Emily L. Kang

University of Cincinnati | UC · Department of Mathematical Sciences

Remote sensing data have been widely used to study various geophysical processes. With the advances in remote sensing technology, massive amount of remote sensing data are collected in space over time. Different satellite instruments typically have different footprints, measurement‐error characteristics, and data coverages. To combine data sets fro...

Multinomial regression is often used to investigate the association between potential independent variables and multi-class nominal responses such as multiple disease subtypes. However, it cannot identify groups of variables that have similar effects on predicting the same subtypes of diseases, which is an important problem in biomedical research....

The spatial autoregressive (SAR) model is a classical model in spatial econometrics and has become an important tool in network analysis. However, with large-scale networks, existing methods of likelihood-based inference for the SAR model become computationally infeasible. We here investigate maximum likelihood estimation for the SAR model with par...

Observing system simulation experiments (OSSEs) have been widely used as a rigorous and cost-effective way to guide development of new observing systems, and to evaluate the performance of new data assimilation algorithms. Nature runs (NRs), which are outputs from deterministic models, play an essential role in building OSSE systems for global atmo...

Motivated by a large ground‐level ozone data set, we propose a new computationally efficient additive approximate Gaussian process. The proposed method incorporates a computational‐complexity‐reduction method and a separable covariance function, which can flexibly capture various spatio‐temporal dependence structures. The first component is able to...

Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co‐kriging models, with the help of scalable techniques, have been able to combine such datasets by using spatially varying bias corrections. The associated Bayesian inference for these models is usually facilitated via Markov chain Monte Carlo (MCMC) met...

Large datasets are daily gathered from different remote sensing platforms and statistical models are usually used to combine them by accounting for spatially varying bias corrections. The statistical inference of these models is usually based on Markov chain Monte Carlo (MCMC) samplers which involve updating a high-dimensional random effect vector...

Remote sensing data products often include quality flags that inform users whether the associated observations are of good, acceptable or unreliable qualities. However, such information on data fidelity is not consistently considered in remote sensing data analyses. Motivated by observations from the atmospheric infrared sounder (AIRS) instrument o...

Remote sensing data products often include quality flags that inform users whether the associated observations are of good, acceptable or unreliable qualities. However, such information on data fidelity is not considered in remote sensing data analyses. Motivated by observations from the Atmospheric Infrared Sounder (AIRS) instrument on board NASA'...

Satellite thermal remote sensing has been utilized to examine the urban heat dynamics in relation to the urban traffic restriction policy. During the 2008 Olympic Games in Beijing, the traffic volume was approximately cut off by half through the road space rationing. Based on daily MODIS satellite thermal observations on the surface temperature, st...

Recent advancements in remote sensing technology and the increasing size of satellite constellations allow for massive geophysical information to be gathered daily on a global scale by numerous platforms of different fidelity. The auto-regressive co-kriging model provides a suitable framework for the analysis of such data sets as it is able to acco...

Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission. One important component in the OCO-2 retrieval algorithm is a full-physics forward model that describes the mathematical relationship between...

No single satellite remote sensing system is able to provide the observations on the Earth's surface at both high spatial and high temporal resolution due to the general trade-off between orbit revisit frequency and satellite sensor's spatial resolution. This paper presents a spatio-temporal Cokriging (ST-Cokriging) method for assimilating remote s...

The Geoscience Laser Altimeter System onboard the NASA Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) provided elevation measurements of Earth's surface between 2003 and 2009. The centroid and maximum-amplitude-peak (MAP) retracking methods have been designed and applied to process the returned laser waveforms for elevation measurements. Al...

Evapotranspiration (ET) is a measure of plant water use that is utilized regionally for drought detection and monitoring, and locally for agricultural water resource management. Understanding the uncertainty associated with this measurement is vital for science predictions and analysis and for water resource management decision making. In this manu...

