Preprints
Filtering by Subject: Statistical Methodology
An Improved Methodology to Estimate Cross-Scale Kinetic Energy Transfers from Third-Order Structure Functions using Regularized Least-Squares
Published: 2025-03-25
Subjects: Analysis, Applied Statistics, Fluid Dynamics, Longitudinal Data Analysis and Time Series, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Other Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistical Methodology, Statistical, Nonlinear, and Soft Matter Physics
Several methods exist for estimating cross-scale kinetic energy (KE) transfers; however, they are ill-adapted for sparse ocean observations, hindering the study of oceanic KE transfers. A newly developed third-order structure function $D3(r)$ framework allows estimation of KE injection rates $\epsilon_j(k)$ and KE transfers $F(k)$ across scales using sparse data. This approach requires inverse [...]
Fingerprinting subduction margins using PCA profiles: A data science approach to assessing earthquake hazard
Published: 2025-01-21
Subjects: Earth Sciences, Statistical Methodology, Statistical Models, Tectonics and Structure
Giant earthquakes (MW ≥ 8.5) along subduction margins pose great hazards to coastal societies. While it is generally accepted that geological margin properties play a role, the controls on giant earthquake occurrence remain undetermined. Their long intermittence times and the comparatively short earthquake record obscure any correlations between margin properties and seismicity. This work [...]
Bayesian Inversion with TimeOptMCMC
Published: 2024-05-16
Subjects: Earth Sciences, Geology, Physical Sciences and Mathematics, Statistical Methodology, Stratigraphy
TimeOptMCMC (Meyers and Malinverno, 2018) is a Bayesian approach for evaluating cyclostratigraphic records that quantitatively links geologic data with astronomical theory. This manuscript provides an introductory guide for conducting a TimeOptMCMC Bayesian inversion, following the approach of Meyers and Malinverno (2018). The purpose of this guide is to help users identify an appropriate data [...]
The Potential for Fuel Reduction to Offset Climate Warming Impacts on Wildfire Intensity in California
Published: 2024-02-21
Subjects: Climate, Earth Sciences, Environmental Sciences, Forest Management, Forest Sciences, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Other Statistics and Probability, Physical Sciences and Mathematics, Probability, Statistical Methodology, Statistical Models, Statistics and Probability
Increasing fuel aridity due to climate warming has and will continue to increase wildfire danger in California. In addition to reducing global greenhouse gas emissions, one of the primary proposals for counteracting this increase in wildfire danger is a widespread expansion of hazardous fuel reductions. Here, we quantify the potential for fuel reduction to reduce wildfire intensity using [...]
Designing and describing climate change impact attribution studies: a guide to common approaches
Published: 2024-01-06
Subjects: Climate, Earth Sciences, Ecology and Evolutionary Biology, Environmental Public Health, Environmental Studies, Human Geography, Physical and Environmental Geography, Physical Sciences and Mathematics, Probability, Public Health, Spatial Science, Statistical Methodology, Statistical Models, Statistics and Probability
Impact attribution is an emerging transdisciplinary sub-discipline of detection and attribution, focused on the social, economic, and ecological impacts of climate change. Here, we provide an overview of common end-to-end frameworks in impact attribution, focusing on examples relating to the human health impacts of climate change. We propose a typology of study designs based on whether [...]
Transgression–regression cycles drive correlations in Ediacaran–Cambrian rock and fossil records
Published: 2023-11-07
Subjects: Earth Sciences, Geology, Paleontology, Physical Sciences and Mathematics, Sedimentology, Statistical Methodology, Statistics and Probability, Stratigraphy
Strata of the Ediacaran Period (635–538.8 Ma) yield the oldest known fossils of complex, macroscopic organisms in the geologic record. These “Ediacaran-type” macrofossils (known as the Ediacaran biota) first appear in mid-Ediacaran strata, experience an apparent decline through the terminal Ediacaran, and directly precede the Cambrian (538.8–485.4 Ma) radiation of animals. Existing hypotheses for [...]
Spatial standardization of taxon occurrence data—a call to action
Published: 2023-10-20
Subjects: Biodiversity, Ecology and Evolutionary Biology, Paleobiology, Paleontology, Statistical Methodology
The fossil record is spatiotemporally heterogeneous: taxon occurrence data have patchy spatial distributions, and this patchiness varies through time. Large-scale quantitative paleobiology studies that fail to account for heterogeneous sampling coverage will generate uninformative inferences at best and confidently draw wrong conclusions at worst. Explicitly spatial methods of standardization are [...]
