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A general asymptotic framework is developed for studying consis- tency properties of principal component analysis (PCA). Our frame- work includes several previously studied domains of asymptotics as special cases and allows one to…
In this article, we review the interdisciplinary techniques (borrowed from physics, mathematics, statistics, machine-learning, etc.) and methodological framework that we have used to understand climate systems, which serve as examples of…
Changing climate signals and the continuous world population growth requires proper hydrologic risk analysis to build and operate water resource infrastructures in a sustainable way. Although modernized computational facilities are becoming…
To monitor the acidity of rain water in real time, a rain water sampling system was developed. The rain sampler detects the initial rain after a dry spell and collects a water sample. Before performing the measurements, the pH probe is…
Non-stationarity affects the sensitivity of change detection in correlated systems described by sets of measurable variables. We study this by projecting onto different principal components. Non-stationarity is modeled as multiple normal…
Extensive aircraft cloud physical observations made in more than 2000 tropical cumulus clouds indicated new observational evidence viz., (1) horizontal structure of the air flow inside the cloud has consistent variations with successive…
In computer vision applications, the visibility of the video content is crucial to perform analysis for better accuracy. The visibility can be affected by several atmospheric interferences in challenging weather-one of them is the…
Many physical processes involve spatio-temporal observations, which can be studied at different spatial and temporal scales. For example, rainfall data measured daily by rain gauges can be considered at daily, monthly or annual temporal…
Identification of the current and expected future pollution sources to rivers is crucial for sound environmental management. For this purpose numerous approaches were proposed that can be clustered under physical based models, stable…
A system with many degrees of freedom can be characterized by a covariance matrix; principal components analysis (PCA) focuses on the eigenvalues of this matrix, hoping to find a lower dimensional description. But when the spectrum is…
Forecasting compound floods presents a significant challenge due to the intricate interplay of meteorological, hydrological, and oceanographic factors. Analyzing compound floods has become more critical as the global climate increases flood…
We analyze solar, geomagnetic and cosmic ray flux data along with rainfall and temperature data for almost five solar cycles. We provide evidence of significant influence of solar variability on climate. Specifically, we demonstrate…
We present two diagnostic methods based on ideas of Principal Component Analysis and demonstrate their efficiency for sophisticated processing of multicolour photometric observations of variable objects.
Photometric measurements are prone to systematic errors presenting a challenge to low-amplitude variability detection. In search for a general-purpose variability detection technique able to recover a broad range of variability types…
Extratropical storms shape midlatitude weather and vary due to the slowly evolving climate and the rapid changes in synoptic conditions. While the influence of each factor has been studied extensively, their relative importance remains…
Principal Component Analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for…
Recent research has community have shown that tropical convection and rainfall is sensitive to mid-tropospheric humidity. Therefore it has been suggested to improve the representation of moist convection by making cumulus parameterizations…
Whether a variable is the cause of another, or simply associated with it, is often an important scientific question. Causal Inference is the name associated with the body of techniques for addressing that question in a statistical setting.…
Urban rail transit provides significant comprehensive benefits such as large traffic volume and high speed, serving as one of the most important components of urban traffic construction management and congestion solution. Using real…
The Meteorology is a field where huge amounts of data are generated, mainly collected by sensors at weather stations, where different variables can be measured. Those data have some particularities such as high volume and dimensionality,…