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representation due to spectral truncation for 10 equally populated bins based on values of ensemble variances (blue dots). Results are shown for temperature for the month of January. The linear regression is also shown (red line). 4. Analysis of the H–L and Desroziers methods In this section we further explore the predictive relationship between the ensemble variance and the variance associated with representation error, qualitatively illustrated in the previous section. In what follows, we address how well
representation due to spectral truncation for 10 equally populated bins based on values of ensemble variances (blue dots). Results are shown for temperature for the month of January. The linear regression is also shown (red line). 4. Analysis of the H–L and Desroziers methods In this section we further explore the predictive relationship between the ensemble variance and the variance associated with representation error, qualitatively illustrated in the previous section. In what follows, we address how well
-cover type to compare GRACE-MWD via these remote sensing datasets. The power spectrum analysis was employed to identify the dominant frequency of the signal for each remote-sensed dataset at each position ( Parsons et al. 2017 ). The dominant frequency at the highest spectral density was used to depict the main periodic characteristics of the time series. To obtain the details of the trend, a simple linear regression was used to model the time series of the remotely sensed datasets as a function of time
-cover type to compare GRACE-MWD via these remote sensing datasets. The power spectrum analysis was employed to identify the dominant frequency of the signal for each remote-sensed dataset at each position ( Parsons et al. 2017 ). The dominant frequency at the highest spectral density was used to depict the main periodic characteristics of the time series. To obtain the details of the trend, a simple linear regression was used to model the time series of the remotely sensed datasets as a function of time
1. Introduction In the recent decades, climate change has been a subject of ongoing debate, which is also in part related to the inevitable discontinuities in long-term climate data records that hamper the reliability of the results of historical climate trend assessment, climate change detection, and attribution. The effects of artificial shifts (nonclimatic sudden changes) on the results of climate analysis, especially of historical climate trend assessment, have been illustrated in many
1. Introduction In the recent decades, climate change has been a subject of ongoing debate, which is also in part related to the inevitable discontinuities in long-term climate data records that hamper the reliability of the results of historical climate trend assessment, climate change detection, and attribution. The effects of artificial shifts (nonclimatic sudden changes) on the results of climate analysis, especially of historical climate trend assessment, have been illustrated in many
significant differences extending back 120 h when the troughs were over land ( Fig. 11a ). This difference in moisture is ahead (west) of the composited troughs over West Africa and the eastern Atlantic. Analysis of lagged maps reveals that this represents a downstream AEW trough for the developing waves, which has brought moisture anomalously far north on its eastern edge. Although this difference in moisture is present over West Africa based on the logistic regression output it appears to not influence
significant differences extending back 120 h when the troughs were over land ( Fig. 11a ). This difference in moisture is ahead (west) of the composited troughs over West Africa and the eastern Atlantic. Analysis of lagged maps reveals that this represents a downstream AEW trough for the developing waves, which has brought moisture anomalously far north on its eastern edge. Although this difference in moisture is present over West Africa based on the logistic regression output it appears to not influence
been used as the foundation of the TreeGen downscaling algorithm ( Stahl et al. 2008 ; Cannon 2008 ). Other forms of automated, supervised synoptic classification have been devised, including fuzzy rule-based classification ( Bardossy et al. 1995 ) and screening discriminant analysis ( Enke et al. 2005 ) techniques. All three methods are somewhat “exotic” and fall outside the suite of widely accepted cluster analysis and regression techniques used in synoptic climatology. Because joint
been used as the foundation of the TreeGen downscaling algorithm ( Stahl et al. 2008 ; Cannon 2008 ). Other forms of automated, supervised synoptic classification have been devised, including fuzzy rule-based classification ( Bardossy et al. 1995 ) and screening discriminant analysis ( Enke et al. 2005 ) techniques. All three methods are somewhat “exotic” and fall outside the suite of widely accepted cluster analysis and regression techniques used in synoptic climatology. Because joint
