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Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument. Scattering profile characterisation for SWIR Leif Vogel, Hartmut Boesch University Leicester. Approach for Retrieval Simulations.

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requirements consolidation of the near infrared channel of the gmes sentinel 5 uvns instrument

Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument

Scattering profile characterisation for SWIR

Leif Vogel, Hartmut Boesch

University Leicester

slide2

Approach for

Retrieval Simulations

  • Spectra are simulated using the forward model of UoL FP retrieval algorithm for a range of geophysical scenarios
  • Sensitivity tests for retrievals w.r.t. scattering profiles, i.e. the retrieval applies
    • the same a priori trace gas profiles, temperature profile, surface albedo
    • different setup for aerosol and cirrus a priori
    • Maximal sensitivity to scattering induced errors
    • Bias given by difference between true and retrieved XCH4
slide3

The UoL Retrieval Algorithm

  • Measured radiance spectra are non-linear function of atmospheric parameters
  • retrieval is performed iteratively by alternating calls to FW and IM
  • 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
slide6

Instrumental errors

(ECHAM Scenarios)

Geophysical scenario described in Butzet al. 2010, Butz et al. 2012

  • Aerosol profiles originating from ECHAM 5 model simulations (Stier et al 2005)
  • Global coverage for one day (April 15th, 2015)
  • Atmosphere: 18-level profile
  • SZA: noon local time (27º- 87º)
  • Total AOD given by MODIS measurements
  • Surface albedo determined by MODIS and Sciamacy data
sensitivity to radiometric accuracy

Simulated ECHAM scenarios

Sensitivity to radiometric accuracy
  • Linear mapping of errors has been used to determine additive and multiplicative ARA errors
    • Additive gain: 3% of trop dark scenario in respective band
    • Multiplicative gain: 3% for NIR1 and 2
  • RSRA/ESRA errors determined by SWIR study
sensitivity to isrf

Simulated ECHAM scenarios

Sensitivity to ISRF
  • Linear mapping of errors has been used to determine sensitivity to ISRF
  • 11 different slit functions are studied

asymmetry

Scene inhom

Spectral offset

width

sensitivity to isrf3

Simulated ECHAM scenarios

Sensitivity to ISRF
  • Greatest CH4 error via idisp-3
  • NIR 1 channel much less sensitive
  • Allowing for the retrieval algorithm to spectrally shift and squeeze may mitigate (or mask) effects
slide15

Simulated ECHAM scenarios

Sensitivity to ISRF, Scene inhomogeniety

  • NIR 1 channel much less sensitive
  • Homm; homp slit function errors are not independent
    • Error of scene inhomogeneity is given as absolute mean
  • Introduced bias is very low with 0.048%
  • A greater variability in the NIR2 channel leads to total standard deviation of 0.469%.
slide16

Instrumental errors (ECHAM)

Conclusions

ARA requirements: Mean CH4 accuracy meets requirements, but standard deviation is rather high.

Reduction would be beneficial

slide17

Simulated MACC scenarios

  • Simulations with ECHAM 5 model simulations as described in Stier et al 2005, Butz et al 2010, Butz et al 2012
    • Description of atmospheric parameters and aerosol optical properties not directly transferable to the UoL algorithm.
    • Calculated aerosol optical properties are either dust or sulphate dominated
  • ECHAM 5 aerosols replaced with aerosols from MACC, ECWMF integrated forecasting system (IFS), 12h GMT April 14th 2010
    • Use atmospheric data from the previous scenarios in combination with ECMWF aerosols to increase number of successful retrievals
macc vs echam scenarios
MACC vs. ECHAM Scenarios

ECHAM Desaster

  • Replace only aerosols
  • All other scenario information remains unchanged
    • Cirrus clouds, atmosphere, pressure levels, surface albedo, etc.
different retrievals for macc simulations

Representation errors

(MACC scenarios)

Different retrievals for MACC simulations

Aerosol parameterization:

  • Use two linear combination of aerosol types to approximate true type
  • 2 generic Gaussian aerosol extinction profiles (altitude =2km agl, width = 1.5km, aod = 0.1)
  • Cirrus (altitude 10km agl, width =1km, cod = 0.05

In total 8 global retrievals to study representation errors`

simulation of fluorescene
Simulation of fluorescene
  • Fluorescence data supplied by L. Guanter
  • FS Spectra added to simulated Spectra taking into account respective aerosol load and viewing direction
slide21

Representation errors

(MACC scenarios)

without fluorescence

with fluorescence

NIR 1&2

NIR 2

NIR 1&2

NIR 2

without offset

with offset

  • Some regional differences can be observed:
  • effect of fluorescence (without offset correction)
  • Indication that zero level offset may couple unfavourably with cirrus clouds
  • Similar coverage of NIR 1&2 and NIR 2 only retrievals
ch 4 bias

Representation errors

(MACC scenarios)

CH4 bias [%]

without fluorescence

with fluorescence

NIR 1&2

NIR 2

NIR 1&2

NIR 2

without offset

with offset

Blue: converged retrievals over ice-free land

Green: a-posteriori filter is applied

ch 4 retrieval error

Representation errors

(MACC scenarios)

CH4 retrieval error [%]

without fluorescence

with fluorescence

NIR 1&2

NIR 2

NIR 1&2

NIR 2

without offset

with offset

Blue: converged retrievals over ice-free land

Green: a-posteriori filter is applied

slide24

MACC scenarios

  • 1) Number of converged retrievals out of a total of 1933 simulated measurements over land and ice free surface.
  • All retrievals fulfill requirements
  • Less converged retrievals for NIR 1&2 than for only NIR2
    • <->Tighter boundary conditions due to O2-B band
  • Retrievals with NIR 1&2 show better performance in random and systematic errors
  • Fluorescence leads to higher errors, but its effect can be mitigated
  • Indication that aerosol information in the O2-B band constrains the retrievals at cost of lesser coverage <-> filtering effect
slide25

Summary

  • Systematic is described here by the mean bias
  • Pseudo random is described as the standard deviation of the mean bias
  • Random is given by the mean of the retrieval error where applicable.
  • 50% of user requirement
  • Minimum and maximum values from the ILS variations. Min. variations are taken from asy1pc, max. values from idisp-3
  • The two values result from the assumed minimum and maximum error of the ILS
slide26

Conclusion:

  • Retrievals have been performed with two geophysical Scenarios based on ECHAM and MACC aerosol distributions for instrumental and representation errors.
  • Most error sources lead to results inline with the requirements. However, additive and multiplicative ARA induced errors are high. Reduction of these error sources is desirable.
  • Representation errors meet the requirements.
  • Using a two NIR (O2-A & B) band retrieval increases accuracy at the cost of slightly diminished coverage, and its use is beneficial to prevent erroneous results.
  • The effect of fluorescence can be mitigated using a zero level offset.
  • Further potential lies in improved aerosol representations (regional dependencies, climatology, improved optical properties), which may also lead to increased coverage.