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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantifying the impact of climate change on erosion - 20397

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha (3,338-ac) site located approximately 48 km (30 mi) south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (metric tons, or 705 short tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Erosion can play an important role in the fate and transport of waste at sites where disposal of long-lived waste is anticipated. The statistical characterization of key processes related to erosion is essential to the understanding of site stability through time. One of the key processes governing erosion is extreme precipitation and it is critical that trends in the distribution of extreme precipitation events through time be represented. The evidence of climate change is increasingly well documented and projected impacts on extreme precipitation events should be incorporated in performance assessment studies when relevant. Not evaluating future climate states in a performance assessment is contradictory to good modeling practice. Specifically, excluding climate change limits development of modeling information that could aid in effective decision making. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. In this work, this information was used to assess the impacts of potential changes in climate on erosion processes. The goal was to understand how projections of future climate relate to the performance of the WNYNSC through time. In the first stage of this work multi-temporal historical aerial images were analyzed in conjunction with orthophotography and Lidar data to develop probability distributions for variables representing important erosion processes. The information from these analyses was then used in conjunction with simulated data from the West Valley Erosion Working Group (EWG). Analysis of EWG simulations provides estimates for the change in erosion rates through time that is driven by changes in climate. The time-varying rates of erosion change were applied to the historical aerial imagery data in order to inform time-varying rates of erosion that are driven by changes in climate. Ultimately, this process resulted in the identification of locations for features like gully heads using both the Lidar dataset and projection of the estimated location from the historical aerial photo under consideration back to the Lidar dataset. The distance between the estimate of the location from the historical image and that of the Lidar dataset was the estimated distance the feature has moved. This distance was then divided by the number of years between the Lidar dataset and the year of the historical aerial image of interest to get a rate of movement through time. This was done for several dozen points on each historical aerial image. The analyses from the EWG were used to characterize the relative impact of climate change on the erosion rates. This relative impact was quantified by comparing the LEM based estimates of erosion that were derived using historical climate data with LEM based estimates of erosion that were derived using projections of future climate. The relative increase in the erosion rates was then applied to the historical estimates of erosion derived from the analysis of the historical aerial imagery. This approach was used since the LEM-based estimated of the historical erosion rates from the past century have a bias towards underestimating erosion. This underestimation is hypothesized to be a consequence of an inability of the LEMs to account for the impacts of land-use change (i.e. deforestation) which had been shown to significantly increase erosion in similar Northern hardwood forest ecosystems. In summary, gully head retreat rate and gully widening rate were characterized using statistical probability distributions from the historical aerial image analysis. Simulated data from the EWG was used to estimate the climatically-driven changes in erosion rates through time. The changes through time from the EWG were applied to the gully head retreat rate and gully widening rate characterized using the historical aerial image analysis. This information can be used to inform PPA models to project future risks from a given site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Forecasting Multi-Wave Epidemics Through Bayesian Inference

We present a simple, near-real-time Bayesian method to infer and forecast a multiwave outbreak, and demonstrate it on the COVID-19 pandemic. The approach uses timely epidemiological data that has been widely available for COVID-19. It provides short-term forecasts of the outbreak’s evolution, which can then be used for medical resource planning. The method postulates one- and multiwave infection models, which are convolved with the incubation-period distribution to yield competing disease models. The disease models’ parameters are estimated via Markov chain Monte Carlo sampling and information-theoretic criteria are used to select between them for use in forecasting. The method is demonstrated on two- and three-wave COVID-19 outbreaks in California, New Mexico and Florida, as observed during Summer-Winter 2020. We find that the method is robust to noise, provides useful forecasts (along with uncertainty bounds) and that it reliably detected when the initial single-wave COVID-19 outbreaks transformed into successive surges as containment efforts in these states failed by the end of Spring 2020.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A multi-scale temperature-based strategy to map hydrologic exchange flows in highly dynamic systems

