Optimization of the Photon Path Length Probability Density Function-Simultaneous (PPDF-S) Method and Evaluation of...

Area of Science:

• Earth and Atmospheric Sciences
• Remote Sensing
• Environmental Science

Background:

• The Greenhouse gases Observing Satellite (GOSAT) uses the photon path length probability density function-simultaneous (PPDF-S) algorithm to retrieve column-averaged concentrations of carbon dioxide (XCO₂) and methane (XCH₄) from Short Wavelength InfraRed (SWIR) spectra.
• Atmospheric aerosols and clouds modify light paths, impacting the accuracy of XCO₂ and XCH₄ retrievals by altering atmospheric transmittance.

Purpose of the Study:

• To optimize PPDF-S algorithm parameters for more accurate XCO₂ retrieval under dense aerosol conditions.
• To enhance the stability and accuracy of XCO₂ retrieval by refining PPDF parameter constraints.
• To investigate the potential of PPDF parameters in identifying atmospheric aerosol types.

Main Methods:

• Simulation studies were conducted using various aerosol types and surface albedos to optimize PPDF parameters.
• The optimized PPDF parameters were applied to GOSAT data from Western Siberia, initially under clear sky conditions near Yekaterinburg.
• The retrieved XCO₂ was validated against ground-based Fourier Transform Spectrometer (FTS) measurements.
• The optimized method was then applied to GOSAT data during biomass burning events with dense smoke aerosols.

Main Results:

• Optimization of PPDF parameters led to a stable XCO₂ solution, even under weak aerosol reflectance effects.
• Retrieval accuracy was validated as reasonable when compared to ground-based FTS measurements in clear sky conditions.
• Application to smoky conditions during biomass burning increased the total number of retrieved XCO₂ data by approximately 70%.
• Simulation and data analysis suggest PPDF parameter values correlate with specific atmospheric aerosol types.

Conclusions:

• The optimized PPDF-S algorithm significantly improves XCO₂ retrieval accuracy and data yield under challenging aerosol conditions.
• The PPDF parameter values show potential for identifying and characterizing atmospheric aerosol types, which can further refine retrieval algorithms.
• This research contributes to more reliable greenhouse gas monitoring from space, particularly in regions affected by aerosols.