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Data Center (NCDC) or the Goddard Institute for Space Studies (GISS)]. Undoubtedly, short-term natural climate fluctuations play a role: The “super” El Niño in 1998 made that year either the warmest or close to the warmest on record, and the La Niña in 2008 contributed to that year being not as warm. It is understood that these, and possibly other, natural fluctuations should be filtered out to reveal the underlying anthropogenic warming. Multiple linear regression (MLR) analysis is often employed
Data Center (NCDC) or the Goddard Institute for Space Studies (GISS)]. Undoubtedly, short-term natural climate fluctuations play a role: The “super” El Niño in 1998 made that year either the warmest or close to the warmest on record, and the La Niña in 2008 contributed to that year being not as warm. It is understood that these, and possibly other, natural fluctuations should be filtered out to reveal the underlying anthropogenic warming. Multiple linear regression (MLR) analysis is often employed
cyclones on the quantity of labor employed and employee remuneration, this paper puts forward several research hypotheses after analyzing the mechanism of tropical cyclones’ impact on employment. Then, meta-regression analysis is adopted to study the sample data from four aspects, including industry dimension, time dimension, income dimension, and tropical cyclone intensity. The conclusions provide some suggestions for the medium- and long-term management of tropical cyclones. The differences between
cyclones on the quantity of labor employed and employee remuneration, this paper puts forward several research hypotheses after analyzing the mechanism of tropical cyclones’ impact on employment. Then, meta-regression analysis is adopted to study the sample data from four aspects, including industry dimension, time dimension, income dimension, and tropical cyclone intensity. The conclusions provide some suggestions for the medium- and long-term management of tropical cyclones. The differences between
quantile level changes linearly in time. Unlike Reich’s model-dependent method, Chapman et al. (2013) and Stainforth et al. (2013) derived a local trend parameter of temperature change; thus, the relative changes of temperature between different quantiles and between different geographical locations for the same quantiles could be evaluated. An alternative approach to detect spatial variation patterns of climate extremes is by combining quantile regression and cluster analysis ( Barbosa et al. 2011
quantile level changes linearly in time. Unlike Reich’s model-dependent method, Chapman et al. (2013) and Stainforth et al. (2013) derived a local trend parameter of temperature change; thus, the relative changes of temperature between different quantiles and between different geographical locations for the same quantiles could be evaluated. An alternative approach to detect spatial variation patterns of climate extremes is by combining quantile regression and cluster analysis ( Barbosa et al. 2011
on the Stuttgart neural network [the recurrent neural network (RNN) method]. This paper simulates the brightness temperatures at the 35 frequency channels using upper-air ascent data of 6 yr in combination with the monochromatic radiative transfer model (MonoRTM), and establishes their relationship with the vertical profiles of temperature and humidity based on principal component analysis (PCA) and the stepwise regression method. The accuracy of this retrieval method would be determined by
on the Stuttgart neural network [the recurrent neural network (RNN) method]. This paper simulates the brightness temperatures at the 35 frequency channels using upper-air ascent data of 6 yr in combination with the monochromatic radiative transfer model (MonoRTM), and establishes their relationship with the vertical profiles of temperature and humidity based on principal component analysis (PCA) and the stepwise regression method. The accuracy of this retrieval method would be determined by
predictor analysis (PPA) are pattern methods specifically tailored for use in linear regression models and, unlike CCA and MCA, are asymmetric in their treatment of the two datasets, identifying one dataset as the predictor and the other as the predictand. RDA selects predictor components that maximize explained variance ( von Storch and Zwiers 1999 ; Wang and Zwiers 2001 ). PPA selects predictor components that maximize the sum of squared correlations ( Thacker 1999 ). Another commonly used pattern
predictor analysis (PPA) are pattern methods specifically tailored for use in linear regression models and, unlike CCA and MCA, are asymmetric in their treatment of the two datasets, identifying one dataset as the predictor and the other as the predictand. RDA selects predictor components that maximize explained variance ( von Storch and Zwiers 1999 ; Wang and Zwiers 2001 ). PPA selects predictor components that maximize the sum of squared correlations ( Thacker 1999 ). Another commonly used pattern
