研究目的
To quantify the effects of phytoplankton morphology assumptions and phytoplankton population vertical profiles in an oceanic column on lidar attenuated backscatter and depolarization ratio signals.
研究成果
The sensitivity study indicates that both phytoplankton morphology and vertical density variation significantly impact lidar measurements. Lidar polarization is useful for investigating morphology, but inclusions have less effect than overall shape. The attenuated backscatter is sensitive to vertical profiles, allowing identification of mixed layer depth, but discretization issues need improvement. Future work should use smoother variations and more efficient radiative transfer solvers.
研究不足
The study assumes idealized conditions, such as specific phytoplankton morphologies and refractive indices, and uses discretized layers which may introduce artifacts. The Cox-Munk model for ocean surface roughness is sufficient but not highly realistic. The influence of nutrient limitations and other environmental factors is neglected. Computational limitations affect the size range and volume fractions in light scattering calculations.
1：Experimental Design and Method Selection:
A vectorized Monte Carlo radiative transfer solver is used for lidar beam propagation in a coupled atmosphere-ocean model. The study involves solving the vector radiative transfer equation using Monte Carlo integration, with modifications for ocean-atmosphere interface handling, including specular reflection and refraction, and explicit treatment of Rayleigh scattering.
2：Sample Selection and Data Sources:
Phytoplankton particles are modeled as spheres, spheres with cores, or randomly distorted hexahedra with or without cores. Vertical phytoplankton distribution is derived from a PAR-limited carbon biomass balance equation. Atmospheric data includes Rayleigh scattering profiles from standard models.
3：List of Experimental Equipment and Materials:
Computational tools include Monte Carlo radiative transfer code, light-scattering computational methods (e.g., Multiple-Sphere T-Matrix Method, Invariant-Imbedding T-Matrix Method), and models for ocean surface roughness (Cox-Munk model). No physical equipment is mentioned; the study is computational.
4：Experimental Procedures and Operational Workflow:
The Monte Carlo solver is applied with variance reduction techniques. Simulations are conducted for different phytoplankton morphologies, vertical profiles, off-nadir angles, and ocean surface roughness conditions. Convergence is checked with up to 4×10^6 Monte Carlo trajectories.
5：Data Analysis Methods:
Results are analyzed for attenuated backscatter and depolarization ratio sensitivities. Comparisons are made with analytical solutions for validation.
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