Mapping and quantifying hydrologic exchange flows (HEFs) is critical to environmental monitoring and remediation at contaminated sites; however, these objectives are challenging in highly dynamic systems, e.g., along dam-regulated rivers, where HEFs vary rapidly. Direct seepage measurements are labor-intensive and difficult to automate, whereas indirect (e.g., thermal) and remote sensing methods have potential to allow continuous monitoring with limited field effort. We present a preliminary assessment of a multi-scale temperature-based strategy for monitoring HEFs along the Hanford Reach of the Columbia River, in eastern WA, United States. Five thermal methods were assessed. First, a vertical temperature profile (VTP) was installed into the streambed. The VTP data were analyzed using a data assimilation algorithm designed for automated real-time estimation in dynamic systems. Second, a thermal infrared (TIR) camera was used in roving surveys to identify seeps. Third, a TIR camera was stationed at the VTP site to collect images at 1-h intervals. Together, the two TIR datasets provided a basis to assess the potential for drone-based TIR. Fourth, temperature was measured at the sediment/water interface to assess fiber-optic distributed temperature sensing. Fifth, imagery from the ECOSTRESS satellite mission was acquired to assess the potential of spaceborne thermal monitoring. Based on our preliminary assessment, VTP, TIR, and bed temperature measurements provide complementary spatial coverage, temporal sampling, and resolution; these methods have potential for long-term, automated monitoring of HEFs. The publicly available spaceborne imagery, however, proved inadequate because of insufficient spatial resolution and data gaps resulting from cloud cover and revisit frequency.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Modeling Atmospheric CO2 Processes to Constrain the Missing Sink

We report on a NASA supported modeling effort to reduce uncertainty in carbon cycle processes that create the so-called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. The major components of this effort are: 1) Continued development of the chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes; 2) An advanced biosphere model, constrained by remote sensing data, coupled to the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability; 3) Improved remote sensing estimates for biomass burning emission fluxes to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source; 4) Evaluating the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Testing the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments. The results will help to prepare for the use of OCO and other satellite data in a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbodclimate interactions.

Kawa, S. R.↗
NASA NTRS2018

Earthshine as an Illumination Source at the Moon

Earthshine is the dominant source of natural illumination on the surface of the Moon during lunar night, and at some locations within permanently shadowed regions (PSRs) near the poles that never receive direct sunlight. As such, earthshine has the potential to enable the scientific investigation and exploration of conditions in areas of the Moon that are either temporarily or permanently hidden from the Sun. Earthshine has also been used to refer to Earthlight reflected from the lunar surface, but in this study we use it to refer specifically to Earthlight incident at the Moon. Under certain circumstances, the heat flux from earthshine could also influence the transport and cold-trapping of volatiles present in the very coldest areas within PSRs. In this study, Earth's spectral irradiance, as it would appear at the Moon in the solar reflectance band (0.3–3.0 µm) and at thermal emission wavelengths (3–50 µm), is examined with a suite of model image cubes and whole-disk spectra created using the Virtual Planetary Laboratory (VPL) three-dimensional (latitude, longitude and altitude) modeling capability. At the Moon, the broadband, hemispherical irradiance from Earth at full-phase is approximately 0.15 W m−2 with comparable contributions from solar reflectance and thermal emission; for context, this about 0.01% that of solar irradiance and has an equivalent temperature of around 40 K. Over the simulated timeframe, spanning two lunations, Earth's thermal irradiance shows very little net change (less than a few mW m−2 resulting from cloud variability and the south-to-north motion of the sub-observer latitude on Earth). In the solar band, Earth's diurnally averaged light curve at phase angles g ≤ 60° is well-fit using a Henyey–Greenstein integral phase function. At wavelengths longward of about 0.7 µm, near the well-known vegetation “red edge”, Earth's reflected solar radiance shows significant diurnal modulation as a result of the broad maximum in projected landmass at terrestrial longitudes between 60°W and 0°, as well as from the distribution of clouds. A simple formulation with adjustable coefficients is presented, condensed from the VPL model grid, for estimating Earth's hemispherical irradiance at the Moon as a function of wavelength, phase angle and sub-observer coordinates (terrestrial latitude and longitude). Uncertainties in any one prediction are estimated to be 10–12% at 0.3 µm, rising to >25% near 2.5 µm as a result of the increasing relative brightness and unpredictable influence of clouds. Although coefficient values are derived from a suite of spring equinox models, the approximation appears to be valid for all seasons, to within the stated uncertainties. It is demonstrated that earthshine is sufficiently bright to serve as a natural illumination source for optical measurements on a robotic lander/rover, allowing the identification of water ice mixed with regolith at the percent-level of fractional area.

