scholarly journals Regional analysis of wind velocity patterns in complex terrain

Geofizika ◽  
2020 ◽  
Vol 36 (2) ◽  
pp. 111-130
Author(s):  
Radian Belu ◽  
Darko Koračin
Keyword(s):  
Statistical Analysis ◽  
Wind Energy ◽  
Wind Velocity ◽  
Time Scales ◽  
Complex Terrain ◽  
Spatial Scales ◽  
Fluctuation Analysis ◽  
Northern Nevada ◽  
Cross Correlation Analysis ◽  
Spatio Temporal

Wind energy is a weather and climate-dependent energy resource with natural spatio-temporal variabilities at time scales ranging from fraction of seconds to seasons and years, while at spatial scales it is strongly affected by the terrain and vegetation. To optimize wind energy systems and maximize the energy extraction, wind measurements on various time scales as well as wind energy forecasts are required and needed. This study focuses on spatio-temporal characteristics of the wind velocity in complex terrain, relevant to wind energy assessment, operation, and grid integration, using data collected at 11 towers ranging from 40 to 80 m tall over a 12-year period in complex terrain of western-central and northern Nevada, USA. The autocorrelation analysis, Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA) showed strong coherence between the wind speed and direction with slowly decreasing amplitude of the multi-day periodicity with increasing lag periods. Besides pronounced diurnal periodicity at all locations, statistical analysis and DFA also showed significant seasonal and annual periodicities, long-memory persistence with similar characteristics at all sites and towers with a relatively narrow range of the Weibull parameters. The DCCA indicates similar wind patterns at each tower, and strong correlations between measurement sites in spite of separations of about 300 km across the towers’ setup. The northern Nevada area exhibits higher wind resource potential and higher wind persis-tence compared to the western-central region. Overall, the DFA and DCCA results suggest higher degree of complementarity among wind data at measure-ment sites compared to previous standard statistical analysis.

scholarly journals Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

2019 ◽  
Vol 7 (3) ◽  
pp. 51 ◽  
Author(s):  
Natália Costa ◽  
César Silva ◽  
Paulo Ferreira
Keyword(s):  
Time Scales ◽  
Cross Correlation ◽  
Data Availability ◽  
Fluctuation Analysis ◽  
Financial Assets ◽  
Sliding Windows ◽  
Cross Correlation Analysis ◽  
Short Time ◽  
Detrended Fluctuation ◽  
Great Development

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

scholarly journals Correlation Structures of PM2.5 Concentration Series in the Korean Peninsula

2019 ◽  
Vol 9 (24) ◽  
pp. 5441
Author(s):  
Gyuchang Lim ◽  
Seungsik Min
Keyword(s):  
Cross Correlation ◽  
Moving Average ◽  
Air Pollutant ◽  
Fluctuation Analysis ◽  
Auto Correlation ◽  
Cross Correlation Analysis ◽  
Correlation Structures ◽  
Spatio Temporal ◽  
Pm2.5 Concentration ◽  
Mass Concentrations

In this paper, the authors investigate the idiosyncratic features of auto- and cross-correlation structures of PM2.5 (particulate matter of diameter less than 2.5 μ m ) mass concentrations using DFA (detrended fluctuation analysis) methodologies. Since air pollutant mass concentrations are greatly affected by geographical, topographical, and meteorological conditions, their correlation structures can have non-universal properties. To this end, the authors firstly examine the spatio-temporal statistics of PM2.5 daily average concentrations collected from 18 monitoring stations in Korea, and then select five sites from those stations with overall lower and higher concentration levels in order to make up two groups, namely, G1 and G2, respectively. Firstly, to compare characteristic behaviors of the auto-correlation structures of the two groups, we performed DFA and MFDFA (multifractal DFA) analyses on both and then confirmed that the G2 group shows a clear crossover behavior in DFA and MFDFA analyses, while G1 shows no crossover. This finding implies that there are possibly two different scale-dependent underlying dynamics in G2. Furthermore, in order to confirm that different underlying dynamics govern G1 and G2, the authors conducted DCCA (detrended cross-correlation analysis) analysis on the same and different groups. As a result, in the same group, coupling behavior became more prominent between two series as the scale increased, while, in the different group, decoupling behavior was observed. This result also implies that different dynamics govern G1 and G2. Lastly, we presented a stochastic model, namely, ARFIMA (auto-regressive fractionally integrated moving average) with periodic trends, to reproduce behaviors of correlation structures from real PM2.5 concentration time series. Although those models succeeded in reproducing crossover behaviors in the auto-correlation structure, they yielded no valid results in decoupling behavior among heterogeneous groups.

scholarly journals Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 363
Author(s):  
George Duffy ◽  
Fraser King ◽  
Ralf Bennartz ◽  
Christopher G. Fletcher
Keyword(s):  
Time Scales ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Snow Accumulation ◽  
High Latitudes ◽  
Percentage Points ◽  
Snow Water ◽  
Time Periods ◽  
Predicted Values ◽  
Good Agreement

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

scholarly journals Depositional setting and limiting factors of early Late Cretaceous glaucony formation: implications from Cenomanian glauconitic strata (Elbtal Group, Germany)

