Drew Yarger
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View article: Elastic Changepoint Detection for Globally-indexed Functional Time Series Data with Climate Applications
Elastic Changepoint Detection for Globally-indexed Functional Time Series Data with Climate Applications Open
Changepoint detection is a vital tool in the application of climate data analysis. Numerous types of climate observation data are most properly represented by functional time series, implying a need for accurate changepoint detection metho…
View article: Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA‐Based Surrogate for Spatial Fields
Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA‐Based Surrogate for Spatial Fields Open
Global Climate Model tuning (calibration) is a tedious and time‐consuming process, with high‐dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand‐picked values of tuning parameter…
View article: Multivariate Confluent Hypergeometric Covariance Functions with Simultaneous Flexibility over Smoothness and Tail Decay
Multivariate Confluent Hypergeometric Covariance Functions with Simultaneous Flexibility over Smoothness and Tail Decay Open
Spatially-indexed multivariate data appear frequently in geostatistics and related fields including oceanography and environmental science. To take full advantage of this data structure, cross-covariance functions are constructed to descri…
View article: Elastic functional changepoint detection of climate impacts from localized sources
Elastic functional changepoint detection of climate impacts from localized sources Open
Detecting changepoints in functional data has become an important problem as interest in monitoring of climate phenomenon has increased, where the data is functional in nature. The observed data often contains both amplitude (‐axis) and ph…
View article: Multivariate Matérn Models -- A Spectral Approach
Multivariate Matérn Models -- A Spectral Approach Open
The classical Matérn model has been a staple in spatial statistics. Novel data-rich applications in environmental and physical sciences, however, call for new, flexible vector-valued spatial and space-time models. Therefore, the extension …
View article: Autocalibration of the E3SM version 2 atmosphere model using a PCA-based surrogate for spatial fields
Autocalibration of the E3SM version 2 atmosphere model using a PCA-based surrogate for spatial fields Open
Global Climate Model (GCM) tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning par…
View article: Detecting changepoints in globally-indexed functional time series
Detecting changepoints in globally-indexed functional time series Open
In environmental and climate data, there is often an interest in determining if and when changes occur in a system. Such changes may result from localized sources in space and time like a volcanic eruption or climate geoengineering events.…
View article: Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources
Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources Open
Detecting changepoints in functional data has become an important problem as interest in monitoring of climate phenomenon has increased, where the data is functional in nature. The observed data often contains both amplitude ($y$-axis) and…
View article: A functional regression model for heterogeneous BioGeoChemical Argo data in the Southern Ocean
A functional regression model for heterogeneous BioGeoChemical Argo data in the Southern Ocean Open
Leveraging available measurements of our environment can help us understand complex processes. One example is Argo Biogeochemical data, which aims to collect measurements of oxygen, nitrate, pH, and other variables at varying depths in the…
View article: A probabilistic model of ocean floats under ice
A probabilistic model of ocean floats under ice Open
The Argo project deploys thousands of floats throughout the world's oceans. Carried only by the current, these floats take measurements such as temperature and salinity at depths of up to two kilometers. These measurements are critical for…
View article: A functional-data approach to the Argo data
A functional-data approach to the Argo data Open
The Argo data is a modern oceanography dataset that provides unprecedented global coverage of temperature and salinity measurements in the upper 2,000 meters of depth of the ocean. We study the Argo data from the perspective of functional …
View article: Statistical Approaches for Spatially-Dependent Functional Data and Their Application in Oceanography
Statistical Approaches for Spatially-Dependent Functional Data and Their Application in Oceanography Open
In many scientific fields, there is interest and need in analyzing data with complex dependence structures. This work is motivated by the Argo data, a dataset of measurements of the upper 2,000 meters of the world’s oceans, which has revol…