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Testing LW fingerprinting with simulated spectra using MERRA

This study aims to reduce fingerprinting error and improve retrieval of temperature and humidity changes. The study examines the use of clear-sky and all-sky scenes and analyzes various atmospheric and cloud properties. The results indicate that using all-sky scenes does not significantly increase error compared to using clear-sky scenes or removing clouds.

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Testing LW fingerprinting with simulated spectra using MERRA

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  1. Testing LW fingerprinting with simulated spectra using MERRA Seiji Kato1, Fred G. Rose2, Xu Liu1, Martin Mlynczak1, and Bruce A. Wielicki1 1NASA Langley Research Center 2Science System & Applications Inc. CLARREO SDT Meeting The National Institute of Aerospace April 10-12, 2012

  2. Objective of this study • Search for the method to reduce the fingerprinting error • Find out “mean” atmospheric and cloud properties to match the temporally and spatially averaged spectral radiance • Find out whether separating clear-sky from all-sky scenes improves the retrieval of temperature and humidity change from climatological mean (anomaly)

  3. MERRA DATA • 1983 – 2010 : 28 years • Global (540,361) ( 0.66 Lon x 0.50 Lat ) • 6 Hourly: • T, Q, O3 Profiles at 42 vertical levels • Hourly : • Tskin, T2m, Q2m, Sfc_emiss • Random Overlap Cloud Fraction (High, Mid, Low) • Cloud Optical Depth (High, Mid, Low) • Cloud Pressure ( 1st layer seen from space) • NO Phase, NO Particle Size, Limited Cloud Height info. • No Cloud LWC/IWC profile files • Clear and Total Sky OLR ( MERRA Rt code) • Files used : • inst6_3d_ana_Np. , tavg1_2d_slv_Nx, tavg1_2d_rad_Nx • On /ASDC_archive

  4. PCRTM • Principal Component Radiative Transfer Model • Xu Liu, William L. Smith, Daniel K. Zhou, and Allen Larar • Applied Optics Vol 45, No. 1, 1 Jan 2006 • Spectral longwave radiance (50 -2760 cm-1) • 0.5cm-1 effective resolution • Obtained using 280 principal components • Cloud properties (P. Yang) • Optical depth, Particle size, Phase ,Effective pressure • Includes Multiple Scattering • Variable Gases: H2O, O3 - CO2, CH4, N2O, CO can vary in PCRTM but not in this simulation. • Other minor trace gas concentrations fixed example: CFC’s • No Aerosol in this simulation

  5. Radiance simulation with MERRA • 90deg CLARREO ‘like’ Orbit • Repeats Annually • Assuming 30 second sampling interval • FOV is taken as closest MERRA grid-hour box • Spectral radiance is computed for every FOV

  6. Orbit Coverage

  7. Monthly mean computations • 10 zonal monthly mean temperature and humidity profiles • 3 cloud types within a zone • Emissivity weighted logarithmic mean optical thickness for each cloud type • Spectral radiances computed with monthly zonal mean properties agree with instantaneous spectral radiance well.

  8. Monthly mean properties Kato et al. 2011 Forward modeling of using monthly mean cloud and atmospheric properties Retrieve from All cloud and atmospheric properties are sampled by the 90° CLARREO orbit

  9. Difference between and Difference of 28-year mean radiance (10°S to 0°) RMS difference Blue: Red: Difference of annual anomalies Annual mean – 28-year mean

  10. Retrieval of annual anomalies • Use spectral radiance computed at a high resolution (instrument sampling) • Compute ΔI by taking annual mean spectral radiance minus 28-year mean • Retrieve cloud and atmospheric property anomalies (deviation from 28-year mean) from annual mean radiance anomalies using spectral kernels computed by perturbing monthly mean properties ( ) • Compare retrieved atmospheric and cloud properties with annual property anomalies ( )

  11. Clear-sky occurrence MERRA clear fraction: excludes clouds with optical thickness less than 0.3 Error bars indicate standard deviation of 28-year clear fractions CALIPSO CloudSat derived clear fraction (2007 to 2009) also exclude clouds of which optical thickness less than 0.3 Error bars are max and min of three year clear fraction

  12. Clear-sky temperature change retrieval Temperature 100 – 10 hPa (20°S to 10°S) ΔT (K) Surface temperature (40°S - 30°S) ΔT (K) Red: retrieved Blue: Truth (deviation from 28 year mean)

  13. Clear-sky versus All-sky Clear-sky only sampling does not affect retrieval All-sky and clear-sky with cloud removed have similar RMS and correlation coef.

  14. Clear-sky versus All-sky 200-100 hPa temperature Tropics: fixed vertical grid of 200-100 hPa might be a problem

  15. Clear-sky versus all-sky: surface temperature SH ocean ~60°S is mostly cloudy: All-sky had a larger RMS Tropics and midlatitude (NH): all-sky has slightly larger RMS and smaller correlation coef. Compared with cloud removed

  16. Upper tropospheric relative humidity Clear-sky fraction weight has a larger RMS and smaller correlation coef.

  17. Clouds: cloud fraction exposed to space Low-level cloud fraction High-level cloud fraction 50°S – 40°S 30°N – 40°N 0°N – 10°N 0°N – 10°N Blue: truth (deviation from 28 mean) Red: retrieved

  18. Cloud properties: cloud fraction

  19. Cloud top height Need to retrieve cloud top effective temperature instead of cloud top height

  20. Summary and conclusions • Using only clear-sky scenes to detect temperature and humidity changes does not seem to improve the retrieval result significantly. • Using all-sky scene to detect temperature and humidity change does not increase error significantly compared with those retrieved from cloud removed. • Fingerprinting retrieves effective cloud properties (e.g. weighted by emissivity). Combination of forward modeling and retrieval simulations is needed to understand retrieved cloud properties.

  21. Back-ups

  22. Instantaneous Vs Mthly Average

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