In this article, computation for the purpose of spatial visualization is presented in the context of understanding the variability in global environmental processes. Here, we generate synthetic but realistic global data sets and input them into computational algorithms that have a visualization capability; we call this a simulation–visualization sy...

Recent advancements in remote sensing technology and the increasing size of satellite constellations allows massive geophysical information to be gathered daily on a global scale by numerous platforms of different fidelity. The auto-regressive co-kriging model is a suitable framework to analyse such data sets because it accounts for cross-dependenc...

The observing system uncertainty experiments (OSUEs) have been widely used as a cost-effective way to make retrieval quality assessment in NASA's Orbiting Carbon Observatory-2 (OCO-2) mission. One important component in the OCO-2 retrieval algorithm is a full-physics forward model that describes the relationship between the atmospheric variables su...

Due to the increased availability of measurements of various geophysical processes, a need has arisen for statistical methods suitable for the analysis of very large nonstationary spatial data sets. The nearest‐neighbor Gaussian process (NNGP) models are one of the latest and most popular Gaussian process‐based models, which reduce computational co...

Spatiotemporal complete sea surface temperature (SST) dataset with higher accuracy and resolution is desirable for many studies in atmospheric science and climate change. The purpose of this study is to establish the spatiotemporal data fusion model, the Hierarchical Bayesian Model (HBM) based on Robust Fixed Rank Filter (R-FRF), that merge Moderat...

We present an exploratory study examining the use of airborne remote-sensing observations to detect ecological responses to elevated CO2 emissions from active volcanic systems. To evaluate these ecosystem responses, existing spectroscopic, thermal, and lidar data acquired over forest ecosystems on Mammoth Mountain volcano, California, were exploite...

Remote sensing data have been widely used to study many geophysical processes. With the advance of remote-sensing technology, massive amount of remote sensing data are collected in space over time. Different satellite instruments typically have different footprints, measurement-error characteristics, and data coverage. To combine datasets from diff...

This research proposes an ensemble method for synergistically combining multiple empirical algorithms to better estimate chlorophyll-a (Chl-a) concentration. In previous studies, different empirical algorithms have been employed separately and a single algorithm was often identified as the most suitable predictor for Chl-a retrieval. Our ensemble m...

We present an exploratory study examining the use of airborne remote sensing observations to detect ecological responses to elevated CO2 emissions from active volcanic systems. To evaluate these ecosystem responses, existing spectroscopic, thermal, and lidar data acquired over forest ecosystems on Mammoth Mountain volcano, California, were exploite...

Seasonal snow cover and its melt dominate regional climate and hydrology in many mountainous regions in the world. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products have been widely used for regional hydrological modeling. However, data gaps in snow products due to frequent clouds remain a serious problem, particularly f...

Remote sensing data are playing a vital role in understanding the pattern of the Earth's geophysical processes in environmental and climate sciences. We propose a spatial data-fusion methodology that is able to take advantage of two (or potentially more) large remote sensing datasets with the exponential family of distributions. Our hierarchical mo...

With the development of new remote sensing technology, large or even massive spatial datasets covering the globe becomes available. Statistical analysis of such data is challenging. This article proposes a semiparametric approach to model large or massive spatial datasets. In particular, a Gaussian process with additive components is proposed, with...

Gaussian process has been widely used in areas including geostatistics and uncertainty quantification due to its parsimonious yet flexible representation of a stochastic process. However, analyzing a large data set with Gaussian process can be challenging due to its O(n³) computational complexity, where n denotes the size of the data set. The recen...

Climate models have become the primary tools for scientists to project climate-change into the future and to understand its potential impact. Continental-scale General Circulation Models (GCMs) oversimplify the regional climate processes and geophysical features such as topography and land cover. The consequences of local/regional climate change ar...

Metabolomics is the comprehensive study of small molecule metabolites in biological systems. By assaying and analyzing thousands of metabolites in biological samples, it provides a whole picture of metabolic status and biochemical events happening within an organism and has become an increasingly powerful tool in the disease research. In metabolomi...

