149 research outputs found
Has spring snowpack declined in the Washington Cascades?
Our best estimates of 1 April snow water equivalent (SWE) in the Cascade Mountains of Washington State indicate a substantial (roughly 15–35%) decline from mid-century to 2006, with larger declines at low elevations and smaller declines or increases at high elevations. This range of values includes estimates from observations and hydrologic modeling, reflects a range of starting points between about 1930 and 1970 and also reflects uncertainties about sampling. The most important sampling issue springs from the fact that half the 1 April SWE in the Cascades is found below about 1240 m, altitudes at which sampling was poor before 1945. Separating the influences of temperature and precipitation on 1 April SWE in several ways, it is clear that long-term trends are dominated by trends in temperature, whereas variability in precipitation adds "noise" to the time series. Consideration of spatial and temporal patterns of change rules out natural variations like the Pacific Decadal Oscillation as the sole cause of the decline. Regional warming has clearly played a role, but it is not yet possible to quantify how much of that regional warming is related to greenhouse gas emissions
Digital quantum simulation of spin models with circuit quantum electrodynamics
Systems of interacting quantum spins show a rich spectrum of quantum phases
and display interesting many-body dynamics. Computing characteristics of even
small systems on conventional computers poses significant challenges. A quantum
simulator has the potential to outperform standard computers in calculating the
evolution of complex quantum systems. Here, we perform a digital quantum
simulation of the paradigmatic Heisenberg and Ising interacting spin models
using a two transmon-qubit circuit quantum electrodynamics setup. We make use
of the exchange interaction naturally present in the simulator to construct a
digital decomposition of the model-specific evolution and extract its full
dynamics. This approach is universal and efficient, employing only resources
which are polynomial in the number of spins and indicates a path towards the
controlled simulation of general spin dynamics in superconducting qubit
platforms.Comment: 12 pages, 9 figure
Bursts of vertex activation and epidemics in evolving networks
The dynamic nature of contact patterns creates diverse temporal structures. In particular, empirical studies have shown that contact patterns follow heterogeneous inter-event time intervals, meaning that periods of high activity are followed by long periods of inactivity. To investigate the impact of these heterogeneities in the spread of infection from a theoretical perspective, we propose a stochastic model to generate temporal networks where vertices make instantaneous contacts following heterogeneous inter-event intervals, and may leave and enter the system. We study how these properties affect the prevalence of an infection and estimate , the number of secondary infections of an infectious individual in a completely susceptible population, by modeling simulated infections (SI and SIR) that co-evolve with the network structure. We find that heterogeneous contact patterns cause earlier and larger epidemics in the SIR model in comparison to homogeneous scenarios for a vast range of parameter values, while smaller epidemics may happen in some combinations of parameters. In the case of SI and heterogeneous patterns, the epidemics develop faster in the earlier stages followed by a slowdown in the asymptotic limit. For increasing vertex turnover rates, heterogeneous patterns generally cause higher prevalence in comparison to homogeneous scenarios with the same average inter-event interval. We find that is generally higher for heterogeneous patterns, except for sufficiently large infection duration and transmission probability
Probing empirical contact networks by simulation of spreading dynamics
Disease, opinions, ideas, gossip, etc. all spread on social networks. How
these networks are connected (the network structure) influences the dynamics of
the spreading processes. By investigating these relationships one gains
understanding both of the spreading itself and the structure and function of
the contact network. In this chapter, we will summarize the recent literature
using simulation of spreading processes on top of empirical contact data. We
will mostly focus on disease simulations on temporal proximity networks --
networks recording who is close to whom, at what time -- but also cover other
types of networks and spreading processes. We analyze 29 empirical networks to
illustrate the methods
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees
The spread of infectious diseases crucially depends on the pattern of
contacts among individuals. Knowledge of these patterns is thus essential to
inform models and computational efforts. Few empirical studies are however
available that provide estimates of the number and duration of contacts among
social groups. Moreover, their space and time resolution are limited, so that
data is not explicit at the person-to-person level, and the dynamical aspect of
the contacts is disregarded. Here, we want to assess the role of data-driven
dynamic contact patterns among individuals, and in particular of their temporal
aspects, in shaping the spread of a simulated epidemic in the population.
We consider high resolution data of face-to-face interactions between the
attendees of a conference, obtained from the deployment of an infrastructure
based on Radio Frequency Identification (RFID) devices that assess mutual
face-to-face proximity. The spread of epidemics along these interactions is
simulated through an SEIR model, using both the dynamical network of contacts
defined by the collected data, and two aggregated versions of such network, in
order to assess the role of the data temporal aspects.
We show that, on the timescales considered, an aggregated network taking into
account the daily duration of contacts is a good approximation to the full
resolution network, whereas a homogeneous representation which retains only the
topology of the contact network fails in reproducing the size of the epidemic.
These results have important implications in understanding the level of
detail needed to correctly inform computational models for the study and
management of real epidemics
Dynamically and Statistically Downscaled Seasonal Simulations of Maximum Surface Air Temperature Over the Southeastern United States
Coarsely resolved surface air temperature (2 m height) seasonal integrations from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) (~1.8º lon.-lat. (T63)) for the period of 1994 to 2002 (March through September each year) are downscaled to a fine spatial scale of ~20 km. Dynamical and statistical downscaling methods are applied for the southeastern United States region, covering Florida, Georgia, and Alabama. Dynamical downscaling is conducted by running the FSU/COAPS Nested Regional Spectral Model (NRSM), which is nested into the domain of the FSU/COAPS GSM. We additionally present a new statistical downscaling method. The rationale for the statistical approach is that clearer separation of prominent climate signals (e.g., seasonal cycle, intraseasonal, or interannual oscillations) in observation and GSM, respectively, over the training period can facilitate the identification of the statistical relationship in climate variability between two data sets. Cyclostationary Empirical Orthogonal Function (CSEOF) analysis and multiple regressions are trained with those data sets to extract their statistical relationship, which eventually leads to better prediction of regional climate from the large-scale simulations. Downscaled temperatures are compared with the FSU/COAPS GSM fields and observations. Downscaled seasonal anomalies exhibit strong agreement with observations and a reduction in bias relative to the direct GSM simulations. Interannual temperature change is also reasonably simulated at local grid points. A series of evaluations including mean absolute errors, anomaly correlations, frequency of extreme events, and categorical predictability reveal that both downscaling techniques can be reliably used for numerous seasonal climate applications
Experimental Quantum Hamiltonian Learning
Efficiently characterising quantum systems, verifying operations of quantum
devices and validating underpinning physical models, are central challenges for
the development of quantum technologies and for our continued understanding of
foundational physics. Machine-learning enhanced by quantum simulators has been
proposed as a route to improve the computational cost of performing these
studies. Here we interface two different quantum systems through a classical
channel - a silicon-photonics quantum simulator and an electron spin in a
diamond nitrogen-vacancy centre - and use the former to learn the latter's
Hamiltonian via Bayesian inference. We learn the salient Hamiltonian parameter
with an uncertainty of approximately . Furthermore, an observed
saturation in the learning algorithm suggests deficiencies in the underlying
Hamiltonian model, which we exploit to further improve the model itself. We go
on to implement an interactive version of the protocol and experimentally show
its ability to characterise the operation of the quantum photonic device. This
work demonstrates powerful new quantum-enhanced techniques for investigating
foundational physical models and characterising quantum technologies
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