scholarly journals Spectral evolution of weakly nonlinear random waves: kinetic description versus direct numerical simulations

2018 ◽  
Vol 844 ◽  
pp. 766-795 ◽  
Author(s):  
Sergei Y. Annenkov ◽  
Victor I. Shrira
Keyword(s):  
Time Scale ◽  
Kinetic Equations ◽  
Wave Spectra ◽  
Kinetic Description ◽  
Spectral Evolution ◽  
Short Term ◽  
Weakly Nonlinear ◽  
Term Evolution ◽  
Integral Characteristics

Kinetic equations are widely used in many branches of science to describe the evolution of random wave spectra. To examine the validity of these equations, we study numerically the long-term evolution of water wave spectra without wind input using three different models. The first model is the classical kinetic (Hasselmann) equation (KE). The second model is the generalised kinetic equation (gKE), derived employing the same statistical closure as the KE but without the assumption of quasistationarity. The third model, which we refer to as the DNS-ZE, is a direct numerical simulation algorithm based on the Zakharov integrodifferential equation, which plays the role of the primitive equation for a weakly nonlinear wave field. It does not employ any statistical assumptions. We perform a comparison of the spectral evolution of the same initial distributions without forcing, with/without a statistical closure and with/without the quasistationarity assumption. For the initial conditions, we choose two narrow-banded spectra with the same frequency distribution and different degrees of directionality. The short-term evolution ($O(10^{2})$ wave periods) of both spectra has been previously thoroughly studied experimentally and numerically using a variety of approaches. Our DNS-ZE results are validated both with existing short-term DNS by other methods and with available laboratory observations of higher-order moment (kurtosis) evolution. All three models demonstrate very close evolution of integral characteristics of the spectra, approaching with time the theoretical asymptotes of the self-similar stage of evolution. Both kinetic equations give almost identical spectral evolution, unless the spectrum is initially too narrow in angle. However, there are major differences between the DNS-ZE and gKE/KE predictions. First, the rate of angular broadening of initially narrow angular distributions is much larger for the gKE and KE than for the DNS-ZE, although the angular width does appear to tend to the same universal value at large times. Second, the shapes of the frequency spectra differ substantially (even when the nonlinearity is decreased), the DNS-ZE spectra being wider than the KE/gKE ones and having much lower spectral peaks. Third, the maximal rates of change of the spectra obtained with the DNS-ZE scale as the fourth power of nonlinearity, which corresponds to the dynamical time scale of evolution, rather than the sixth power of nonlinearity typical of the kinetic time scale exhibited by the KE. The gKE predictions fall in between. While the long-term DNS show excellent agreement with the KE predictions for integral characteristics of evolving wave spectra, the striking systematic discrepancies for a number of specific spectral characteristics call for revision of the fundamentals of the wave kinetic description.

scholarly journals Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales

2019 ◽  
Vol 20 (6) ◽  
pp. 1165-1182 ◽  
Author(s):  
Kaighin A. McColl ◽  
Qing He ◽  
Hui Lu ◽  
Dara Entekhabi
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Time Scale ◽  
Surface Soil ◽  
Satellite Observations ◽  
Surface Soil Moisture ◽  
Short Term ◽  
Term Memory ◽  
Memory Time

Abstract Land–atmosphere feedbacks occurring on daily to weekly time scales can magnify the intensity and duration of extreme weather events, such as droughts, heat waves, and convective storms. For such feedbacks to occur, the coupled land–atmosphere system must exhibit sufficient memory of soil moisture anomalies associated with the extreme event. The soil moisture autocorrelation e-folding time scale has been used previously to estimate soil moisture memory. However, the theoretical basis for this metric (i.e., that the land water budget is reasonably approximated by a red noise process) does not apply at finer spatial and temporal resolutions relevant to modern satellite observations and models. In this study, two memory time scale metrics are introduced that are relevant to modern satellite observations and models: the “long-term memory” τL and the “short-term memory” τS. Short- and long-term surface soil moisture (SSM) memory time scales are spatially anticorrelated at global scales in both a model and satellite observations, suggesting hot spots of land–atmosphere coupling will be located in different regions, depending on the time scale of the feedback. Furthermore, the spatial anticorrelation between τS and τL demonstrates the importance of characterizing these memory time scales separately, rather than mixing them as in previous studies.