Surface↗
NASA NTRS2020

Larger Drought and Flood Hazards and Adverse Impacts on Population and Economic Productivity Under 2.0 than 1.5°C Warming

Climate change may have major influences on surface runoff, which would consequently result in important implications for terrestrial ecosystems and human well‐being. At global scale there is limited understanding of these issues with respect to the warming targets stipulated in the Paris Agreement. Here we use a well‐established hydrological model (Variable Infiltration Capacity [VIC]) forced with a representative ensemble of latest climate projections from four global circulation models (GCMs) to estimate potential future changes in runoff and Terrestrial Ecosystem Water Retention (TEWR), as well as changes in extreme runoff and their impacts on population, and overall gross domestic product (GDP) worldwide. Results suggest that annual runoff generally would have larger increases, while annual TEWR generally would have larger decreases under the 2.0°C warming scenario as opposed to 1.5°C warming scenario. Global mean warming of 2°C versus 1.5°C would lead to more distinct spatial patterns in runoff change, with a general shift of the runoff distribution towards more extreme low runoff in Mexico, western United States, Western Europe, southeastern China, West Siberian Plain and more extreme high runoff in Alaska, northern Canada, and large parts of Asia. More people and GDP would be exposed to extreme low runoff decrease, extreme high runoff increase, extreme low runoff decrease as well as extreme high runoff increase under a higher warming scenario. This study differentiates hydrological impacts between the two warming scenarios and illustrates higher runoff, lower TEWR, larger potential drought and flood hazards and adverse impacts on population and GDP under 2°C than 1.5°C.

Paris Agreement↗
NASA NTRS2023

Extensive Global Wetland Loss Over the Past Three Centuries

Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity. Nevertheless, the global extent of natural wetland loss remains remarkably uncertain. Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km 2 (confidence interval 2.9–3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16–23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration.

Etienne Fluet-Chouinard↗
DOE OSTI2026

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation

Deep learning has exhibited remarkable results across diverse areas. To understand its success, substantial research has been directed towards its theoretical foundations. Nev- ertheless, the majority of these studies examine how well deep neural networks can model functions with uniform regularities. In this paper, we explore a different angle: how deep neural networks can adapt to varying degrees of smoothness in functions and nonuni- form data distributions across different locations and scales. More precisely, we focus on a broad class of functions defined by nonlinear tree-based approximation methods. This class encompasses a range of function types, such as functions with uniform regularities and discontinuous functions. We develop nonparametric approximation and estimation theories for this class using deep ReLU networks. Our results show that deep neural networks are adaptive to the nonuniform smoothness of functions and nonuniform data distributions at different locations and scales. We apply our results to several function classes, and derive the corresponding approximation and generalization errors. The validity of our results is demonstrated through numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Correlating hidden local structural distortion with antiferroelectric-ferroelectric transition in PbZrO 3 -based perovskites

