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publications
A statistical approach to fast nowcasting of lightning potential fields
Published in Advances in Statistical Climatology, Meteorology and Oceanography, 2020
Recommended citation: North, J., Stanley, Z., Kleiber, W., Deierling, W., Gilleland, E., & Steiner, M. (2020). A statistical approach to fast nowcasting of lightning potential fields. Advances in Statistical Climatology, Meteorology and Oceanography, 6(2), 79-90. https://ascmo.copernicus.org/articles/6/79/2020/
On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics
Published in Environmetrics, 2020
Recommended citation: North, J. S., Schliep, E. M., & Wikle, C. K. (2020). On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics. Environmetrics, e2665. https://onlinelibrary.wiley.com/doi/10.1002/env.2665
A Bayesian approach for data-driven dynamic equation discovery
Published in Journal of Agricultural, Biological, and Environmental Statistics, 2022
Recommended citation: North, J. S., Wikle, C. K., & Schliep, E. M. (2022). “A Bayesian approach for data-driven dynamic equation discovery”. Journal of Agricultural, Biological, and Environmental Statistics, 1 (1), 1–28. https://doi.org/10.1007/s13253-022-00514-1 https://doi.org/10.1007/s13253-022-00514-1
talks
On the Spatial and Temporal Shift in the Archetypal Seasonal Temperature Cycle as Driven by Annual and Semi-Annual Harmonics
Published:
Abstract
Statistical methods are required to evaluate and quantify the uncertainty in environmental processes, such as temperature, in a changing climate. Typically, annual harmonics are used to characterize the variation in the seasonal temperature cycle, overlooking the semi-annual harmonic, which can account for a significant portion of the variance of the seasonal cycle. Together, the spatial variation in the annual and semi-annual harmonics can play an important role in driving processes that are tied to seasonality. We propose a multivariate spatio-temporal model to quantify the spatial and temporal change in minimum and maximum temperature seasonal cycles as a function of the annual and semi-annual harmonics. Our approach captures spatial dependence, temporal dynamics, and multivariate dependence of these harmonics through spatially and temporally-varying coefficients. We apply the model to minimum and maximum temperature over North America for the years 1979 to 2018. Model inference within the Bayesian paradigm enables the identification of regions experiencing significant changes in seasonal temperature cycles due to the relative effects of changes in the two harmonics.
Data-Driven Approach to Nonlinear Dynamic Equation Discovery
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Abstract
Many real-world atmospheric, ecological, and economic processes are governed by complex, non-linear, interactions, and differential equations are commonly used to approximate the dynamics of these complex systems. While the approximating differential equations generally capture the dynamics of the system well, they impose a rigid modeling structure that assumes the dynamics of the system. Recently, there has been work in the applied math and computer science community to use a data-driven approach to learn the dynamics that govern complex systems. Here, we present a Bayesian data-driven approach to non-linear dynamic equation discovery that is robust to measurement noise and stochastic forcing. We show the effectiveness of our method on simulated systems and then apply the method to a real-world environmental process.
A Bayesian Approach for Data-Driven Dynamic Equation Discovery
Published:
Abstract
Many real-world geophysical, ecological, and biological processes are governed by complex nonlinear interactions, and differential equations are commonly used to explain the dynamics of these complex systems. While the differential equations generally capture the dynamics of the system well, they impose a rigid modeling structure that assumes the dynamics of the system are known. In many complex systems we may know some dynamical relationships a priori, but not the form of the governing equations. Discovering the form of the governing equations can lead to a better understanding of these complex systems and the interactions within. Here, we present a Bayesian data-driven approach to nonlinear dynamic equation discovery that can accommodate measurement noise and missing data. We show the effectiveness of our method on simulated data and apply the method to real-world processes.
teaching
Past Courses
Undergraduate/Graduate Course, University of Missouri Columbia, Statistics, 2017
Past courses I have taught.
Methods In Sports Analytics II
Undergraduate/Graduate Course, University of Missouri Columbia, Statistics, 2021
Advanced course in methods for analyzing individual and team based performance in sports and the use of data to drive strategy and tactics. Emphasis will be put on analytical methods to improve skills and optimize the performance of athletes.
Methods In Sports Analytics I
Undergraduate/Graduate Course, University of Missouri Columbia, Statistics, 2021
Introductory course on collecting, processing, visualizing, and analyzing data in sports. Technologies used in data collection and processing will be explored, along with methods for measuring and comparing individual and team performance.