期刊
REMOTE SENSING
卷 14, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/rs14225685
关键词
cloud camera; cloud modeling; nowcast; forecast; solar irradiance; cloud transmittance
类别
资金
- European Union [864337]
All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. A novel procedure is developed using a network of ASIs to accurately model clouds and determine cloud attenuation over every location in the observed area. The approach combines images from neighboring ASIs to detect areas covered by different types of clouds and transforms them into maps of attenuation index. These maps are then combined using an accuracy-weighted average, and a probabilistic procedure is used to derive a map of cloud attenuation. The ASI network significantly reduces errors compared to current methods in all atmospheric conditions.
All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m x 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation's local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network's estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m(2), GHI 98 W/m(2)) compared to the ASI pair (RMSD for DNI 173 W/m(2), GHI 119 W/m(2) and radiometer alone (RMSD for DNI 213 W/m(2)), GHI 140 W/m(2)). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据