scholarly journals Application of Principal Component Analysis to CHAMP Radio Occultation Data for Quality Control and a Diagnostic Study

2006 ◽  
Vol 134 (11) ◽  
pp. 3263-3282 ◽  
Author(s):  
Zhen Zeng ◽  
X. Zou
Keyword(s):  
Principal Component Analysis ◽  
Quality Control ◽  
Radio Occultation ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Diagnostic Study ◽  
The North ◽  
Vertical Scale ◽  
The Tropics

Abstract A principal component analysis (PCA) method is applied to Challenging Minisatellite Payload (CHAMP) level-2 radio occultation (RO) observations and the corresponding global analyses from the National Centers for Environmental Prediction (NCEP) in March 2004. The PCA is performed on a square symmetric vertical correlation matrix of observed or modeled RO profiles. By decomposing the matrix into pairs of loadings (EOFs) and associated principal components (PCs), outliers are identified and important modes that explain most variances of the vertical variability of the atmosphere as represented by the GPS RO data and the NCEP analyses are extracted and compared. Specifically, a quality control of RO data based on Hotelling’s T 2 index is applied first, which removes 255 RO profiles from 4884 total profiles (about 5%) and smoothes the distributions of PC modes, making the remaining GPS RO dataset much more meaningful. The leading PC mode for global refractivity explains 60% of the total variance and is associated with a symmetric zonal pattern, with positive anomalies in the Tropics and negative anomalies at the two poles. The second PC mode explains an additional 16% of the total variance and shows a dipole pattern with positive anomalies in the North Pole and negative anomalies in the South Pole. Three significant positive anomalies are also found in the second and third PC modes over three predominant convective areas in the western Pacific, South America, and Africa in the Tropics. The first leading PC mode calculated from global NCEP analyses compared favorably with that from CHAMP observations, which proves that NCEP analyses are capable of representing most of the variance of the atmospheric profiles. However, disagreements between CHAMP observations and NCEP analyses are noticed in the second EOF over the Tropics and the Southern Hemisphere (SH). It is also found that the NCEP analyses describe CHAMP-observed larger vertical scale features better than smaller-scale features, captures features of more leading EOF modes in the Northern Hemisphere than in the SH and the Tropics, and does not capture the vertical structures revealed by the EOFs in CHAMP observations near and above the tropopause in the Tropics.

Principal component analysis of substituent constants

1990 ◽  
Vol 55 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Drahomír Hnyk
Keyword(s):  
Principal Component Analysis ◽  
Inductive Effect ◽  
Resonance Effect ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Data Matrix ◽  
Cumulative Proportion ◽  
Two Factors ◽  
Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

scholarly journals Multi-trait Analysis of Agroclimate Variations During the Growing Season in East-Central Poland (1971-2005)

2015 ◽  
Vol 29 (2) ◽  
pp. 213-219 ◽  
Author(s):  
Elżbieta Radzka ◽  
Katarzyna Rymuza
Keyword(s):  
Principal Component Analysis ◽  
Air Temperature ◽  
Meteorological Data ◽  
Principal Component ◽  
Growing Season ◽  
Component Analysis ◽  
The South ◽  
East Central ◽  
Central Poland ◽  
The North

Abstract The work is based on meteorological data recorded by nine stations of the Institute of Meteorology and Water Management located in east-central Poland from 1971 to 2005. The region encompasses the North Podlasian Lowland and the South Podlasian Lowland. Average values of selected agroclimate indicators for the growing season were determined. Moreover, principal component analysis was conducted to indicate elements that exerted the greatest influence on the agroclimate. Also, cluster analysis was carried out to select stations with similar agroclimate. Ward method was used for clustering and the Euclidean distance was applied. Principal component analysis revealed that the agroclimate of east-central Poland was predominantly affected by climatic water balance, number of days of active plant growth, length of the farming period, and the average air temperature during the growing season (Apr-Sept). Based on the analysis, the region of east-central Poland was divided into two groups (areas) with different agroclimatic conditions. The first area comprized the following stations: Szepietowo and Białowieża located in the North Podlasian Lowland and Biała Podlaska situated in the northern part of the South Podlasian Lowland. This area was characterized by shorter farming periods and a lower average air temperature during the growing season. The other group included the remaining stations located in the western part of both the Lowlands which was warmer and where greater water deficits were recorded.

scholarly journals Penggunaan Analisis Komponen Utama Dalam Penggabungan Data Peubah Ganda pada Kasus Produksi Pertanian dan Perkebunan Di Wilayah Bolaang Mongondow Tahun 2008

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (2) ◽  
pp. 1 ◽  
Author(s):  
Sunarsi Habib Abdurrachman ◽  
Hanny Komalig ◽  
Nelson Nainggolan
Keyword(s):  
Principal Component Analysis ◽  
Initial Data ◽  
Principal Components ◽  
Multivariate Data ◽  
Secondary Data ◽  
Principal Component ◽  
Component Analysis ◽  
The North ◽  
North Sulawesi ◽  
Production Agriculture

