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A New Cloud Screening Algorithm for Ground-Based Direct-Beam Solar Radiation

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  • 1 UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado
  • | 2 UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, and Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado
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Abstract

Cloud screening of direct-beam solar radiation is an essential step for in situ calibration and atmospheric properties retrieval. The internal cloud screening module of a Langley analysis program [Langley Analyzer (LA)] used by the U.S. Department of Agriculture (USDA) UV-B Monitoring and Research Program (UVMRP) is used for screening the uncalibrated direct-beam measurements and for deriving Langley offset voltages for calibration of the UV version of the Multifilter Rotating Shadowband Radiometer (UV-MFRSR). The current LA cloud screening module utilizes data from extended clear-sky periods and tends to ignore shorter periods that typify periods of broken cloudiness, and as a result, fewer values are generated for sites with higher frequencies of cloudy days (cloudy sites). A new cloud screening algorithm is presented that calculates the total optical depth (TOD) difference between a target point and pairs of points, and identifies the target as cloudy if the mean TOD difference exceeds a certain threshold. The screening is an iterative process that finishes when no new cloudy points are found. The result at a typical clear/sunny site shows that values from partly cloudy days are consistent with those from cloud-free days, when the new method is employed. The new cloud screening algorithm picks up significantly more values at cloudy sites. The larger decrease of the annual mean value of at cloudy sites than at relatively clear sites suggests the potential for improving calibration accuracy at cloudy sites. The results also show that the new cloud screening method is capable of detecting clear points in short clear windows and in transitional regions.

Corresponding author address: Maosi Chen, UV-B Monitoring and Research Program, Colorado State University, 1499 Campus Delivery, Fort Collins, CO 80523-1499. E-mail: maosi.chen@colostate.edu

Abstract

Cloud screening of direct-beam solar radiation is an essential step for in situ calibration and atmospheric properties retrieval. The internal cloud screening module of a Langley analysis program [Langley Analyzer (LA)] used by the U.S. Department of Agriculture (USDA) UV-B Monitoring and Research Program (UVMRP) is used for screening the uncalibrated direct-beam measurements and for deriving Langley offset voltages for calibration of the UV version of the Multifilter Rotating Shadowband Radiometer (UV-MFRSR). The current LA cloud screening module utilizes data from extended clear-sky periods and tends to ignore shorter periods that typify periods of broken cloudiness, and as a result, fewer values are generated for sites with higher frequencies of cloudy days (cloudy sites). A new cloud screening algorithm is presented that calculates the total optical depth (TOD) difference between a target point and pairs of points, and identifies the target as cloudy if the mean TOD difference exceeds a certain threshold. The screening is an iterative process that finishes when no new cloudy points are found. The result at a typical clear/sunny site shows that values from partly cloudy days are consistent with those from cloud-free days, when the new method is employed. The new cloud screening algorithm picks up significantly more values at cloudy sites. The larger decrease of the annual mean value of at cloudy sites than at relatively clear sites suggests the potential for improving calibration accuracy at cloudy sites. The results also show that the new cloud screening method is capable of detecting clear points in short clear windows and in transitional regions.

Corresponding author address: Maosi Chen, UV-B Monitoring and Research Program, Colorado State University, 1499 Campus Delivery, Fort Collins, CO 80523-1499. E-mail: maosi.chen@colostate.edu
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