Sentences with phrase «is spatial correlation»

Ross McKitrick did prepare a response to the argument re spatial correlation.

Not exact matches

«On the basis of time and spatial correlations, we conclude the DFW sequence may be the result of fluid injection at the SWD [saltwater - disposal] well,» they wrote in a paper published in March 2010 in The Leading Edge.

«When we compared the spatial correlation using datasets that include only magnitude 3 - plus earthquakes, there was no change,» said Pollyea, adding that a larger reduction in wastewater injection volumes is needed to reduce the dangers of large magnitude earthquakes.

In particular, Brillouin optical correlation - domain reflectometry (BOCDR), which operates based on the correlation control of continuous lightwaves, is known to be an intrinsically one - end - access distributed sensing technique with high spatial resolution (< 1 cm).

In addition, the Honeycomb Maze enables researchers to study the correlation of spatial navigation performance with the activity of place cells in the hippocampus where there is a map of an animal's location in space.

Negative correlations between SVD burden score and cognitive scores were observed for global cognitive, memory, and visual spatial scores (all p < 0.05).

In addition, temporal [17] and spatial [16,17] correlations between increased levels of HIF - 1α, the regulatory subunit of HIF - 1, and increased VEGF expression have been shown, indicating that HIF - 1 may control the expression of VEGF in the retina under hypoxic conditions.

The chemical composition of these decay products in relation to the substrate material, the various conservation and restoration historic phases is discussed along with their spatial distribution on the monument's surfaces and also in correlation with environmental and climatic data.

Although there were modest negative correlations between HFB and alpha local activity within each network, spatial connectivity patterns showed similarities between these frequency ranges.

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 concluded that the adopted covariance model was compatible with the data, as the empirical semi-variogram fell within the 95 % tolerance intervals computed via Monte Carlo simulation and a spatial correlation test of residuals.

Fritz and her team are the first to confirm this correlation on such a large spatial and temporal scale — for North America as well as for Europe.

Functional connectivity is typically measured using one of three approaches: (1) regression analysis using a seed region of interest (Greicius et al., 2003; Fox et al., 2005), (2) full or partial correlation analysis of multiple regions of interest (Ryali et al., 2012), or (3) independent component analysis (ICA) of the entire imaging dataset to identify spatial maps with common temporal profiles (Beckmann and Smith, 2004; Cole et al., 2010).

We first demonstrate less variability of global Pearson correlations with respect to the two chosen networks using a sliding - window approach during WM task compared to rest; then we show that the macroscopic decrease in variations in correlations during a WM task is also well characterized by the combined effect of a reduced number of dominant CAPs, increased spatial consistency across CAPs, and increased fractional contributions of a few dominant CAPs.

The spatial smoothness of T2 * images was greater at 3 T by less than 1 mm, suggesting that the greater extent of activation at 3 T beyond these spatial scales was not due primarily to increased intrinsic spatial correlations at 3 T. Rather, the increase in percentage of voxels activated reflects increased sensitivity for detection of brain activation at higher field strength.

One index capturing this could be the correlation between the spatial patterns in OLR and the surface IR flux over time (figure below taken from Benestad (2016)-RRB-.

in my paper indicates that the d - o - f of all the fields is significantly less (and sometimes much, much less) than what you would get in the zero spatial correlation case.

If you download 1998 - 2009 cloud cover here, and sea surface temperatures here, you can see that, except for a cloud band from ~ 0 to 10 degrees N, cloudiness is generally less where SST is warmer, though there are lots of details and spatial variation that lessen the correlation.

There is lots of spatial correlation in the data.

My take on his preprint is that his conclusion that «zero spatial correlation can not be rejected» is astonishing.

The spatial patterns of our emission fluxes and observed methane — propane correlations indicate that fossil fuel extraction and refining are major contributors (45 ± 13 %) in the south - central United States.»

First of all, the observed changes in global mean temperatures are more easily calculated in terms of anomalies (since anomalies have much greater spatial correlation than absolute temperatures).

I speculated that this was due to him not appreciating the spatial auto - correlation structure of the variables and over-estimating the degrees of freedom.

