We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. Here we develop new near-global, fine-grain (≈1km) monthly cloud frequencies from 15 years of twice-daily MODIS satellite images that expose spatio-temporal cloud cover dynamics of previously undocumented global complexity. If captured over large extent yet at sufficiently fine spatial grain cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Hourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from and The Weather Channel. pbio.1002415 Dataset DetailsĬloud cover can influence numerous important ecological processes including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. Wilson AM, Jetz W (2016) Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions. Based on satellite images, moving clouds are detected and their vectors are calculated using the optical flow method. For this purpose, this paper presents a forecast model that predicts the local cloud cover over a PV system.
For additional information about the integration approach and the evaluations of the datasets, please see the associated journal article: A major influence on the generated power of a photovoltaic (PV) system is the cloud cover over the system. The datasets integrate 15 years of twice-daily remote sensing-derived cloud observations at 1-km resolution.
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