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

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 i...

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