Externally stimulated antiferroelectric-ferroelectric (AFE-FE) transition makes AFE materials attractive for many applications ranging from energy storage to sensing. This transition-mediated macroscopic electrical characteristics have been extensively investigated, yet the underlying structural evolution remains poorly understood. Herein, we study the local- and long-range structural evolutions in the Nb-doped Pb(Zr,Sn,Ti)O 3 perovskites by in-situ X-ray total scattering and diffraction, which show three different types of AFE-FE transitions. The evolution of long-range structure from diffraction and local structure identified by atomic pair distribution function (PDF) are consistent well with the phase transition behavior indicated by macroscopic polarization/strain measurements, while discrepancies exist. Interestingly, frequently employed long-range structure picture that the uniform AFE pseudo-tetragonal to FE rhombohedral transformation is insufficient to understand the distinct composition-dependent AFE-FE transition behaviours. Instead, the reversibility of this transition correlates with the chemically sensitive local atomic displacements revealed by newly developed in-situ PDF. The critical electric field of transition is associated with the local structural differences between the AFE and FE state. The macroscopic electrostrains can be well estimated by the phase transition associated lattice strain and domain alignment from in-situ synchrotron XRD data, while the local strains calculated from in-situ PDFs are found to be much lower. Finally, these results provide a fresh insight into the AFE-FE transition, and advance the understanding of structure-property relationships in AFE materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scaling hyporheic nitrogen cycling in large river alluvial aquifers

Research Element 1: Denitrification is a difficult flux to measure when fluxes are low and when the ecosystem is difficult to sample. These attributes describe Nyack floodplain, a large gravel-bedded aquifer adjacent to the Middle Fork Flathead River. Given that denitrification is simply respiration with nitrate as a terminal electron acceptor, we reasoned that N 2 production should negatively covary with oxygen respiration so long that oxygen is available. We sampled 16 wells 8 times throughout a year and analyzed these water samples on a membrane-inlet mass spectrometer for N 2 :Ar and O 2 . We fit multilevel models to N 2 :Ar as a function of dissolved oxygen. Within wells, there was no relationship between O 2 and N 2 :Ar. Among wells N 2 :Ar increased as O 2 declined. Based on the slope of this relationship, ~ -0.07, and estimated oxygen respiration in Nyack derived from prior studies, a possible range for denitrification is 0-8 mmol m -2 d -1 corresponding to a turnover rate for nitrate of 0-0.2/d. This estimated turnover rate is in the lower 20% of rates measured in the LINX 2 study, yet shows that even in a low nitrate (0.005 mmol/L) aquifer denitrification can be a substantial sink for dissolved N. Research Element 3 and 4: We developed a new theoretical approach to describe the relationship between water inflow, water outflow, and water age in porous media control volumes under dynamic flow conditions. Our approach had three distinct steps. First we described the application of well-established equations borrowed from Chemical Engineering to describe water age distributions in the hyporheic zone under steady state conditions. We published the associated results in the scientific journal PLOS ONE. Second, we developed a new theory ("flow spans") to describe how predicted steady-state water age distributions will respond to time varying imbalances in hyporheic discharge and recharge. Finally, we developed a proof-of-concept simulation model based on the new theory and used the model to examine the effects of flow regulation on a simulated river with characteristics representative of western U.S. montane rivers with expansive hyporheic zones.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

Time-dependent approximation schemes for some problems of parameter estimation in distributed systems

A parameter estimation method that can be used to estimate functional parameters in delay differential equations and moving boundary problems is discussed. In either problem, the original model equation (which is infinite-dimensional) is approximated by a system of ordinary differential equations that can be solved numerically in an efficient way. The approximation scheme is based on time-dependent spline elements. For the delay equation with time-varying delay, convergence results are presented that indicate the estimates obtained using the approximating system. Numerical test examples converge in some sense to a best-fit parameter for the original system are included by means of which time-varying and state-dependent delays and a time-varying diffusion coefficient in a one-phase, one-dimensional Stefan problem are estimated.

