Sentences with phrase «used spatial models»

Researchers from the University of Vermont and Oregon State University used spatial models and statistical analyses to map 81 fires as well as insect outbreaks over a 25 - year period in Oregon and Washington state.

To conduct their study, the researchers used a spatial model of marsh morphodynamics into which they incorporated recently published observations from field experiments on marsh vegetation response to varying levels of atmospheric carbon dioxide.

Not exact matches

Using a spatial economic model of the US dairy sector, the results showed that investment in two dairy plants in Pennsylvania would bring the state millions of dollars in additional revenue and slash transportation costs.

Dr. Michael M. Yartsev, Research Associate and C.V. Starr Fellow at the Princeton Neuroscience Institute at Princeton University receives the US$ 25,000 research prize for his work using the bat as an unusual animal model to study the underlying neural mechanisms of spatial memory and navigation in the mammalian brain.

Using version 5.1 of the Community Atmospheric Model, developed by the Department of Energy (DOE) and the National Science Foundation (NSF) for use by the scientific community, Wehner and his co-authors conducted an analysis for the period 1979 to 2005 at three spatial resolutions: 25 km, 100 km, and 200 km.

Using published data from the circumpolar arctic, their own new field observations of Siberian permafrost and thermokarsts, radiocarbon dating, atmospheric modeling, and spatial analyses, the research team studied how thawing permafrost is affecting climate change and greenhouse gas emissions.

The different types of motion of the plasmons were characterized using a microscopy technique called electron energy - loss spectroscopy (EELS), whose very fine spatial and spectral resolution enabled the researchers to propose a new theoretical model of plasmon behavior.

Emanuel and his colleagues had previously devised a technique to simulate hurricane development in a changing climate, using a specialized computational model they developed that simulates hurricanes at high spatial resolutions.

To further test the generalizability of this model, Parise and Ernst ran additional computer simulations, where they used the Multisensory Correlation Detector model to replicate several previous findings on the temporal and the spatial aspects of multisensory perception.

Professor O'Higgins said: «We used modelling software to shave back Kabwe's huge brow ridge and found that the heavy brow offered no spatial advantage as it could be greatly reduced without causing a problem.

Using two complementary approaches to reduce the deposits of amyloid - beta in the brain rather than either approach alone improved spatial navigation and memory in a mouse model of Alzheimer's disease.

Disease prevention versus data privacy: Using landcover maps to inform spatial epidemic models.

Martinez - Trujillo, a member of Western's renowned Brain and Mind Institute, notes that most spatial memory experiments include animal models being tested in actual, real - world mazes while humans are assessed virtually, using computer screens, more often than not in a two - dimensional setting.

ASTER data is used to create detailed maps of land surface temperature, reflectance, and elevation.ASTER captures high spatial resolution data in 14 bands, from the visible to the thermal infrared wavelengths, and provides stereo viewing capability for digital elevation model creation.

Longitudinal mixed models were also used to estimate the effect of vaccine dose on mean log - transformed antibody levels over time, using a spatial exponential covariance structure to model the correlation between measurements from the same individual while taking into account the number of study days between measurements.

We checked the validity of the assumed covariance model for spatial correlation using the Monte Carlo algorithm and empirical semi-variogram as described in Supplementary File 1.

We carried out validation of the model using a variogram - based procedure, which tested the compatibility of the adopted spatial structure with the data.

We used general linear models to estimate annual nesting at two spatial scales relevant to conservation management [31]- local and regional nesting surveys.

The delta method interpolates the General Circulation Model generally used in climate modelling at scales of 100 to 200 km using a thin plate spline spatial interpolation method to achieve the 30 arc seconds resolution [52].

Lauren uses poison frogs as a model for understanding how variation in predation and spatial structure of the environment has driven the evolution of chemical defences and parental behaviors.

For children who learn through the spatial intelligence, their classroom work can be enhanced through the use of manipulatives and hands - on work, such as models and dissections.

Best known for the slashed and cut canvases — and related spatial environments — of the Concetti spaziali that he created primarily in the 1950s and 60s, Argentine — Italian artist Lucio Fontana (1899 — 1968) trained as a sculptor at the Academy of Fine Arts of Brera and used ceramics and clay modeling to explore larger problems in sculpture and painting.

The shape of the landscape (the details of mountains, coastline etc.) used in the models reflect the spatial resolution, hence the model will not have sufficient detail to describe local climate variation associated with local geographical features (e.g. mountains, valleys, lakes, etc.).

To test that I varied the data sources, the time periods used, the importance of spatial auto - correlation on the effective numbers of degree of freedom, and most importantly, I looked at how these methodologies stacked up in numerical laboratories (GCM model runs) where I knew the answer already.