scholarly journals Temporal and spatial analysis of ozone concentrations in Europe based on time scale decomposition and a multi-clustering approach

2019 ◽  
Author(s):  
Eirini Boleti ◽  
Christoph Hueglin ◽  
Stuart K. Grange ◽  
André S. H. Prévôt ◽  
Satoshi Takahama
Keyword(s):  
Time Scale ◽  
Seasonal Cycle ◽  
Clustering Algorithm ◽  
Short Term ◽  
Ozone Concentrations ◽  
Time Period ◽  
Term Variation ◽  
Long Term Trends ◽  
Time Scale Decomposition

Abstract. Air quality measures that were implemented in Europe in the 1990s resulted in reductions of ozone precursors concentrations. In this study, the effect of these reductions on ozone is investigated by analyzing surface measurements of ozone for the time period between 2000 and 2015. Using a non-parametric time scale decomposition methodology, the long-term, seasonal and short-term variation of ozone observations were extracted. A clustering algorithm was applied to the different time scale variations, leading to a classification of sites across Europe based on the temporal characteristics of ozone. The clustering based on the long-term variation resulted in a site type classification, while a regional classification was obtained based on the seasonal and short-term variations. Long-term trends of de-seasonalized mean and meteo-adjusted peak ozone concentrations were calculated across large parts of Europe for the time period 2000–2015. A multi-dimensional scheme was used for a detailed trend analysis, based on the identified clusters, which reflect precursor emissions and meteorological influence either on the inter-annual or the short-term time scale. Decreasing mean ozone concentrations at rural sites and increasing or stabilizing at urban sites were observed. At the same time downward trends for peak ozone concentrations were detected for all site types. The effect of hemispheric transport of ozone can be seen either in regions affected by synoptic patterns in the northern Atlantic or at sites located at remote high altitude locations. In addition, a reduction of the amplitude in the seasonal cycle of ozone was observed, and a shift in the occurrence of the seasonal maximum towards earlier time of the year. Finally, a reduced sensitivity of ozone to temperature was identified. It was concluded that long-term trends of mean and peak ozone concentrations are mostly controlled by precursors emissions changes, while seasonal cycle trends and changes in the sensitivity of ozone to temperature are driven by regional climatic conditions.

scholarly journals Long-term evolution is surprisingly predictable in lattice proteins

2013 ◽  
Vol 10 (82) ◽  
pp. 20130026 ◽  
Author(s):  
Michael E. Palmer ◽  
Arnav Moudgil ◽  
Marcus W. Feldman
Keyword(s):  
Natural Selection ◽  
Basins Of Attraction ◽  
Long Term Outcomes ◽  
Short Term ◽  
Term Selection ◽  
Term Evolution ◽  
Evolutionary Success ◽  
Lattice Proteins

It has long been debated whether natural selection acts primarily upon individual organisms, or whether it also commonly acts upon higher-level entities such as lineages. Two arguments against the effectiveness of long-term selection on lineages have been (i) that long-term evolutionary outcomes will not be sufficiently predictable to support a meaningful long-term fitness and (ii) that short-term selection on organisms will almost always overpower long-term selection. Here, we use a computational model of protein folding and binding called ‘lattice proteins’. We quantify the long-term evolutionary success of lineages with two metrics called the k -fitness and k -survivability. We show that long-term outcomes are surprisingly predictable in this model: only a small fraction of the possible outcomes are ever realized in multiple replicates. Furthermore, the long-term fitness of a lineage depends only partly on its short-term fitness; other factors are also important, including the ‘evolvability’ of a lineage—its capacity to produce adaptive variation. In a system with a distinct short-term and long-term fitness, evolution need not be ‘short-sighted’: lineages may be selected for their long-term properties, sometimes in opposition to short-term selection. Similar evolutionary basins of attraction have been observed in vivo , suggesting that natural biological lineages will also have a predictive long-term fitness.