Murphy, K. A.↗
DOE OSTI2026

Spatiotemporal and Statistical Mapping of Transition Metal Equilibria in Alkaline Media

Transition metal dissolution and redeposition (D/R) kinetics in alkaline media play a critical role in various chemical and electrochemical processes. Competitive reaction kinetics between different transition metals can modulate individual metal behavior in these processes. To date, these phenomena have remained largely unmeasured, and even when captured, they are difficult to statistically characterize due to their dynamic nature, simultaneous occurrence, and spatially heterogeneous nature. Here, in this study, we develop a statistical analysis framework based on in situ and operando X-ray fluorescence microscopy (XFM) to investigate the relative D/R kinetics of multiple transition metals in alkaline media. By employing statistical analysis, we quantify the spatial distribution of D/R species and assess the rate at which the system reaches equilibrium under varying reaction conditions. We show that pH does not simply change the rate of dissolution and redeposition, but reorganizes the cross-element kinetic correlations among Ni, Fe, and Mn and accelerates the spatial equilibration of D/R events, as quantified through correlation analysis, reaction-rate estimation, probability function distributions, and texture-based monitoring statistics. Additionally, we demonstrate how modifying the solvent environment can influence D/R kinetics, providing a pathway for tuning materials synthesis and process optimization. Our study offers valuable insights into the complex interplay between different transition metals and provides a reliable statistical framework for spatial analysis of diverse imaging data sets, enabling deeper extraction of latent information across multiple modalities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Simulation-Based Inference for Neutrino Interaction Model Tuning

This project demonstrates, for the first time, the application of simulation-based inference (SBI) techniques to tune neutrino–nucleus interaction models. Using a mock dataset based on the MicroBooNE tuning of the GENIE event generator, our approach employs a Neural Posterior Estimator (NPE) with Masked Autoregressive Flows (MAF) to infer the posterior distributions of key GENIE parameters directly from simulated histograms. The workflow provides a scalable and amortized framework for performing likelihood-free inference in high-dimensional parameter spaces, offering a pathway to more efficient and uncertainty-aware model tuning for next-generation neutrino experiments such as DUNE and SBND.

Tame-Narvaez, KarlaMaria [Fermi National Accelerat↗
DOE OSTI2022

Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs

Rainfall is arguably the most important yet most variable input for rainfall-runoff hydrologic models. In this study, the authors search for the characteristics of radar-rainfall estimates that are most important for skillful streamflow predictions. They perform comprehensive hydrologic investigations of radar-rainfall characteristics, including spatiotemporal resolution, radar range visibility, statistical characterization of rainfall variability, all vis-a-vis basin characteristics such as size and river network topology. Since the true rainfall fields are unknown, the authors exploit a paradigm of using two independently constructed radar-rainfall products i.e., Multi-Radar Multi-Sensor and IFC-ZR used operationally by the Iowa Flood Center (IFC). Using the distributed hydrologic model called the Hillslope-Link Model for the domain of the state of Iowa, they evaluate streamflow prediction at 140 USGS gauge stations that monitor rivers in Iowa. Through spatial and temporal rainfall aggregation experiments, the authors show that the impact of spatial and temporal resolution of rainfall is significant typically for smaller basins while starts reducing significantly for basins larger than 1,000 km 2 . Other rainfall characteristics they explored do not reveal a strong signature in the relationship of rainfall differences between the two products and hydrograph errors. However, exploring the product similarities rather than differences reveals that the basin-wide rainfall volume has the most significant effect on streamflow prediction. The results from this study are generalizable for all rainfall observing systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A novel framework for increasing research transparency: Exploring the connection between diversity and innovation

A split sample/dual method research protocol is demonstrated to increase transparency while reducing the probability of false discovery. We apply the protocol to examine whether diversity in ownership teams increases or decreases the likelihood of a firm reporting a novel innovation using data from the 2018 United States Census Bureau’s Annual Business Survey. Transparency is increased in three ways: 1) all specification testing and identifying potentially productive models is done in an exploratory subsample that 2) preserves the validity of hypothesis test statistics fromde novoestimation in the holdout confirmatory sample with 3) all findings publicly documented in an earlier registered report and in this journal publication. Bayesian estimation procedures that leverage information from the exploratory stage included in the confirmatory stage estimation replace traditional frequentist null hypothesis significance testing. In addition to increasing statistical power by using information from the full sample, Bayesian methods directly estimate a probability distribution for the magnitude of an effect, allowing much richer inference. Estimated magnitudes of diversity along academic discipline, race, ethnicity, and foreign-born status dimensions are positively associated with innovation. A maximally diverse ownership team on these dimensions would be roughly six times more likely to report new-to-market innovation than a homophilic team.