When this is done for predicting elections, say, something called «stratification» is used, where observations are qualified by (in this case) spatial extent, time of day, and other auxiliary variables and the response state of atmosphere considered as conditioned on these, and the model evaluated comparably, where it can be.

But models based on physical principles also reproduce the response to seasonal and spatial changes in radiative forcing fairly well, which is one of the many lines of evidence that supports their use in their prediction of the response to anthropogenic forcing.

He claims that this can be corrected for, but he still isn't using the proper null — in M&N they show the results from the ensemble means (of the GISS model and the full AR4 model set), but seem to be completely ignorant of the fact that ensemble mean results remove the spatial variations associated with internal variability which should be the exact thing you would use!

When GCMs are used to model atmospheric conditions and spatial grid size is reduced is there a scale at which chaotic conditions prevail and make modeling difficult in the same way that weather is harder to model than climate?

To get a sense of the mix of whaling - era data, tracking and modeling used to estimate past blue whale abundance, read this PloS ONE paper by an overlapping research team from last year: «Estimating Historical Eastern North Pacific Blue Whale Catches Using Spatial Calling Patterns.»

For example, many mechanistic models used to simulate the ecological effects of climate change operate at spatial resolutions varying from a single plant to a few hectares.

Unfortunately, the figure also confirms that the spatial resolution of theoutput from the GCMs used in the Mediterranean study is too coarse for constructing detailed regional scenarios.To develop more detailed regional scenarios, modelers can combine the GCM results with output from statistical models.3 This is done by constructing a statistical model to explain the observed temperature or precipitation at a meteorological station in terms of a range of regionally - averaged climate variables.

For the first time, researchers have been able to combine different climate models using spatial statistics — to project future seasonal temperature changes in regions across North America.

Islands smaller than the spatial resolution used in global climate models (GCMs)-- including French Polynesia, the Marshall Islands, and the Lesser Antilles — are difficult to assess because GCMs can only provide estimates of precipitation there, not potential evapotranspiration.

In this case, the land use was first downscaled to the 0.5 ° harmonization grid, following the algorithms of the global land - use model (GLM)(Hurtt et al. 2006), preserving GCAM regional land use area totals and generating smooth spatial patterns in the transition from historical to future states.

Sea surface temperature (SST) measured from Earth Observation Satellites in considerable spatial detail and at high frequency, is increasingly required for use in the context of operational monitoring and forecasting of the ocean, for assimilation into coupled ocean - atmosphere model systems and for applications in short - term numerical weather prediction and longer term climate change detection.

To assess these implications, we translate global into local SLR projections using a model of spatial variation in sea - level contributions caused by isostatic deformation and changes in gravity as the Greenland and Antarctic ice sheets lose mass (36 ⇓ — 38), represented as two global 0.5 ° matrices of scalar adjustment factors to the ice sheets» respective median global contributions to SLR and (squared) to their variances.

«Once models can adequately predict past climates and their spatial patterns, then we have confidence that they work and so can be used to predict the future.»

While regional climate downscaling yields higher spatial resolution, the downscaling is strongly dependent on the lateral boundary conditions and the methods used to constrain the regional climate model variables to the coarser spatial scale information from the parent global models.

Also the spatial structure of changes in precipitation linked to altered surface temperature by convection can be improved by using higher resolution model experiments, although the relative gain here is generally small (Di Luca et al, 2012).

Thus, even with the higher resolution analyses of terrain and land use in the regional domain, the errors and uncertainty from the larger model still persist, rendering the added simulated spatial details inaccurate.

Dynamic and statistical downscaling is widely used to refine predictions from global climate models to smaller spatial scales.

Wang, 2011: Detecting the ITCZ in instantaneous satellite data using spatial - temporal statistical modeling: ITCZ climatology in the east Pacific.

Using an ensemble of four high resolution (~ 25 km) regional climate models, this study analyses the future (2021 - 2050) spatial distribution of seasonal temperature and precipitation extremes in the Ganges river basin based on the SRES A1B emissions scenario.

An optimal transport and land use scenario was developed through modelling predicted demand, thus creating a future spatial design that promotes urban efficiency and sustainability for residents and the city government.

Problems also arise in the polar regions for models that use a lat - lon spatial grid, requiring noise filters to eliminate spurious oscillations.

Moreover, the detailed three - dimensional spatial structure and the temporal evolution of the many forcings of the climate system that are used to «drive» the models are poorly known.

She has been working on analysing climate change impacts on societal sectors for several years, focusing on the analysis of human - environmental systems, using conceptual as well as quantitative modelling approaches and spatial analysis methods.

«Both of these are established methodologies for doing spatial prediction otherwise known as interpolation» — phew, good thing you're not using «computer models» to fill in the data.

Climate projections were statistically downscaled and used to drive a macro-scale hydrology model at high spatial resolution.

Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB).