21,762 research outputs found
The NWRA Classification Infrastructure: Description and Extension to the Discriminant Analysis Flare Forecasting System (DAFFS)
A classification infrastructure built upon Discriminant Analysis has been
developed at NorthWest Research Associates for examining the statistical
differences between samples of two known populations. Originating to examine
the physical differences between flare-quiet and flare-imminent solar active
regions, we describe herein some details of the infrastructure including:
parametrization of large datasets, schemes for handling "null" and "bad" data
in multi-parameter analysis, application of non-parametric multi-dimensional
Discriminant Analysis, an extension through Bayes' theorem to probabilistic
classification, and methods invoked for evaluating classifier success. The
classifier infrastructure is applicable to a wide range of scientific questions
in solar physics. We demonstrate its application to the question of
distinguishing flare-imminent from flare-quiet solar active regions, updating
results from the original publications that were based on different data and
much smaller sample sizes. Finally, as a demonstration of "Research to
Operations" efforts in the space-weather forecasting context, we present the
Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time
operationally-running solar flare forecasting tool that was developed from the
research-directed infrastructure.Comment: J. Space Weather Space Climate: Accepted / in press; access
supplementary materials through journal; some figures are less than full
resolution for arXi
Turbulence in the Solar Atmosphere: Manifestations and Diagnostics via Solar Image Processing
Intermittent magnetohydrodynamical turbulence is most likely at work in the
magnetized solar atmosphere. As a result, an array of scaling and multi-scaling
image-processing techniques can be used to measure the expected
self-organization of solar magnetic fields. While these techniques advance our
understanding of the physical system at work, it is unclear whether they can be
used to predict solar eruptions, thus obtaining a practical significance for
space weather. We address part of this problem by focusing on solar active
regions and by investigating the usefulness of scaling and multi-scaling
image-processing techniques in solar flare prediction. Since solar flares
exhibit spatial and temporal intermittency, we suggest that they are the
products of instabilities subject to a critical threshold in a turbulent
magnetic configuration. The identification of this threshold in scaling and
multi-scaling spectra would then contribute meaningfully to the prediction of
solar flares. We find that the fractal dimension of solar magnetic fields and
their multi-fractal spectrum of generalized correlation dimensions do not have
significant predictive ability. The respective multi-fractal structure
functions and their inertial-range scaling exponents, however, probably provide
some statistical distinguishing features between flaring and non-flaring active
regions. More importantly, the temporal evolution of the above scaling
exponents in flaring active regions probably shows a distinct behavior starting
a few hours prior to a flare and therefore this temporal behavior may be
practically useful in flare prediction. The results of this study need to be
validated by more comprehensive works over a large number of solar active
regions.Comment: 26 pages, 7 figure
What is the relationship between photospheric flow fields and solar flares?
We estimated photospheric velocities by separately applying the Fourier Local
Correlation Tracking (FLCT) and Differential Affine Velocity Estimator (DAVE)
methods to 2708 co-registered pairs of SOHO/MDI magnetograms, with nominal
96-minute cadence and ~2" pixels, from 46 active regions (ARs) from 1996-1998
over the time interval t45 when each AR was within 45^o of disk center. For
each magnetogram pair, we computed the average estimated radial magnetic field,
B; and each tracking method produced an independently estimated flow field, u.
We then quantitatively characterized these magnetic and flow fields by
computing several extensive and intensive properties of each; extensive
properties scale with AR size, while intensive properties do not depend
directly on AR size. Intensive flow properties included moments of speeds,
horizontal divergences, and radial curls; extensive flow properties included
sums of these properties over each AR, and a crude proxy for the ideal Poynting
flux, the total |u| B^2. Several magnetic quantities were also computed,
including: total unsigned flux; a measure of the amount of unsigned flux near
strong-field polarity inversion lines, R; and the total B^2. Next, using
correlation and discriminant analysis, we investigated the associations between
these properties and flares from the GOES flare catalog, when averaged over
both t45 and shorter time windows, of 6 and 24 hours. We found R and total |u|
B^2 to be most strongly associated with flares; no intensive flow properties
were strongly associated with flares.Comment: 57 pages, 13 figures; revised content; added URL to manuscript with
higher-quality image
Dominance of grain size impacts on seasonal snow albedo at deforested sites in New Hampshire
Snow cover serves as a major control on the surface energy budget in temperate regions due to its high reflectivity compared to underlying surfaces. Winter in the northeastern United States has changed over the last several decades, resulting in shallower snowpacks, fewer days of snow cover, and increasing precipitation falling as rain in the winter. As these climatic changes occur, it is imperative that we understand current controls on the evolution of seasonal snow albedo in the region. Over three winter seasons between 2013 and 2015, snow characterization measurements were made at three open sites across New Hampshire. These near-daily measurements include spectral albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density, black carbon content, local meteorological parameters, and analysis of storm trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory model. Using analysis of variance, we determine that land-based winter storms result in marginally higher albedo than coastal storms or storms from the Atlantic Ocean. Through multiple regression analysis, we determine that snow grain size is significantly more important in albedo reduction than black carbon content or snow density. And finally, we present a parameterization of albedo based on days since snowfall and temperature that accounts for 52% of variance in albedo over all three sites and years. Our improved understanding of current controls on snow albedo in the region will allow for better assessment of potential response of seasonal snow albedo and snow cover to changing climate
Dominance of grain size impacts on seasonal snow albedo at deforested sites in New Hampshire
Snow cover serves as a major control on the surface energy budget in temperate regions due to its high reflectivity compared to underlying surfaces. Winter in the northeastern United States has changed over the last several decades, resulting in shallower snowpacks, fewer days of snow cover, and increasing precipitation falling as rain in the winter. As these climatic changes occur, it is imperative that we understand current controls on the evolution of seasonal snow albedo in the region. Over three winter seasons between 2013 and 2015, snow characterization measurements were made at three open sites across New Hampshire. These near-daily measurements include spectral albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density, black carbon content, local meteorological parameters, and analysis of storm trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory model. Using analysis of variance, we determine that land-based winter storms result in marginally higher albedo than coastal storms or storms from the Atlantic Ocean. Through multiple regression analysis, we determine that snow grain size is significantly more important in albedo reduction than black carbon content or snow density. And finally, we present a parameterization of albedo based on days since snowfall and temperature that accounts for 52% of variance in albedo over all three sites and years. Our improved understanding of current controls on snow albedo in the region will allow for better assessment of potential response of seasonal snow albedo and snow cover to changing climate
Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network
The network of patents connected by citations is an evolving graph, which
provides a representation of the innovation process. A patent citing another
implies that the cited patent reflects a piece of previously existing knowledge
that the citing patent builds upon. A methodology presented here (i) identifies
actual clusters of patents: i.e. technological branches, and (ii) gives
predictions about the temporal changes of the structure of the clusters. A
predictor, called the {citation vector}, is defined for characterizing
technological development to show how a patent cited by other patents belongs
to various industrial fields. The clustering technique adopted is able to
detect the new emerging recombinations, and predicts emerging new technology
clusters. The predictive ability of our new method is illustrated on the
example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of
patents is determined based on citation data up to 1991, which shows
significant overlap of the class 442 formed at the beginning of 1997. These new
tools of predictive analytics could support policy decision making processes in
science and technology, and help formulate recommendations for action
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