Sea surface temperature (SST) plays a vital role in the Earth’s atmosphere and climate systems. Complete and accurate SST observations are in great demand for forecasting tropical cyclones and projecting climate change. Satellite remote sensing has been used to retrieve SST globally, but missing values and biased observations impose difficulties on...

We consider current (1971–2000) and future (2041–2070) average seasonal surface temperature fields from two regional climate models (RCMs) driven by the same atmosphere–ocean general circulation model (GCM) in the North American Regional Climate Change Assessment Program (NARCCAP) Phase II experiment. We analyze the difference between future and cu...

Calculating the statistical linear response of turbulent dynamical systems to the change in external forcing is a problem of wide contemporary interest. Here the authors apply linear regression models with memory, AR(p) models, to approximate this statistical linear response by directly fitting the autocorrelations of the underlying turbulent dynam...

Cucurbit downy mildew caused by Pseudoperonospora cubensis is economically the most important disease of cucurbits globally and the pathogen is disseminated aerially over a large spatial scale. Spatio-temporal spread of the disease was characterized during phase I (low and sporadic disease outbreaks) and II (rapid increase in disease outbreaks) of...

Fundamental barriers in practical filtering of nonlinear spatio-temporal chaotic systems are model errors attributed to the stiffness in resolving multiscale features. Recently, reduced stochastic filters based on linear stochastic models have been introduced to overcome such stiffness; one of them is the Mean Stochastic Model (MSM) based on a diag...

This paper presents a fast reduced filtering strategy for assimilating multiscale systems in the presence of observations of only the macroscopic (or large scale) variables. This reduced filtering strategy introduces model errors in estimating the prior forecast statistics through the (heterogeneous multiscale methods) HMM-based reduced climate mod...

We investigate the 20-year-average boreal winter temperatures generated by an ensemble of six regional climate models (RCMs) in phase I of the North American Regional Climate Change Assessment Program. We use the long-run average (20-year integration) to smooth out variability and to capture the climate properties from the RCM outputs. We find that...

Datasets from remote-sensing platforms and sensor networks are often spatial, temporal, and very large. Processing massive amounts of data to provide current estimates of the (hidden) state from current and past data is challenging, even for the Kalman filter. A large number of spatial locations observed through time can quickly lead to an overwhel...

Spatial statistical analysis of massive amounts of spatial data can be challenging because computation of optimal procedures can break down. The Spatial Random Effects (SRE) model uses a fixed number of known but not necessarily orthogonal (multiresolutional) spatial basis functions, which gives a flexible family of nonstationary covariance functio...

The ability to take many observations at precisely known spatial locations has given birth to precision agriculture and transformed traditional agriculture into a spatial science. An important aspect of precision agriculture is its intersection with pedometrics. Maps of soil properties are in great demand, but there is a point at which datasets fro...

The National Aeronautics and Space Administration (NASA) has a remote-sensing program with a large array of satellites whose mission is earth-system science. To carry out this mission. NASA produces data at various levels; level-2 data have been calibrated to the satellite's footprint at high temporal resolution, although there is often a lot of mi...

Datasets from remote-sensing platforms and sensor networks are often spatial, temporal, and very large. Processing massive amounts of data to provide current estimates of the (hidden) state from current and past data is challenging, even for the Kalman filter. A large number of spatial locations observed through time can quickly lead to an overwhel...

Data associated with spatially contiguous small areas may be modeled via regression on covariates, with error terms that are either independent or are spatially dependent according to which areas are neighbors of each other. But the data may have extra components of variability due to measurement error, which a careful statistical analysis should f...

We consider the use of generalized additive models with correlated errors for analysing trends in time series. The trend is represented as a smoothing spline so that it can be extrapolated. A method is proposed for choosing the smoothing parameter. It is based on the ability to predict a short term into the future. The choice not only addresses the...

Using temporal variability to improve spatial mapping of satellite data. Canadian J. Statist., forthcoming.