Field validation and benchmarking of a cloud shadow speed sensor

• Published in: Solar energy Vol. 173; pp. 229 - 245
• Main Authors: Kuhn, P., Wirtz, M., Wilbert, S., Bosch, J.L., Wang, G., Ramirez, L., Heinemann, D., Pitz-Paal, R.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.10.2018; Pergamon Press Inc
• Subjects: Bias; Cloud shadow speed sensor; Cloud speed; Clouds; Cumulus clouds; Curve fitting; Deserts; Leaves; Measurement; Nowcasting; Photovoltaic cells; Photovoltaics; Reference systems; Robustness (mathematics); Sensors; Shading; Shadow camera system; Shadows; Solar energy; Weather forecasting; Shadow camera system; Cloud shadow speed sensor; Cloud speed
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2018.07.053

•The Cloud Shadow Speed Sensor (CSS) is field-validated in southern Spain on 59 days.•A reference shadow camera based system is developed and used for the validation.•The detection rate of the CSS is investigated on 223 days.•If applicable, a shadow camera approach has advantages compared to the CSS.•Long term cloud motion vector validations of NWP and satellite models are possible. With ramp rate regulations for photovoltaic plants being discussed in many countries, the speed of clouds has gained significant importance lately. Besides, measuring cloud velocities and directions is of interest for validations of numerical weather predictions and solar nowcasting systems. Recently, the Cloud Shadow Speed Sensor (CSS) was developed and validated in San Diego for low cumulus clouds. In this publication, the CSS is studied under different weather and cloud conditions in the desert of Tabernas in southern Spain. Furthermore, a novel shadow camera based low-cost, low-maintenance approach to determine cloud shadow motion vectors is presented and used as a reference to benchmark the CSS. In comparison, the absolute velocities derived from the CSS and the shadow camera on 59 days for ±5 min temporal medians show deviations of RMSD 2.1 m/s (28.0%), MAD 1.2 m/s (15.7%) and a bias of −0.2 m/s (2.8%). Deviations of the cloud shadow direction are RMSD 47.9° (26.6%), MAD 25.3° (14.0%) and bias 3.7° (2.0%). An adaption of the CSS software yields 91% more measurements on 59 days in comparison to the previously used algorithms at the expense of reduced accuracies, both for the measured velocities and for the measured directions. The CSS and the novel shadow camera based reference system enable long-time, low-maintenance ground measurements of cloud shadow speeds, which were previously not available. The distinct advantages and limitations of the two systems are discussed. In addition to the comparisons between the shadow camera system and the CSS on 59 days, the detection rates of the CSS are classified and measured on 223 days by analyzing CSS radiometer signals. Depending on the shading strength and shading durations, detection rates vary between 3.7% and 21.6%. Furthermore, the basic assumption as well as possible correction approaches of the linear cloud edge – curve fitting method are studied. The CSS was found to be a robust tool with great potential. However, optically thin clouds with diffuse edges pose a challenge and the detection rate leaves room for improvements. The newly developed shadow camera system provides more measurements which scatter less but needs certain geographical requirements. The shadow camera is found to be a feasible validation tool for cloud (shadow) motion vectors.