Abstract The objective of this research is to study the combine the two groups of data with multivariate variables using Principal Component Analysis. The data used in this study is a secondary data drawn from the North Sulawesi BPS data in Production Agriculture and Plantation Bolaang Mongondow region in 2008. The results show that PCA can be used to combining two separate groups multivariate data and the correlation between the Principal Components of the data are combined with the Principal Component of the overall initial data (intact) is relatively high wich correlation between PC1 and PC1AB as big 0,987 and correlation between PC2 and PC2AB as big 0,916. Keywords : Principal Component Analysis, Agriculture Production and Plantation Abstrak Tujuan penelitian ini adalah menggabungkan dua gugus data peubah ganda dengan menggunakan Analisis Komponen Utama. Data yang digunakan dalam penelitian ini merupakan data sekunder yang diambil dari BPS Sulawesi Utara yakni Data Produksi Pertanian Dan Perkebunan Di Wilayah Bolaang Mongondow Tahun 2008. Hasilnya menunjukkan bahwa AKU dapat digunakan untuk menggabungkan dua gugus data peubah ganda yang terpisah dan korelasi antara komponen utama dari data yang digabungkan dengan komponen utama dari keseluruhan data awal (utuh)  relatif tinggi yakni dengan nilai korelasi PC1 dan PC1AB sebesar 0,987 dan PC2 dan PC2AB  sebesar 0,916.   Kata kunci : Analisis Komponen Utama, Produksi Pertanian dan Perkebunan

scholarly journals Spatial and temporal variation of total electron content as revealed by principal component analysis

2016 ◽  
Vol 34 (12) ◽  
pp. 1109-1117 ◽  
Author(s):  
Elsayed R. Talaat ◽  
Xun Zhu
Keyword(s):  
Principal Component Analysis ◽  
Total Electron Content ◽  
General Circulation ◽  
Circulation Model ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Spatial And Temporal Variation ◽  
Electron Content ◽  
Total Electron

Abstract. Eleven years of global total electron content (TEC) data derived from the assimilated thermosphere–ionosphere electrodynamics general circulation model are analyzed using empirical orthogonal function (EOF) decomposition and the corresponding principal component analysis (PCA) technique. For the daily averaged TEC field, the first EOF explains more than 89 % and the first four EOFs explain more than 98 % of the total variance of the TEC field, indicating an effective data compression and clear separation of different physical processes. The effectiveness of the PCA technique for TEC is nearly insensitive to the horizontal resolution and the length of the data records. When the PCA is applied to global TEC including local-time variations, the rich spatial and temporal variations of field can be represented by the first three EOFs that explain 88 % of the total variance. The spectral analysis of the time series of the EOF coefficients reveals how different mechanisms such as solar flux variation, change in the orbital declination, nonlinear mode coupling and geomagnetic activity are separated and expressed in different EOFs. This work demonstrates the usefulness of using the PCA technique to assimilate and monitor the global TEC field.

scholarly journals Data Mining to Select Relevant Variables Influencing External and Internal Workload of Elite Blind 5-a-Side Soccer

2021 ◽  
Vol 18 (6) ◽  
pp. 3155
Author(s):  
José M. Gamonales ◽  
Kiko León ◽  
Daniel Rojas-Valverde ◽  
Braulio Sánchez-Ureña ◽  
Jesús Muñoz-Jiménez
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Technique ◽  
Total Variance ◽  
Data Mining Technique ◽  
Internal Load ◽  
Relevant Variables ◽  
Data Reduction Method

(1) Background: Data mining has turned essential when exploring a large amount of information in performance analysis in sports. This study aimed to select the most relevant variables influencing the external and internal load in top-elite 5-a-side soccer (Sa5) using a data mining model considering some contextual indicators as match result, body mass index (BMI), scoring rate and age. (2) Methods: A total of 50 top-elite visually impaired soccer players (age 30.86 ± 11.2 years, weight 77.64 ± 9.78 kg, height 178.48 ± 7.9 cm) were monitored using magnetic, angular and rate gyroscope (MARG) sensors during an international Sa5 congested fixture tournament.; (3) Results: Fifteen external and internal load variables were extracted from a total of 49 time-related and peak variables derived from the MARG sensors using a principal component analysis as the most used data mining technique. The principal component analysis (PCA) model explained 80% of total variance using seven principal components. In contrast, the first principal component of the match was defined by jumps, take off by 24.8% of the total variance. Blind players usually performed a higher number of accelerations per min when losing a match. Scoring players execute higher DistanceExplosive and Distance21–24 km/h. And the younger players presented higher HRAVG and AccMax. (4) Conclusions: The influence of some contextual variables on external and internal load during top elite Sa5 official matches should be addressed by coaches, athletes, and medical staff. The PCA seems to be a useful statistical technique to select those relevant variables representing the team’s external and internal load. Besides, as a data reduction method, PCA allows administrating individualized training loads considering those relevant variables defining team load behavior.

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