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The VIIRS Day Night Band sensor on the Suomi NPP satellite provides almost a decade of observations of night light. The daily frequency of sampling, without the temporal averaging of annual composites, requires the distinction between apparent changes of imaged night light related to the imaging process and actual changes in the underlying sources of the light being imaged. This study characterizes night light variability over a range of spatial and temporal scales to provide a context for interpretation of changes on both subannual and interannual time scales. This analysis uses a combination of temporal moments, spatial correlation and Empirical Orthogonal Function (EOF) analysis. A key result is the pervasive heteroskedasticity of VIIRS monthly mean night light. Specifically, the monotonic decrease of temporal variability with increasing mean brightness. Anthropogenic night light is remarkably stable on subannual time scales. Overall variance partition derived from the eigenvalues of the spatiotemporal covariance matrix are 88%, 2% and 2% for spatial, seasonal and interannual variance in the most diverse geographic region on Earth (Eurasia). Heteroskedasticity is present in all areas for all months, suggesting that much, if not most, of observed month-to-month variability may result from luminance of otherwise stable sources subjected to multiple aspects of the imaging process varying in time. Given the skewed distribution of all night light arising from radial peripheral dimming of bright sources, even aggregate metrics using thresholds must be interpreted in light of the fact that much larger numbers of more variable low luminance pixels may statistically overwhelm smaller numbers of stable higher luminance pixels and cause apparent changes related to the imaging process to be interpreted as actual changes in the light sources.
We conduct numerical simulations based on a model of blowing snow to reveal the long-term properties and equilibrium state of aeolian particle transport from $10^{-5} hspace{0.5 ex} mathrm{m}$ to $10 hspace{0.5 ex} mathrm{m}$ above the flat surface.
We develop a non-linear semi-parametric Gaussian process model to estimate periods of Miras with sparsely-sampled light curves. The model uses a sinusoidal basis for the periodic variation and a Gaussian process for the stochastic changes. We use max
Mazzarella and Scafetta (2016) showed that the seismic activity recorded at the Bunker-East (BKE) Vesuvian station from 1999 to 2014 suggests a higher nocturnal seismic activity. However, this station is located at about 50 m from the main road to th
In this paper, we present an analysis of seismic spectra that were calculated from all broadband channels (BH?) made available through IRIS, NIED F-net and Orfeus servers covering the past five years and beyond. A general characterization of the data
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. In high stakes domains, being able to generate probabil