Probably the correlation between the high - SST area and the total TC activity is positive, but it is a result of spatial aggregation of complicated phenomena.

What is lacking is mainly the correlation between the models and real world in local and regional weather but this is also ever improving as the model time and spatial resolution improves.

It is striking to what extent they resemble the spatial pattern seen in the AR4 ensemble free - running version rather than the initiallised forecast, though there are also some correlations there too (for instance, west of the Antarctic peninsula, related to the ozone - hole and GHG related increase in the Southern Annular Mode).

I am really impressed by the spatial correlation between the model run and the actual temperature anomalies.

Degrees of freedom is commonly used in statistics, but can also describe how much information you really need to describe something after stripping away redundant information (spatial correlation).

This spatial pattern is consistent with the air temperature — North Atlantic Oscillation (NAO) index correlation pattern, with maximum correlation in the near - Atlantic region, which decays toward the North Pacific.

Arthur Smith argues that the spatial correlation of ENSO is evidence against chaos, because chaotic systems can not (in Arthur's eyes) produce structures which correlate across large scales.

[T] he basis of the results are correlations over a very restricted set of locations (predominantly western Europe, Japan and the USA) which project strongly onto naturally occurring patterns of climate variability, or are with fields with significant amounts of spatial auto - correlation.

The climate community does not seem to exercise such care, and when their poor use of methods is pointed out they just ignore it and carry on (I could give scores of examples, from improper use of principal components, data mining, data snooping, spatial correlation, upside down data, single cause fallacy... and now uniform priors).

But I suppose we can leave that to a study of the proper way of calculating a error due to spatial coverage, that error will be a function (at least in the math I've seen) involving the spatial correlation which varies considerably.

Parker considered simple autocorrelation, but the this sort of data also exhibits homoscedasticity, as well as spatial and long - term - temporal correlations that likely need to be addressed, too.

They calculated the so - called shape asymmetries from the seismic data and found each coefficient was essentially zero at activity minimum and rose in precise spatial correlation with rising surface activity, as measured using Ca II K data from Big Bear Solar Observatory.

Linear statistics were used first: area - averaged and Australia - wide spatial correlations of STR intensity and position with precipitation in south - west eastern Australia reveal that STR intensity has a much stronger and more widespread relationship with precipitation in both seasons.

In the past couple of years at BEST, has there been any opportunity yet to look into clustering effects on your spatial model, i.e., correlation function.

The correlations are with the «mush», so any spatial influence should be handled in the averaging phase.

Temporal trends will exist in those temporal series, meaning their spatial correlation calculations are impacted by temporal trends.

The running text in MBH98 stated: «Figure 3 shows the spatial patterns of calibration β, and verification β and the squared correlation statistic r2, demonstrating highly significant reconstructive skill over widespread regions of the reconstructed spatial domain [emphasis added]» and later: «β [or RE] is a quite rigorous measure of the similarity between two variables... For comparison, correlation (r) and squared - correlation (r2) statistics are also determined.

I think there are technical issue with how BEST is computing the correlation that is exacerbating the spatial smearing problem in their kriging function:

The Lasso method would have been a lot more useful if spatial correlation were taken into account.

The approach can be refined for spatial autocorrelation by replacing WLS with «Generalized Least Squares» (GLS), aka Aitken's rule, which takes correlations into account.

Even apart from the cherry picking issue, it is likely that these error bands do not take the spatial correlations into account, which, with the much larger number of proxies, are more severe than in Loehle and McC.

However, developing an improved correlation model that incorporates additional spatial variations is a likely topic for future research.»

Such concerns, however, are tangential to the global mean temperature signature of oceanic natural variability, which is robust and independent of spatial correlations that might obscure the identification of the precise geographical source of such variability.

> clearly in California, the spatial correlation is poor, no?

You can see very close stations (in California for exemple) showing different trends... so clearly in California, the spatial correlation is poor, no?

And this is what I had in mind from previous discussions: a fairly large spatial correlation, like the 800-1200 km that can be found in the Hansen 1987 paper.

Question from a novice: how is this spacial noise — or poor spatial correlation — taken into account when estimating the uncertainty on the average temperature?