regarding directionality, correlation-based methods, such as lagged linear regression, remain popular and useful tools for identifying lagged relationships between climate variables. A lagged regression model can provide a straightforward assessment of spatial and temporal variability. Lagged regression analysis has been a popular technique in climate science for nearly 100 years (e.g., Walker 1923 , 1924 ). Since 1988, the phrases “lagged regression,” “lag regression,” “lagged correlation,” and “lag
regarding directionality, correlation-based methods, such as lagged linear regression, remain popular and useful tools for identifying lagged relationships between climate variables. A lagged regression model can provide a straightforward assessment of spatial and temporal variability. Lagged regression analysis has been a popular technique in climate science for nearly 100 years (e.g., Walker 1923 , 1924 ). Since 1988, the phrases “lagged regression,” “lag regression,” “lagged correlation,” and “lag
Rohlf 1995 ), it is known as the geometric mean regression coefficient. The latter nomenclature follows because is the geometric mean of the estimates and . Henceforth, we write Note that is also the regression coefficient obtained when x is normalized by s x , y is normalized by s y , and the perpendicular distance from the regression line is minimized rather than the vertical distance as in an ordinary least squares fit. A principal component analysis of the normalized variables
Rohlf 1995 ), it is known as the geometric mean regression coefficient. The latter nomenclature follows because is the geometric mean of the estimates and . Henceforth, we write Note that is also the regression coefficient obtained when x is normalized by s x , y is normalized by s y , and the perpendicular distance from the regression line is minimized rather than the vertical distance as in an ordinary least squares fit. A principal component analysis of the normalized variables
methodology used to train and create the linear regression models as well as the metrics that will be used to evaluate the forecasts. Additionally, a summary of the adjusted framework conceptualization is included. Section 3 covers the different predictors that will be used in the analysis, which are also detailed in Table 1 . An analysis of the much longer preindustrial control run from the Community Earth System Model Large Ensemble (CESM LENS) version 1 configuration is presented in section 4
methodology used to train and create the linear regression models as well as the metrics that will be used to evaluate the forecasts. Additionally, a summary of the adjusted framework conceptualization is included. Section 3 covers the different predictors that will be used in the analysis, which are also detailed in Table 1 . An analysis of the much longer preindustrial control run from the Community Earth System Model Large Ensemble (CESM LENS) version 1 configuration is presented in section 4
human well-being has been relatively well investigated; the literature that has examined the contribution of weather and climate to LS is in comparison more limited. 3 This paper proposes alternative LS regression models incorporating climate variables. We then conduct a spatial analysis using geographically weighted regression to examine the variations in the model(s) across different regions of Australia. The outcomes from this research can inform predictions about how the anticipated changes in
human well-being has been relatively well investigated; the literature that has examined the contribution of weather and climate to LS is in comparison more limited. 3 This paper proposes alternative LS regression models incorporating climate variables. We then conduct a spatial analysis using geographically weighted regression to examine the variations in the model(s) across different regions of Australia. The outcomes from this research can inform predictions about how the anticipated changes in
2038 JOURNAL OF THE ATMOSPHERIC SCIENCES VOL.$0, NO. 13Downstream Development of Baroclinic Waves As Inferred from Regression Analysis EDMUND K. M. CHANGProgram in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey(Manuscript received 17 July 1992, in final form 12 October 1992) ABSTRACT The structure and evolution of transient disturbances in
2038 JOURNAL OF THE ATMOSPHERIC SCIENCES VOL.$0, NO. 13Downstream Development of Baroclinic Waves As Inferred from Regression Analysis EDMUND K. M. CHANGProgram in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey(Manuscript received 17 July 1992, in final form 12 October 1992) ABSTRACT The structure and evolution of transient disturbances in
