Sentences with word «predictand»

Each of these approaches has relative strengths and weaknesses in representing the range of temporal variance of the local climate predictand.

Appendix 10.4 provides a non-exhaustive list of past studies indicating predictands, geographical domain, and technique category.

Sensitivity of results to predictors or predictands used and to the number of PLS components used.

BC17 consider all four IPCC RCP scenarios and focus on mid-century and end - century warming; in each case there is a single predictand, ΔT.

The right hand panels involve different predictands, with all predictors used simultaneously.

We obtained the predictors from the CGCM 3.1 20C3M (1971 — 2000) and A2 (2041 — 2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands.

The basic idea of this procedure is to identify regions with stable teleconnections between the predictors and the predictand.

Predictor and predictand can be the same variables on different spatial scales (e.g., Bürger, 1997; Wilks, 1999b; Widmann and Bretherton, 2000), but more commonly are different.

In empirical downscaling the cross-scale relationship is expressed as a stochastic and / or deterministic function between a set of large - scale atmospheric variables (predictors) and local / regional climate variables (predictands).

Time is the time - scale of the predictor and predictand: H (hourly), D (daily), M (monthly), S (seasonal), and A (annual).

The whole goal of emergent - constraint hunting is to find predictors which we have reason to expect are tightly - related to our predictand.

Perhaps we could say that probabilistic statements about climate sensitivity ignore uncertainty regarding the true relationship between predictor and predictand.

It also seems unfair to say that the model - weighting approach is better because it doesn't rely on the existence of a linear relationship when you * chose * the variable to compare against observations on the basis of that variable providing a good linear fit to your predictand.

The model, which uses Canonical Correlation Analysis to describe linear relationships between predictors and predictands, is used in both Canada and the United States for seasonal predictions of temperature and precipitation.

For any given level of correlation with the predictand, a high - variance predictor variable will have a higher covariance with it than a low - variance one.

The 37 latitude, 72 longitude grid provides 2,664 variables, still an extraordinarily large number of predictors when there are only 36 models, each providing one instance of the predictand, to fit the statistical model.

In either case, the prediction error reduces to zero when the maximum number of PLS components is used (one less than the number of models), since then there are sufficient degrees of freedom available to exactly fit each CMIP5 model's predictand.

The left hand panels all involve RCP8.5 2090 ΔT as the predictand, but with the nine different predictors used separately.

b, As in a but using all nine of the predictor fields simultaneously while switching the predictand that is targeted.

a, Prediction ratios for the nine predictor fields, each individually targeting the ΔT 2090 - RCP 8.5 predictand.

I am not very experienced in the use of PLS regression, but my understanding is that it should create components that each in turn consist of an optimal mixture — having regard to maximizing retained cross-covariance between the predictors and the predictand — of all the available predictors that is orthogonal to that in earlier components.

PLS regression is designed to compress as much as possible of the relevant information in the predictors into a small number of orthogonal components, ranked in order of (decreasing) relevance to predicting the predictand (s), here ΔT.

In view of the large margin of superiority in both cases it seems highly probable that use of the OLR seasonal cycle produces more skilful predictions for all predictand cases.

And in RegEM, there is no distinction between predictor and predictand — to this extent, some of the descriptions from Gavin and Steig aren't always as clear as they might be.

The basic idea of this procedure is to identify regions with stable teleconnections between the predictors and the predictand.

The average RASST values within these partition elements are used as predictors in a multiple linear regression, with the global mean temperature as the predictand.