scholarly journals Growth and business cycle in Argentina. A long-run approach, 1870–2015

2020 ◽  
Vol 28 (84) ◽  
pp. 197-220
Author(s):  
María Dolores Gadea ◽  
Isabel Sanz-Villarroya
Keyword(s):  
Long Term Evolution ◽  
Growth Potential ◽  
Short Term ◽  
Content Type ◽  
Developed Economies ◽  
Term Evolution ◽  
Long Run ◽  
High Volatility ◽  
The Moment

Purpose The purpose of this study is to focus deeply on the short term to explain the relative long-term evolution of the Argentinian economy in the long and the short term. Design/methodology/approach The study of the long-term evolution of the Argentine economy and identifying the moment in which it began to lose ground compared to other developed economies, such as Australia and Canada, constitutes the central axis of the historiography of this country. However, an additional problem presented by the Argentine economy is its high volatility. For this reason, the long term should be influenced by the short term, an issue that requires a more detailed study of the cyclical behavior and a deep analysis of the relationship between the long and the short term. Findings The results obtained point to a cyclical development that influences the long-term evolution and, therefore, explains Argentina’s convergence process with Australia and Canada. Frequent deep busts and short booms characterize the Argentine cycle, offsetting its long-term growth potential. Originality/value Although the long term has been profusely studied in Argentina, the short term has not been analyzed to the same extent, which is surprising given the extreme volatility of this economy (Prebisch, 1950). The studies performed on economic cycles have always been partial, disconnected from the long term and carried out without much technical rigor.

scholarly journals Empirical model for short-time prediction of COVID-19 spreading

2020 ◽  
Vol 16 (12) ◽  
pp. e1008431
Author(s):  
Martí Català ◽  
Sergio Alonso ◽  
Enrique Alvarez-Lacalle ◽  
Daniel López ◽  
Pere-Joan Cardona ◽  
...  
Keyword(s):  
Health Workers ◽  
Gompertz Model ◽  
Control Measures ◽  
Short Term ◽  
Spreading Velocity ◽  
Current State ◽  
Term Evolution ◽  
Public Health Workers ◽  
Short Time

The appearance and fast spreading of Covid-19 took the international community by surprise. Collaboration between researchers, public health workers, and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both the current state and short-term future trends can be carefully evaluated. Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries. Results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the short-term evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements.

Long-term Evolution, Short-term Evolution, and Population Genetic Theory

1998 ◽  
Vol 191 (4) ◽  
pp. 391-396 ◽  
Author(s):  
Ilan Eshel ◽  
Marcus W. Feldman ◽  
Aviv Bergman
Keyword(s):  
Population Genetic ◽  
Long Term Evolution ◽  
Short Term ◽  
Genetic Theory ◽  
Term Evolution

scholarly journals Simulations on the survivability of Tidal Dwarf Galaxies

2014 ◽  
Vol 10 (S309) ◽  
pp. 157-158
Author(s):  
Sylvia Ploeckinger ◽  
Simone Recchi ◽  
Gerhard Hensler ◽  
Pavel Kroupa
Keyword(s):  
Dark Matter ◽  
Numerical Simulations ◽  
Dwarf Galaxies ◽  
Time Dependent ◽  
Short Term ◽  
Stellar Feedback ◽  
Term Evolution ◽  
Matter Component ◽  
Simulation Time

AbstractWe present detailed numerical simulations of the evolution of Tidal Dwarf Galaxies (TDGs) after they kinematically decouple from the rest of the tidal arm to investigate their survivability. Both the short-term (500 Myr) response of TDGs to the stellar feedback of different underlying stellar populations as well as the long-term evolution that is dominated by a time dependent tidal field is examined. All simulated TDGs survive until the end of the simulation time of up to 3 Gyr, despite their lack of a stabilising dark matter component.

Modelling long-term and short-term evolution of karst in vicinity of tunnels

2020 ◽  
Vol 581 ◽  
pp. 124282 ◽  
Author(s):  
Georg Kaufmann ◽  
Douchko Romanov
Keyword(s):  
Short Term ◽  
Term Evolution
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