Standardized Benchmark of Historical Compound Wind and Solar Energy Droughts Across the Continental United States
Published: 2023-06-23
Subjects: Meteorology, Oil, Gas, and Energy, Statistical Methodology, Sustainability
As we move towards a decarbonized grid, reliance on weather-dependent energy increases as does exposure to prolonged natural resource shortages known as energy droughts. Compound energy droughts occur when two or more predominant renewable energy sources simultaneously are in drought conditions. In this study we present a methodology and dataset for examining compound wind and solar energy [...]
Carbon Utilization and Storage through Rehabilitation of Groundwater Wells
Published: 2023-05-20
Subjects: Applied Statistics, Civil and Environmental Engineering, Climate, Earth Sciences, Environmental Sciences, Geochemistry, Hydrology, Longitudinal Data Analysis and Time Series, Oil, Gas, and Energy, Statistical Methodology, Statistical Models, Statistics and Probability
According to the Intergovernmental Panel on Climate Change (IPCC) of the United Nations (UN), rise in atmospheric concentration of carbon dioxide (CO2) due to anthropogenic factors is considered as the primary driver for global climate change. With almost every major corporation around the world working towards their “net-zero goals”, it is becoming increasingly important to have more [...]
Sinh-arcsinh-normal distributions to add uncertainty to neural network regression tasks: applications to tropical cyclone intensity forecasts
Published: 2022-07-15
Subjects: Artificial Intelligence and Robotics, Earth Sciences, Statistical Methodology
A simple method for adding uncertainty to neural network regression tasks in earth science via estimation of a general probability distribution is described. Specifically, we highlight the sinh-arcsinh-normal distributions as particularly well suited for neural network uncertainty estimation. The methodology supports estimation of heteroscedastic, asymmetric uncertainties by a simple modification [...]
The Lock-Down Effects of COVID-19 on the Air Pollution Indices in Iran and Its Neighbors
Published: 2022-07-02
Subjects: Applied Statistics, Environmental Public Health, Environmental Sciences, Environmental Studies, Near and Middle Eastern Studies, Other Statistics and Probability, Statistical Methodology, Statistical Models
Introduction The Covid-19 restrictions have a lot of various peripheral negative and positive effects like economic shocks and decreasing air pollution, respectively. Many studies showed NO2 reduction in most parts of the world. Method Iran and its land and maritime neighbors have about 7.4% of the world population and 6.3% and 5.8% of World COVID-19 cases and deaths, respectively. The air [...]
Robust Probabilities of Detection and Quantification Uncertainty for Aerial Methane Detection: Examples for Three Airborne Technologies
Published: 2022-06-17
Subjects: Atmospheric Sciences, Climate, Environmental Monitoring, Mechanical Engineering, Oil, Gas, and Energy, Statistical Methodology, Statistical Models
Thorough characterization of probabilities of detection (POD) and quantification uncertainties is fundamentally important to understand the place of aerial measurement technologies in alternative means of emission limitation (AMEL) or alternate fugitive emissions management programs (Alt-FEMP); monitoring, reporting, and verification (MRV) efforts; and surveys designed to support [...]
Probabilistic Assessment of Antarctic Thermomechanical Structure: Impacts on Ice Sheet Stability
Published: 2022-05-19
Subjects: Earth Sciences, Geophysics and Seismology, Mineral Physics, Physical Sciences and Mathematics, Statistical Methodology
Uncertainty in present-day glacial isostatic adjustment (GIA) rates represents at least 44% of the total gravity-based ice mass balance signal over Antarctica. Meanwhile, physical couplings between solid Earth, sea level and ice dynamics enhance the dependency of the spatiotemporally varying GIA signal on three-dimensional variations in mantle rheology. Improved knowledge of thermomechanical [...]
Reducing the Uncertainty of Multi-Well Petrophysical Interpretation from Well Logs via Machine-Learning and Statistical Models
Published: 2022-03-22
Subjects: Analysis, Earth Sciences, Engineering, Geophysics and Seismology, Mining Engineering, Multivariate Analysis, Statistical Methodology, Statistical Models
Well-log interpretation provides in situ estimates of formation properties such as porosity, hydrocarbon pore volume, and permeability. Reservoir models based on well-log-derived formation properties deliver reserve-volume estimates, production forecasts, and help with decision making in reservoir development. However, due to measurement errors, variability of well logs due to multiple [...]
Multilevel multifidelity Monte Carlo methods for assessing coastal flood risk
Published: 2022-03-14
Subjects: Applied Mathematics, Earth Sciences, Hydrology, Risk Analysis, Statistical Methodology, Statistics and Probability
When choosing an appropriate hydrodynamic model, there is always a compromise between accuracy and computational cost, with high fidelity models being more expensive than low fidelity ones. However, when assessing uncertainty, we can use a multifidelity approach to take advantage of the accuracy of high fidelity models and the computational efficiency of low fidelity models. Here, we apply the [...]