Facies ◽  
2021 ◽  
Vol 67 (3) ◽  
Author(s):  
Markus Wilmsen ◽  
Udita Bansal
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Time Scales ◽  
Late Cretaceous ◽  
Limiting Factors ◽  
High Accumulation ◽  
Spatio Temporal ◽  
Facies Types ◽  
Facies Succession ◽  
Early Late

AbstractCenomanian strata of the Elbtal Group (Saxony, eastern Germany) reflect a major global sea-level rise and contain, in certain intervals, a green authigenic clay mineral in abundance. Based on the integrated study of five new core sections, the environmental background and spatio-temporal patterns of these glauconitic strata are reconstructed and some general preconditions allegedly needed for glaucony formation are critically questioned. XRD analyses of green grains extracted from selected samples confirm their glauconitic mineralogy. Based on field observations as well as on the careful evaluation of litho- and microfacies, 12 glauconitc facies types (GFTs), broadly reflecting a proximal–distal gradient, have been identified, containing granular and matrix glaucony of exclusively intrasequential origin. When observed in stratigraphic succession, GFT-1 to GFT-12 commonly occur superimposed in transgressive cycles starting with the glauconitic basal conglomerates, followed up-section by glauconitic sandstones, sandy glauconitites, fine-grained, bioturbated, argillaceous and/or marly glauconitic sandstones; glauconitic argillaceous marls, glauconitic marlstones, and glauconitic calcareous nodules continue the retrogradational fining-upward trend. The vertical facies succession with upwards decreasing glaucony content demonstrates that the center of production and deposition of glaucony in the Cenomanian of Saxony was the nearshore zone. This time-transgressive glaucony depocenter tracks the regional onlap patterns of the Elbtal Group, shifting southeastwards during the Cenomanian 2nd-order sea-level rise. The substantial development of glaucony in the thick (60 m) uppermost Cenomanian Pennrich Formation, reflecting a tidal, shallow-marine, nearshore siliciclastic depositional system and temporally corresponding to only ~ 400 kyr, shows that glaucony formation occurred under wet, warm-temperate conditions, high accumulation rates and on rather short-term time scales. Our new integrated data thus indicate that environmental factors such as great water depth, cool temperatures, long time scales, and sediment starvation had no impact on early Late Cretaceous glaucony formation in Saxony, suggesting that the determining factors of ancient glaucony may be fundamentally different from recent conditions and revealing certain limitations of the uniformitarian approach.

scholarly journals Are We Doing ‘Systems’ Research? An Assessment of Methods for Climate Change Adaptation to Hydrohazards in a Complex World

2019 ◽  
Vol 11 (4) ◽  
pp. 1163 ◽  
Author(s):  
Melissa Bedinger ◽  
Lindsay Beevers ◽  
Lila Collet ◽  
Annie Visser
Keyword(s):  
Climate Change ◽  
Human Nature ◽  
Climate Change Adaptation ◽  
Time Scales ◽  
Spatial Scales ◽  
Local Context ◽  
Multiple Time Scales ◽  
Future Research ◽  
Multiple Time ◽  
Systems Research

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for ‘systems’ research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research.

Statistical analysis of coastal morphological data sets over seasonal to decadal time scales

2008 ◽  
Vol 55 (7-8) ◽  
pp. 581-600 ◽  
Author(s):  
Aart Kroon ◽  
Magnus Larson ◽  
Iris Möller ◽  
Hiromune Yokoki ◽  
Grzegorz Rozynski ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Time Scales ◽  
Morphological Data ◽  
Data Sets

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

scholarly journals Spatio-Temporal Dynamic Architecture of Living Brush Mattress: Root System and Soil Shear Strength in Riverbanks

Forests ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 493 ◽  
Author(s):  
Dong Zhang ◽  
Jinhua Cheng ◽  
Ying Liu ◽  
Hongjiang Zhang ◽  
Lan Ma ◽  
...  
Keyword(s):  
Shear Strength ◽  
Root Morphology ◽  
Spatial Scales ◽  
Root Diameter ◽  
Slope Position ◽  
Protective Effects ◽  
Salix Alba ◽  
Ecological Protection ◽  
Soil Shear Strength ◽  
Spatio Temporal

As a basal measure of soil bioengineering, the living brush mattress has been widely applied in riparian ecological protection forest construction. The living brush mattress shows favorable protective effects on riverbanks. However, there are few reports on the root structure and the soil strengthening benefit of the living brush mattress. The present work reports a series of experiments on root morphology and soil shear strength enhancement at the temporal and spatial scales. The object of the study is 24 living brush mattress trees constructed with Salix alba L. ‘Tristis’ (LBS hereafter). Traditional root morphology and mechanical measurement methods were used to collect the parameters. The results showed that the root systems of LBS had the characteristics of symmetry and upslope growth. The roots were mainly distributed in a cylindrical region of the soil (radius × thickness: 0.4 m × 0.5 m) and their biomass increased with different growth rates for the periods from 1 to 5 and from 5 to 7 years. Both age and slope position were factors that influence root growth. The root diameter falls within 0–5 mm, has a significant effect on the soil shear strength and provides a conical-shape potentiation zone to ensure the efficient protection of a riverbank. The results of this study demonstrate that LBS is an efficient and feasible engineering measure in the field of riverbank protection.

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