Sentence examples for regression weather from inspiring English sources

In case of site specific predictions it may be noted that the problem of downscaling a global low-resolution general circulation models to the regional high-resolution scale has been addressed in the past through methods like regression, weather pattern matching, limited area models and stochastic weather generators (Wilby and Wigley 1997).

Regression of weathering-rind thickness against height above modern drainageways of remnant fan surfaces in two areas suggests that fans are being incised more rapidly in one area than in another.

The second and third points are useful for implementing ordinal regression concerning numerical weather prediction.

To check for the presence of a linear trend, we run a simple linear regression of the weather data against hourly time.

Our model consists of components that represent trends, seasons at different levels (yearly, weekly, daily, special days and holidays), short-term dynamics and weather regression effects, including nonlinear functions for heating effects.

Secondly, the regression analysis of weather and the number of interruptions based on (8) has been done, results are in ([45.2%,;50.1% ]) for different MAs, as the first analysis shows the maximum value is for the fifth MA and the minimum value happens at the third MA.

In the final conditional logistic regression models, the weather variables were treated as confounders.

This study proposes a new monthly whole-building water use regression model for weather-normalized water performance evaluation: a combination three-parameter multi-variable regression (3-P MVR) cooling model using outdoor temperature in a change-point model and precipitation amount/occurrence as an additional independent variable.

Using a bidirectional control sampling approach, the results from a conditional logistic regression model controlling for weather conditions showed that the nonaccidental mortality associated with a 50-ppb increment over a 3-day moving average of SO(2) concentrations, including the concurrent day and preceding 2 days, was 1.023 [95% confidence interval (CI), 1.016-1.084].

We estimated changes in the relative risk cumulative over 0 2 lagged days of wildfire PM2.5 exposure using a quasi-Poisson regression model adjusted for weather, weekends, and poverty.

Significant variables from the univariate analysis were then combined in a stepwise backwards regression to identify the weather variables that provided the best model fit according to the Akaike Information Criteria AICC) [ 14].