Satellite-based snow identification and its impact on monitoring photovoltaic systems

Earth observation allows the separation of snow cover and cloudiness using multispectral measurements. Several satellite-based snow monitoring services are available, ranging from regional to world-wide scales. Using these data enables photovoltaic (PV) plant management to differentiate between failures due to snow coverage on a PV system and other error sources. Additionally, yield estimates for solar siting are improved. This paper presents a validation study from January to April 2006 comparing satellite-based datasets with ground measurements from German and Swiss meteorological stations. A false alarm rate, an error due to irradiance underestimation, the availability of daily data, and the classification accuracy are introduced as quality metrics. Compared to Switzerland, generally a higher accuracy is found in all datasets for Southern Germany. The most significant difference among the datasets is found in the error pattern shifting from too much snow (which results in an error due to underestimation of irradiance) to too little snow detection, causing a false alarm in PV monitoring.Overall, the data records of the Land Surface Analysis Satellite Application Facility (LSA SAF), the German Aerospace Center (DLR) and the Interactive Multisensor Snow and Ice Mapping System (IMS) are found to be most suitable for solar energy purposes. The IMS dataset has a low false alarm rate (4%) and a good data availability (100%) making it a good choice for power plant monitoring, but the error due to underestimation relevant in site auditing is large with 59%. If a cumulative snow cover algorithm is applied to achieve information every day as needed both for power plant monitoring and site auditing, both the DLR and the LSA SAF datasets are comparable with classification accuracies of 70%, false alarm rates of 37% and 34%, respectively, and errors due to irradiance underestimation in 26% and 27% of all coincidences.