Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSpatiotemporal Characterization of VIIRS Night Light
108
0
0.0
(
0
)
Authors
Christopher Small
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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. The numerical results are as follows. (i) Time-series data of particle transport are divided into development, relaxation, and equilibrium phases, which are formed by rapid wind response below $10 hspace{0.5 ex} mathrm{cm}$ and gradual wind response above $10 hspace{0.5 ex} mathrm{cm}$. (ii) The particle transport rate at equilibrium is expressed as a power function of friction velocity, and the index of 2.35 implies that most particles are transported by saltation. (iii) The friction velocity below $100 hspace{0.5 ex} mumathrm{m}$ remains roughly constant and lower than the fluid threshold at equilibrium. (iv) The mean particle speed above $300 hspace{0.5 ex} mumathrm{m}$ is less than the wind speed, whereas that below $300 hspace{0.5 ex} mumathrm{m}$ exceeds the wind speed because of descending particles. (v) The particle diameter increases with height in the saltation layer, and the relationship is expressed as a power function. Through comparisons with the previously reported random-flight model, we find a crucial problem that empirical splash functions cannot reproduce particle dynamics at a relatively high wind speed.
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 maximum likelihood to estimate the period and the parameters of the Gaussian process, while integrating out the effects of other nuisance parameters in the model with respect to a suitable prior distribution obtained from earlier studies. Since the likelihood is highly multimodal for period, we implement a hybrid method that applies the quasi-Newton algorithm for Gaussian process parameters and search the period/frequency parameter over a dense grid. A large-scale, high-fidelity simulation is conducted to mimic the sampling quality of Mira light curves obtained by the M33 Synoptic Stellar Survey. The simulated data set is publicly available and can serve as a testbed for future evaluation of different period estimation methods. The semi-parametric model outperforms an existing algorithm on this simulated test data set as measured by period recovery rate and quality of the resulting Period-Luminosity relations.
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 the volcanos crater and since 2009 its seismograms also record a significant diurnal cultural noise due mostly to tourist tours to Mt. Vesuvius. Herein, we investigate whether the different seismic frequency between day and night times could be an artifact of the peculiar cultural noise that affects this station mostly from 9:00 am to 5:00 pm from spring to fall. This time-distributed cultural noise should evidently reduce the possibility to detect low magnitude earthquakes during those hours but not high magnitude events. Using hourly distributions referring to different magnitude thresholds from M = 0.2 to M = 2.0, the Gutenberg-Richter magnitude-frequency diagram applied to the day and night-time sub-catalogs and Montecarlo statistical modeling, we demonstrate that the day-night asymmetry persists despite an evident disruption induced by cultural noise during day-hours. In particular, for the period 1999-2017, and for earthquakes with M > 2 we found a Gutenberg-Richter exponent b = 1.66 +/- 0.07 for the night-time events and b = 2.06 +/- 0.07 for day-time events. Moreover, we repeat the analysis also for an older BKE catalog covering the period from 1992 to 2000 when cultural noise was not present. The analysis confirms a higher seismic nocturnal activity that is also characterized by a smaller Gutenberg-Richter exponent b for M > 2 earthquakes relative to the day-time activity. Thus, the found night-day seismic asymmetric behavior is likely due to a real physical feature affecting Mt. Vesuvius.
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 is given in terms of spectral histograms and data-availability plots. We show that the spectral information can easily be categorized in time and regions. Spectral histograms indicate that seismic stations exist in Africa, Australia and Antarctica that measure spectra significantly below the global low-noise models above 1 Hz. We investigate world-wide coherence between the seismic spectra and other data sets like proximity to cities, station elevation, earthquake frequency, and wind speeds. Elevation of seismic stations in the US is strongly anti-correlated with seismic noise near 0.2 Hz and again above 1.5 Hz. Urban settlements are shown to produce excess noise above 1 Hz, but correlation curves look very different depending on the region. It is shown that wind speeds can be strongly correlated with seismic noise above 0.1 Hz, whereas earthquakes produce seismic noise that shows most clearly in correlation around 80 mHz.
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 probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. Hence, a systematic study of uncertainty quantification (UQ) methods for spatiotemporal forecasting is missing in the community. In this paper, we describe two types of spatiotemporal forecasting problems: regular grid-based and graph-based. Then we analyze UQ methods from both the Bayesian and the frequentist point of view, casting in a unified framework via statistical decision theory. Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical and computational trade-offs for different UQ methods: Bayesian methods are typically more robust in mean prediction, while confidence levels obtained from frequentist methods provide more extensive coverage over data variations. Computationally, quantile regression type methods are cheaper for a single confidence interval but require re-training for different intervals. Sampling based methods generate samples that can form multiple confidence intervals, albeit at a higher computational cost.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
