Publication | Open Access
A model for the diffuse attenuation coefficient of downwelling irradiance
382
Citations
64
References
2005
Year
Radiative Transfer EquationEnvironmental MonitoringEngineeringOceanographyEarth ScienceRadiative TransferAtmospheric ScienceReflectance ModelingRadiative AbsorptionGeographyRadiation MeasurementRadiation TransportRadiometryOcean Remote SensingRadiative Transfer ModellingDiffuse Attenuation CoefficientAtmospheric RadiationRemote SensingK D
The diffuse attenuation coefficient of downwelling irradiance (Kd) is a key oceanographic parameter, but current remote‑sensing estimates rely on empirical algorithms that yield large uncertainties and limited insight into Kd variability. This study seeks to develop a semianalytical model for Kd. The model derives from the radiative‑transfer equation, with parameters obtained from Hydrolight simulations using averaged particle phase functions. When tested against data generated with diverse particle phase functions, the model reproduces Hydrolight Kd values with an average error of ~2 % and a maximum error of ~12 %, offering a more accurate and interpretable basis for remote‑sensing Kd retrievals.
The diffuse attenuation coefficient for downwelling irradiance ( K d ) is an important parameter for ocean studies. For the vast ocean the only feasible means to get fine‐scale measurements of K d is by ocean color remote sensing. At present, values of K d from remote sensing are estimated using empirical algorithms. Such an approach is insufficient to provide an understanding regarding the variation of K d and contains large uncertainties in the derived values. In this study a semianalytical model for K d is developed based on the radiative transfer equation, with values of the model parameters derived from Hydrolight simulations using the averaged particle phase function. The model is further tested with data simulated using significantly different particle phase functions, and the modeled K d are found matching Hydrolight K d very well (∼2% average error and ∼12% maximum error). Such a model provides an improved interpretation about the variation of K d and a basis to more accurately determine K d (especially using data from remote sensing).
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