研究目的
Investigating the method for retrieving cloud optical depth using ground-based sky imagery and radiative transfer models to improve solar forecasting accuracy.
研究成果
The RRBR method provides accurate τc for overcast thick clouds but shows larger relative RMSE for τc < 10. The method is validated for overcast clouds but requires improvement for heterogeneous cloud conditions. The procedure developed provides a foundation to test and develop other cloud detection algorithms.
研究不足
The method is currently computationally infeasible to use SHDOM to solve τc in real time as required for sky imager solar forecasting. The study focuses on liquid water clouds and does not address ice clouds or multiple cloud layers. The method's accuracy is lower for thin clouds (τc < 10).
1:Experimental Design and Method Selection:
The study uses the spherical harmonic discrete ordinate method (SHDOM) for radiative transfer calculations to produce synthetic overcast sky images.
2:Sample Selection and Data Sources:
Data from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for the year 2013 are used, including AERONET data for aerosol properties.
3:List of Experimental Equipment and Materials:
UCSD developed ground-based sky imager (USI), microwave radiometer (MWR), and a multi-filter rotating shadowband radiometer (MFRSR).
4:Experimental Procedures and Operational Workflow:
Daytime images from the USIs were collected continuously every 30 s for 220 days. The radiance red–blue ratio (RRBR) method is applied to these images.
5:Data Analysis Methods:
The RRBR method combines the RBR and radiance to overcome the non-monotonic nature of each individual parameter for cloud optical depth retrieval.
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