Sentences with phrase «correlation coefficient»

For maritime total cloud cover, SSN has a strong − 0.125 correlation coefficient.

So this wouldn't fix the problem with the low - freqency portion («the trend») dominating the estimate of the correlation coefficient, when what you really want is just the the high - frequency portion unadorned by the trend from another region.

«Synchronization as measured by the root - mean - square correlation coefficient between all pairs of modes over a 7 - year running window.

As with low level and total level cloud cover, no variable achieves a valid correlation coefficient over the entire domain for high cloud cover.

Low ocean cloud cover fails to produce a correlation coefficient larger than the rcritical value between other ocean cloud altitudes.

no, if you look at the formula for the correlation the first thing that happens is that the mean value is subtracted from the signal, hence the correlation coefficient is completely, mathematically insensitive to the mean value.

The AMO additionally exhibits a negative correlation coefficient with the NAO and the PDO at − 0.247, and − 0.221, respectively.

Marler, The net warming from 1880 to now is 100 % due to man - made forcing, following the growth of aCO2 to well above a 0.99 correlation coefficient.

One obtains a modest correlation coefficient of r =.64 (r ^ 2 =.4).

As I have pointed out, the correlation coefficient is insenitive to mu, so it only tells you about G (T).

However, the ENSO total cloud cover correlations more accurately reflect its mid-level and high - level cloud cover patterns for land, with a correlation coefficient of 0.135.

The corresponding 1st principal component is highly correlated with the principal component of total water storage (correlation coefficient is 0.70 at a 5 year lag).

What's the correlation coefficient between the two series, undifferenced of course.

Correlation coefficient between two timeseries in each panel is shown by upper - right corner

Correlation coefficient of 0.20 corresponds to the statistical significance at the 95 % level by using a two - side Student \ (t \) test with 100 samples

The predictability horizon corresponds to the time interval when the anomaly correlation coefficient exceeds the 99 % significance level using 37 degrees of freedom (correlation coefficient is 0.37)

For that reason, the comparisons of observed and modelled series are done not only in terms of the coefficient of efficiency, which is affected by the presence of bias, but also in terms of the correlation coefficient, which by definition removes the effect of bias.

Statistical comparisons are not provided: readers do not even get a correlation coefficient, let alone a significance test.

By removing those cases the correlation coefficient improves from 0.69 to 0.87 for the PM2.

In fact, their correlation coefficient is in excess of 0.98 over their common run.

Let be the empirically derived (Kendall) correlation coefficient between the two series, that 0.98 value mentioned above.

As estimated through various methods, the lag - one correlation coefficient used to generate the random null proxy sets is usually set between 0.2 and 0.4.

Between days − 5 to +3 (after the first appear - ance of significant cloud changes) the observed correlation coefficient between SLAT and cloud cover was found to be R = − 0.91.

The correlation coefficient is only 0.05, so sunspot number «explains» about 5 % of the variance in season maximum snow depth.

Nonetheless, they find that the correlation coefficient between solar irradiance and Neptune's brightness is near 0.90 (1.00 is perfect).

The EPP IE interannual variability correlated very well, however (correlation coefficient of 0.85 — 0.90), with auroral and medium energy hemispheric power, and with column NO measured by the SNOE satellite instrument from 97 to 150 km.

I assume that what is meant by the «R2» statistic is the squared Pearson dot - moment correlation, or r2 (i.e., the square of the simple linear correlation coefficient between two time series) over the 1856 - 1901 «verification» interval for our reconstruction.

the model does not simulate any dependence of Northern England precipitation on the state of El Niño, with a correlation coefficient of r = 0.01 and no visual indications that extreme events behave differently than the mean.

Thanks for filling in the details as below: The EPP IE interannual variability correlated very well, however (correlation coefficient of 0.85 — 0.90), with auroral and medium energy hemispheric power,

No CI, correlation coefficient, discussion of uncertainty nor of alternate hypotheses, reference to peer - reviewed studies to affirm its interpretations.

The correlation coefficient, R, between the two parameters is approximately 0.88 ± 0.03.

The fit has a correlation coefficient of 0.997; but in reality sucks as my model assumes that CO2 efflux is first order with respect to CO2, where in fact it is probably closer to second order.

Measurements collected with the X-SACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, differential Reflectivity (Zdr), correlation coefficient (rho - hv), and specific differential phase (phi - dp).

Calculate the correlation coefficient.

The result is correlation coefficient 0.95 with measured anomalies since before 1900 and credible trend back to the LIA.

the evolution of cosmic rays and the amplitude of the semi-annual day length are correlated (correlation coefficient the order of 0.7), and are in phase.

If to justify your values you need to use a fourth order polynomial, as is shown on the trend you present, you have to show that there is a significant improvement in the correlation coefficient between the trend and the data by using three additional fitting parameters.

(a) Correlation coefficient between [E] n and [Ta], [Td] of n − 3 to n + 2 SCs (total 12 parameters) is shown for ws = 9, 11, and 13.

A simple linear fit to the UAH temperature series gives a correlation coefficient of 0.53, while your fourth order polynomial gives 0.59.

It is evident from Figure 5b that these two parameters correlate well, yielding a high correlation coefficient of r = 0.95.

(b) Correlation coefficient between [E] n and test parameter [Ttest] n is shown with corresponding number of variables used for estimating the [Ttest] n.

The combination C1 shows a correlation coefficient almost identical to that shown by the other five test parameters, but with the least number of variables.

In Figure 4b, for the six correlated combinations, the number of variables Ci (i = 1 — 6) is plotted as a function of the correlation coefficient (ri).

A correlation coefficient between entropy of nth SC and [Ta], [Td] of n − 3 to n + 2 SCs (total 12 parameters) is shown in Figure 4a for ws = 9, 11, and 13.

However, we realize that the high value of the formal correlation coefficient (≥ 0.9) is not sufficient to justify the uniqueness of this particular combination.

The obtained correlation coefficient has a confidence limit of more than 99 %, and thus, these parameters can be effectively used in the prediction of the descent time of forthcoming SCs.

It is clear that there is a strong correlation (correlation coefficient about -0.7) between ECS on the vertical axis and the natural fluctuations on the horizontal axis — an example of an empirical fluctuation - dissipation relation in the models.

He does not quantify the correlation between the two, but the squared correlation coefficient (r2) for the two time series is 0.36.

The highest accumulation season correlation coefficient is total accumulation season precipitation, ranging from 0.35 - 0.59.

11) suggests that the correlation coefficient falls to around 0.5 at a distance of 300 km, and to 0.2 at 800 km.»