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Glint – from Scenarios to Global Scale

Glint – from Scenarios to Global Scale. Hartmut Boesch University of Leicester. Initial Glint CO 2 Retrieval Tests (TN3b1). Simulations with OCO algorithm One atmosphere for all simulations Aerosol: AOD = 0.03, 0.1, 0.2, 0.4; only BL aerosol

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Glint – from Scenarios to Global Scale

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  1. Glint – from Scenarios to Global Scale Hartmut Boesch University of Leicester

  2. Initial Glint CO2 Retrieval Tests (TN3b1) • Simulations with OCO algorithm • One atmosphere for all simulations • Aerosol: AOD = 0.03, 0.1, 0.2, 0.4; only BL aerosol • Cirrus: COD = 0.02, 0.1, 0.2, 0.3; Height 8 and 12 km • SZA: 5o, 50o, 70o • Windspeed: 1, 5, 10 m/s (1 parameter only to describe surface) • Instrument: • Retrieval setup: • A priori as truth except for aerosols/cirrus profiles • Aerosol and cirrus type as in simulations • Retrieval experiments: • 3-band CO2 and CH4 retrieval with increased window in SWIR-2 • 3-band CO2 and CH4 retrieval with increased window in SWIR-2 with continuum scaling in SWIR-1 and SWIR-2 (to describe whitecaps and other band-to-band uncertainties)

  3. Summary Glint CO2 Retrieval • A priori setup is as truth except vertical profiles of aerosols and cirrus • -> biases are only interference errors ! • More soundings but larger spread of biases with continuum scaling • Quality Filter • 2 < 0.8 per band • Dof > 1.8 • AOD < 0.2

  4. Glint CO2 Retrieval – Aerosols and Cirrus Retrieval with continuum scaling • Much improved retrieval of AOD compared to nadir retrievals • Continuum scaling has little effect on AOD and COD retrieval but XCO2 biases couple with cirrus Retrieval without continuum scaling

  5. Glint CO2 Retrieval with Continuum Scaling – Correlation Matrix COD = 0.2 COD = 0.02 Correlations between cirrus and continuum scaling SV Elements: 1-27: CO2 28: H2O 29: CH4 30: T 31: AOD 32: haerosol 33: waerosol 34: COD 35: hice 36: wice 37: ws 38: cont (SWIR1) 39: cont (SWIR2) (SZA = 50o, ws = 5m/s)

  6. Error Parameterization - Glint CO2 Retrieval • Full set (640) of simulations for error parameterization to create error tables to obtain global distribution of errors • SZA: 5o, 25o, 50o, 60o, 70o • Windspeed: 1, 5, 7.5, 10 m/s • Retrieval experiments: • 3-band CO2 and CH4 retrieval with increased window in SWIR-2 without continuum scaling Mean Precision: 0.52 ppm Mean bias: 0.27 ppm +/- 0.25 ppm 258 converged soundings and 83 pass filter

  7. Converged and Filtered Soundings windspeeds filtered Largely reduction of number of soundings with increasing SZA

  8. Characteristics of Sunglint Retrieval Windspeed is highly non-linear retrieval parameter Cox-Munk BRDF Lots of correlations between state vector elements Windspeed Correlation Coefficient CO2 iaerosol T, H2O CO2 iaerosol T, H2O ws ws ice ice Low windspeed: Correlations with ice High windspeed: Correlations with aerosols

  9. XCO2 Bias as function of AOD and COD AOD COD • Biases show clear increase with AOD and COD but depends also on SZA and windspeed • Threshold: • AOD < 0.1 • COD < 0.1 • AOD+COD < 0.2. AOD + COD

  10. Regression of XCO2 Biases • Regression of XCO2 biases against wind speed, AOD, COD and cirrus height (and constant) for each SZA • Polynomial interpolation of regression parameters with SZA to account for non-linearity

  11. XCO2 Error Parameterization Filtered and converged ConvergedNot converged Calculated Biases Regressed Biases Calculated Random Error Regressed Random Error

  12. Aerosol and Cirrus Parameters High windspeed Low windspeed Aerosol Cirrus Windspeed Filtered and converged ConvergedNot converged

  13. Glint - Summary and Conclusions • Sunglint retrievals are important as they provide a constraint of CO2 and CH4 over distribution oceans which will help with surface flux inversions over land. • Sunglint retrievals tend to behave differently compared to nadir-land retrievals • High non-linearity of windspeed parameter • Using one parameter (windspeed) to describe surface reflectance results in large information content for aerosol retrieval • More freedom might be required to deal with whitecaps, calibration uncertainties, etc • Overall, good performance with mean XCO2 bias of 0.27 ppm +/- 0.25 ppm and estimated precision of 0.5 ppm • A regression for bias, precision and kernels is available to map global error distribution and that can be used in OSSEs.

  14. Glint - Summary and Conclusions • Sunglint studies so far look promising but further work is needed to • Resolve remaining issues • how to best deal with correlations between windspeed and aerosols/cirrus • investigate best method to deal with whitecaps • extent error parameterizations with aerosol type • use of more advanced aerosol/cirrus filters • Develop global error files (L2e-files) using more realistic geometries (viewing angle  solar angle). This might need inputs from ESA on expected observational scenarios • Develop full error budget for sunglint observations • Consider specific case studies for localized sources over ocean

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