Eigenvectors for representing the 500 mb geopotential surface over the Northern Hemisphere

The aim of this paper is to make a synoptic-climatic classification of atmospheric circulation in order to obtain synoptic frost-related patterns in the wet Pampas. Both partial (recorded in 25 to 75% of the meteorological stations) and widespread (recorded in more than 75%) frost events that occurred during the winters (June-July-August) of 1972-83 are included in this study. Frost days are grouped into Neutral (G1), El Niño (G2), and La Niña (G3) years. In addition, the complete dataset, called the total group, is analysed for reference purposes. Each group is analysed using the unrotated and Varimax rotated principal component analysis using the T-mode approach. Six synoptic situations accounted for 94% of the variance associated with frosts in the area studied.In general, the principal component score patterns given by the unrotated and rotated components are similar. The biggest difference between unrotated and rotated solutions was in variance redistribution. After rotation, only one of the two possible situations (direct/inverse) of each pattern represented a real synoptic type associated with frost in the wet Pampas. Persistence and location cause temperature drops in the area studied. The most frequent patterns in rotated results are those termed A, B, C* (in G2) and D (in G1 and G3). They are connected with cold anticyclones, which cause advective and/or radiative frosts. The remaining patterns accounted for about 5% of the variance and represent real, though rare, situations, which are important because of their effect on the wet Pampas.The results obtained for the different groups show that inter-winter variability of the equatorial Pacific signal produces changes in the frequency of frost-connected patterns, rather than different patterns for cold events.

Section: Methodsmentioning

confidence: 99%

Surface circulation associated with frost in the wet Pampas

Müller

Compagnucci

Núñez

et al. 2003

“…Since the first few components explain most of the variance of the original dataset, truncation rules are used to identify which of the subsequent components, order by variance explained, may be discarded. In this analysis, a combination of the logarithmic eigenvalue plot (Craddock and Flood, 1969), the Monte Carlo technique of Overland and Preisendorfer (1982) and a subjective criteria, that the components be readily interpretable, were used to identify whether the components should be retained. The unrotated solutions were then subjected to Varimax orthogonal and Harris-Kaiser (H-K) II oblique rotations, and the solutions compared using simple structure plots (Richman, 1986) and coefficients of congruence (White et al, 1991). 3.1.4.…”

Section: Synoptic Classificationmentioning

confidence: 99%

Downscaling temperature and precipitation: a comparison of regression‐based methods and artificial neural networks

Schoof

Pryor

2001

236

148

A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18-year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing season and five significant components for the non-growing season. These PCs explained 86% and 83% of the original rawinsonde data for the growing and non-growing seasons, respectively. Cluster analysis of the PC scores using the average linkage method resulted in eight growing season synoptic types and twelve non-growing synoptic types. The downscaling of temperature and precipitation is conducted using PC scores and cluster frequencies in regression models and artificial neural networks (ANNs).Regression models and ANNs yielded similar results, but the data for each regression model violated at least one of the assumptions of regression analysis. As expected, the accuracy of the downscaling models for temperature was superior to that for precipitation. The accuracy of all temperature models was improved by adding an autoregressive term, which also changed the relative importance of the dominant anomaly patterns as manifest in the PC scores. Application of the transfer functions to model daily maximum and minimum temperature data from an independent time series resulted in correlation coefficients of 0.34 -0.89. In accord with previous studies, the precipitation models exhibited lesser predictive capabilities. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0.5 for both seasons, while that for monthly total precipitation was below 0.65. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements.

“…Correlation matrices are used as the dispersion matrices for the PCA to weight the input variables evenly, and the resulting PCs are rotated using (orthogonal) Varimax rotation (Richman, 1986;Bonell and Sumner, 1992;Brinkmann, 1999a). The number of PCs to retain for rotation and interpretation is evaluated using a number of methods, including scree plots (Catell, 1966), logarithmic eigenvalue plots (Craddock and Flood, 1969) and statistical tests, such as the N rule (Overland and Preisendorfer, 1982). The resulting PC scores are then clustered to produce synoptic weather types using a hierarchical, agglomerative algorithm followed by a non-hierarchical 'reassigning' algorithm (Davis and Kalkstein, 1990).…”

Section: Development Of the Synoptic Classificationsmentioning

confidence: 99%

Evaluation of the NCEP–NCAR reanalysis in terms of synoptic‐scale phenomena: a case study from the Midwestern USA

Schoof

Pryor

2003

We evaluate the ability of the National Centers for Environmental Prediction (NCEP)-National Center for Atmosphere Research (NCAR) reanalysis to represent the synoptic-scale climate of the Midwestern USA relative to radiosonde data. Independent, automated synoptic classifications, based on rotated principal component analysis (PCA) of 500 hPa geopotential heights, 850 hPa air temperatures, and 200 hPa wind speeds and a two-step clustering algorithm, result in a 15-type NCEP-NCAR synoptic classification and a 14-type radiosonde classification. The classifications are examined in terms of similarities and differences in the modes of variance manifest in the PCA solutions, the spatial patterns and variability of input variables within each weather type, and the temporal variability of the occurrence of each weather type. The classifications are then compared in terms of these characteristics and the degree of mutual class occupancy. Although the classifications identify a number of the same weather types (in terms of the input data, PCA solution, and mutual occupancy), the correspondence is imperfect. To assess whether the differences in the classifications are due to errant assignment of data to clusters or to differences in the fundamental modes present in the data sets as represented by the PC loadings and scores, a third targeted classification is undertaken that categorizes the NCEP-NCAR reanalysis data according to the radiosonde PCA solution. This classification exhibits a higher degree of similarity to that derived using the radiosonde data (in terms of both interpretability and mutual class occupancy), but the solutions still exhibit considerable differences. It is probable that the discrepancies are partly a function of the differing data structures and densities, but they may also reflect differences in the intensity of synoptic-scale phenomena as manifest in the data sets.