mitigate overfitting, including truncated singular value decomposition (SVD) analysis, ridge regression, and constrained least squares ( Yun et al. 2003 ; van den Dool and Rukhovets 1994 ; DelSole 2007 ). These procedures often are applied on a point-by-point basis. Recently, it has been recognized that seemingly different approaches to multimodel forecasting are actually special cases of a single Bayesian methodology, each distinguished by different prior assumptions on the model weights ( DelSole
mitigate overfitting, including truncated singular value decomposition (SVD) analysis, ridge regression, and constrained least squares ( Yun et al. 2003 ; van den Dool and Rukhovets 1994 ; DelSole 2007 ). These procedures often are applied on a point-by-point basis. Recently, it has been recognized that seemingly different approaches to multimodel forecasting are actually special cases of a single Bayesian methodology, each distinguished by different prior assumptions on the model weights ( DelSole
, 7570 – 7585 , doi: 10.1175/JCLI-D-12-00729.1 . Draper , N. R. , and H. Smith , 1981 : Applied Regression Analysis . 2nd ed. John Wiley and Sons, 709 pp . Feldstein , S. B. , 2000 : The timescale, power spectra, and climate noise properties of teleconnection patterns . J. Climate , 13 , 4430 – 4440 , doi: 10.1175/1520-0442(2000)013<4430:TTPSAC>2.0.CO;2 . Graybill , F. A. , 1983 : Matrices with Applications in Statistics . Wadsworth International, 461 pp . Hansen , J. , R. Ruedy
, 7570 – 7585 , doi: 10.1175/JCLI-D-12-00729.1 . Draper , N. R. , and H. Smith , 1981 : Applied Regression Analysis . 2nd ed. John Wiley and Sons, 709 pp . Feldstein , S. B. , 2000 : The timescale, power spectra, and climate noise properties of teleconnection patterns . J. Climate , 13 , 4430 – 4440 , doi: 10.1175/1520-0442(2000)013<4430:TTPSAC>2.0.CO;2 . Graybill , F. A. , 1983 : Matrices with Applications in Statistics . Wadsworth International, 461 pp . Hansen , J. , R. Ruedy
1. Introduction In this study, we introduce a novel procedure to extract modes of variability of climate that correspond to oscillatory patterns coevolving in space and time. We call the new method “multichannel empirical orthogonal teleconnection” (MEOT) and illustrate its application to a sea surface temperature (SST) dataset as an example. We also explore the differences and similarities between the new MEOT method and the more well-known multichannel singular spectrum analysis (MSSA). Earth
1. Introduction In this study, we introduce a novel procedure to extract modes of variability of climate that correspond to oscillatory patterns coevolving in space and time. We call the new method “multichannel empirical orthogonal teleconnection” (MEOT) and illustrate its application to a sea surface temperature (SST) dataset as an example. We also explore the differences and similarities between the new MEOT method and the more well-known multichannel singular spectrum analysis (MSSA). Earth
Evaluation of a Support Vector Machine–Based Single-Doppler Wind Retrieval Algorithmof observation points in the analysis volume, and the summation is over i from 1 to N for all observation points in the analysis volume. The derived equations yielding the minimum difference are or, equivalently, where and . b. The SVM-based VVP algorithm Regarding the radial speed equation, SVM-based VVP attempts to use SVM regression to retrieve the wind field parameters instead of the least squares technique. Through the analysis of the general support vector regression machine, the
of observation points in the analysis volume, and the summation is over i from 1 to N for all observation points in the analysis volume. The derived equations yielding the minimum difference are or, equivalently, where and . b. The SVM-based VVP algorithm Regarding the radial speed equation, SVM-based VVP attempts to use SVM regression to retrieve the wind field parameters instead of the least squares technique. Through the analysis of the general support vector regression machine, the
of temperature at several pressure levels within the lower stratosphere. We present a multiple regression analysis of Southern Hemisphere polar temperatures, using the National Centers for Environmental Prediction (NCEP) reanalysis dataset, designed to show the influence of various factors on the seasonal evolution of the polar vortex. We apply the same definition of final warming date as used on the radiosonde temperatures to the NCEP polar data to indicate the extent to which these factors may
of temperature at several pressure levels within the lower stratosphere. We present a multiple regression analysis of Southern Hemisphere polar temperatures, using the National Centers for Environmental Prediction (NCEP) reanalysis dataset, designed to show the influence of various factors on the seasonal evolution of the polar vortex. We apply the same definition of final warming date as used on the radiosonde temperatures to the NCEP polar data to indicate the extent to which these factors may
