) covering the whole ROCINN CRB state space (1% noise added):

SH in [0, 2] km SA in [0, 1] CH in [0, 15] km CA in [0, 1] SZA in [0, 85] ° VZA in [0, 75] ° CF in [0, 1]

Test retrieval performance with respect to {CF, CTH}

Relative difference: CTHCFXX

XXX

ref

ref ,;)(

*100:)(

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ROCINN_CRB --- CTH, CA --- verification (1)

The relative differences between the reference CF‘s and CTH‘s and corresponding retrieved values,

X_rel := 100 * (X_out – X_ref) / X_ref,

show good overall perfonmance of the algorithm

Median of the distributions close to zero Most differences within few percent

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Very good overall CF retrieval performance

Almost perfect correlation between reference and retrieved CFs

Relative differences show higher spread for large SZA (small cosines: CSZA)

CF retrieval does not show dependency on cloud (CA) and surface albedo (SA)

ROCINN_CRB --- CTH, CA --- verification (2)

CF_out vs. CF_ref CF_rel vs. CSZA

CF_rel vs. CA CF_rel vs. SA

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Good overall CTH retrieval performance

CTH slightly understimated and higher spread of CTH_rel for large SZA

CTH relative differences show higher spread for small „cloud albedo fractions“ CAF=CA*CF

CTH retrieval does not show dependency on surface albedo (SA)

ROCINN_CRB --- CTH, CA --- verification (2)

CTH_out vs. CF_ref CTH_rel vs. CSZA

CTH_rel vs. CAF CTH_rel vs. SA

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CA --- COT --- SZA relationship