Download
1 / 33
330 likes | 332 Views
This study presents a new algorithm for remote sensing of aerosol properties using PARASOL measurements. The algorithm retrieves aerosol composition, size, shape, concentration, and optical properties, as well as measurements near and above clouds.
E N D
Remote Sensing of Aerosol Properties over the ocean from PARASOL measurements Otto Hasekamp, ArjenStap, PavelLitvinov, Andre Butz
Outline • Introduction • Algorithm description. • Synthetic retrievals • Results and Validation. • PARASOL: next steps • Conclusions.
Needed (globally): • Refractive index (composition, water uptake). • Size, shape, and concentration. • Optical properties (AOT, SSA, phase function) • Measurementsclose to and above clouds Aerosols & Climate Current standard aerosol products only provide very limited information on aerosol microphysics and SSA. Also AOT retrievals have large errors.
POLDER / PARASOL • Launched 2004 • Wide FOV CCD Camera with +/- 43 degrees cross track - +/- 51degrees along track. • Each ground pixel is viewed under multiple viewing angles (up to 16). • 9 spectral bands between 443-1020 nm, polarization (Stokes parameters Q,U) • at 490, 670, 865 nm. • Polarimetric accuracy estimated between 0.01 (ocean)-0.02 (land)
New Generation of Algorithms • Current standard retrieval approaches are not able to cope with (potential) capabilities of POLDER: • Limited number of "standard" aerosol models. • Simplified treatment of ocean / land reflectance. • New generation of algorithms is currently under development: • No restriction to "standard" aerosol models (size distribution, refractive index, shape) but retrieve properties of aerosol models. • Retrieve land / ocean properties simultaneously with aerosol. • Fit full polarized RT model to multi-angle, multi-wavelength measurements of radiance and polarization
atmosphere ocean surface ocean body Forward Model Landgraf et al. (JQSRT 2001; JGR 2002) Hasekamp and Landgraf (JQSRT 2002; JGR 2005) Hasekamp and Butz (JGR 2008) Hasekamp et al (JGR 2011)
Atmospheric Radiative Transfer aerosol microphysical properties (mixture of spheroids and spheres LUT with T-matrix and Mie calculations (after Dubovik et al [2002;2006]) aerosol optical properties and derivatives combine with trace gas absorption and Rayleigh scattering atmosphere optical properties (for height layers) Gauss Seidel linearized vecor radiative transfer solver (Hasekamp and Landgraf[2002;2005]; Landgraf et al. [2002]) Stokes parameters I, Q, U and derivatives
Ocean Surface • Fresnel reflection on the ocean surface. • Distribution of surface slopes depends on wind speed and direction [Cox and Munk, 1954]. • Foam: albedo 0.2
Ocean body • Calculate optical ocean properties as function of [Chl-a] following Morel (2001) and Chowdhary (2006). • Solve RT for ocean body and store in LUT as function of [Chl-a]
Fit Parameters Bi-modal log-normal size distribution • Effective radius • Effective variance • Complex refractive index • Column integrated number concentration • fraction of non-spherical particles (large mode) • Oceanic Chlorophyll-a concentration. • Foam coverage. • Wind speed (2 components).
Inversion Approach Tikhonov regularization: Solve iteratively with linearized forward model G: Gain/contribution matrix, A: Averaging kernel Tilde (~) means weighted by measurement and/or prior covariance matrix
Error Analysis Retrieval noise: Effect of measurement noise (and pseudo noise) on the retrieval Regularization error: Effect of error on prior information
Selection of regularization parameter:Two methods 1. L-curve applied to linearized problem (each iteration step) combine with reduced step Gauss Newton iteration to deal with non-linearity 2. Evaluate c2(g) for each iteration step using an approximate (fast) forward model. Choose g that yields smallest c2
First guess and a priori Given the high degree of non-linearity of F(x) the choice of the 1st guess state vector is important Use LUT with pre-calculated values of I, Q, and U for 196 aerosol models. • Fit AOT (fine), AOT (coarse), phytoplankton concentration, and wind speed using LUT as forward model (interpolation between node points).
Synthetic Retrievals • 250 synthetic measurements created with varying effective radius, effective variance, refractive index, and aerosol column for fine and coarse mode. • 1% noise on radiance, 0.01 DoLP +0.002 on DoLP • 50% of the retrievals converged to satisfactory c2 (< 2) first guess still not sufficient (we can reach 100% convergence if we try different 1st guesses)
Application to Parasol over AERONET stations • 2006 - 2009. • Comparison with AERONET stations at coast or islands. • Time difference: < 1 hour, distance < 40 km
Validation AOT Hasekamp et al, JGR, 2011 (only 2006, first 10 days per month, 490 and 670 nm)
Validation (AOT and Angstrom Exponent) Same data as in JGR 2011, but now accounting for non-sphericity, use 490, 670, and 865 nm)
Validation (Single Scattering Albedo) Anmyon Solid:PARSOL Dashed: AERONET Forth Crete Shirahama RMS difference between PARASOL and AERONET is ~0.04 Hasekamp et al, JGR, 2011 Sevastopol
Refractive Index Note: AERONET inversions yield size independent refractive index, we retrieve for fine and coarse mode (averaged in the table)
Retrieval Errors Small mode Coarse mode
New retrievals • 2006-2009 • New iteration scheme
EGU conference Vienna, 8 April 2011 Forth Crete, Greece
Conclusion & Discussion • Good agreement AOT and Angstrom Exponent, some outliers in 2006-2009 dataset. • Reasonable agreement for Single scattering albedo. PARASOL systematically higher (0.025) than AERONET. Standard deviation of differences 0.03. • Discussion: how accurate is AERONET SSA? To what extend do co-location issues play a role?
