Empirical orthogonal function analysis of humidity profiles over the Indian Ocean and an assessment of their retrievability using satellite microwave radiometry

Abstract

The paper examines the variability of vertical humidity profiles over the Indian oceanic region using a set of 1200 radiosonde observations spanning 10 years (1982–1991). The examination is based upon the method of empirical orthogonal function (EOF) analysis. The first EOF explains 61% of the total variance and the first three EOFs together account for 85% of the total variability. The first principal component is almost perfectly correlated with the total precipitable water (TPW) and the second one is well correlated with the ratio of boundary layer moisture and TPW. This fact and an inequality derived from the analysis of the variances of individual terms of the EOF expansion of specific humidity are utilised to establish an algorithm for retrieving humidity profile from satellite microwave measurement of TPW over the region of study. Power of the retrieval technique is demonstrated using 127 independent radiosonde measurements and by plotting the profiles of rms error and bias. The method is found to be distinctly superior compared to a power‐law retrieval. A few examples of profile retrieval from satellite measurements of TPW have been checked against colocated radiosonde measurements. Some examples of retrieval show that the method is uniquely able to capture the humidity variability in the boundary layer, particularly the high moisture loading in the monsoon season.

Potential Impacts of Sahara Dust Aerosol on Rainfall Vertical Structure Over the Atlantic Ocean as Identified From EOF Analysis

“…To isolate vertical variability patterns in the precipitation profile, the EOF analysis (Basu et al, ; Björnsson & Venegas, ; W. Liu et al, ; Wagner et al, ) was applied to precipitation profiles of convective and stratiform rain in the dust‐laden and dust‐free sectors separately, as shown in Figures b–d for convective rain and Figures f–h for stratiform rain. The EOF analysis was performed via covariance matrixes, and the EOF modes (i.e., the eigenvectors) were normalized by the square root of the associated eigenvalues, using the functions in the NCAR Command Language (http://www.ncl.ucar.edu/).…”

“…To isolate and quantify the aerosol effect on rainfall vertical structure, this paper adopted the empirical orthogonal functions (EOF) method (Basu et al, ; Björnsson & Venegas, ; W. Liu et al, ; Wagner et al, ). The EOF method decomposes multidimensional meteorological data into independent and orthogonal components (Bauer, ).…”

“…To isolate and quantify the aerosol effect on rainfall vertical structure, this paper adopted the empirical orthogonal functions (EOF) method (Basu et al, 1995;Björnsson & Venegas, 1997;W. Liu et al, 1991;Wagner et al, 1990).…”

Forecasting of scatterometer‐derived wind fields in the north Indian Ocean with a data‐adaptive approach

“…Hence the empirical orthogonal function (EOF) technique has been used to compress the major part of the spatial variability in a few eigenmodes following the studies by Alvarez et al [2000] and Sharma et al [2007a, 2007b] and GA has been applied on the individual principal components (PCs) of the zonal and meridional wind field separately. Although EOF technique has been elaborately described elsewhere [ Preseindorfer , 1988; Basu et al , 1995], the salient features are described below for the sake of self‐consistency of this paper. In this technique the parameter vector is expanded in a series of empirical orthogonal vectors, which are nothing but the eigenvectors of the covariance matrix of the data set, in the following manner: Here u ( x,y,p ) denotes the zonal (or meridional ) wind field derived by the scatterometer onboard QuikSCAT on p th day denotes the temporal mean at each grid location, and N is the total number of grid points considered in the study.…”

A SEEK filter assimilation of sea surface salinity from Aquarius in an OGCM: Implication for surface dynamics and thermohaline structure

“…Since we have used only two years of data, the question which immediately comes to the mind is, whether the EOFs are stable or not. In order to check this, we used the well known stability criterion [Basu et al, 1995] Stability RatioðiÞ5 eigenvalueðiÞ eigenvalueðiÞ2eigenvalueði11Þ Ã ffiffiffi ffi 2 N r Here i is the mode index and N is the total number of simulated current vectors undergoing EOF analysis. The stability ratios for the first three modes were found to be 0.12, 0.11, and 0.16, respectively.…”