Science & Technology - Other Topics↗
DOE OSTI2020

Characterization of Uranium Foil Irradiations at the WSU TRIGA Reactor Using a New Reactor Model in SCALE [Slides]

A depleted uranium foil and a highly enriched uranium foil that were irradiated at the Washington State University TRIGA reactor were used to measure the relative production of 237 U and 239 U, two key isotopes of interest in nuclear forensics, as well as their decay daughters 237 Np and 239 Pu. Total fissions in the two foils were also estimated using 99 Mo measurement. A reactor model of the TRIGA was built in the SCALE neutron transport code using dimensions obtained from an existing MCNP geometry. The model was successfully used to simulate foil fissions and the reaction rate ratios in 238 U, using the laboratory-measured isotope ratios as a benchmark. The flux distribution in the foils was determined. Finally, using the continuous energy Tsunami-3d module of SCALE, a sensitivity/uncertainty analysis was performed to determine the effects on the reaction ratio caused by other reactions in the reactor and by uncertainties in the known cross-sections.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRSDate not supplied

Quantifying Tropopause-Overshooting Volume from Satellite and Radar Observations During the DCOTSS 2021 and 2022 Campaigns

Tropopause-overshooting cloud locations, their volume, and the percentage of anvil cloud occupied by overshooting were quantified from state-of-the-art merged NEXRAD radar and GOES-16/17 satellite products during the 2021 and 2022 Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) study periods. A novel method for defining and tracking large individual storms or storm outbreak objects in space and time was presented, which enabled improved understanding of the complex feature-specific biases among GOES- and GridRad-derived overshooting metrics. GOES overshooting cloud top height was estimated using a stratospheric lapse rate approach informed by GOES visible and IR measurements of shadows from OTs and calibrated based on previously observed maximum GridRad overshooting distribution. Total overshooting volumes derived over CONUS are higher for GOES than GridRad for both years, especially during 2021. Over all storm outbreak objects with observed overshooting, GOES observed higher overshooting percentages while GridRad observed relatively higher overshooting magnitudes. The greater spatial coverage of GOES overshooting, especially noticeable during the mature and dissipation stages of outbreaks due to inherent limitations of infrared satellite data which make it difficult to differentiate cold, high-altitude outflow from true updraft cores, outweighed differences in magnitude to yield higher GOES volume. Individual events, however, revealed that the higher overshooting magnitudes observed by GridRad produce higher overshooting volumes at times. Four-dimensional trajectories of tropopause-overshooting air parcels were compared 5 days after overshooting occurred to depict the envelope of convectively impacted air in the stratosphere. The generally good agreement in parcel plume area initiated from GOES and GridRad overshooting detections suggests that geostationary satellite data can be used to estimate where and how often overshooting impacts stratospheric composition in regions without a ground-based weather radar network to estimate climate impacts of overshooting convection.

Kristopher Bedka↗
DOE OSTI2025

Joint Modeling of Wind Speed and Wind Direction Through a Conditional Approach

Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is therefore crucial to accurately estimate the joint probability distribution of wind speed and direction. In this work, we develop a conditional approach to model these two variables, where the joint distribution is decomposed into the product of the marginal distribution of wind direction and the conditional distribution of wind speed given wind direction. To accommodate the circular nature of wind direction, a von Mises mixture model is used; the conditional wind speed distribution is modeled as a directional dependent Weibull distribution via a two-stage estimation procedure, consisting of a directional binned Weibull parameter estimation, followed by a harmonic regression to estimate the dependence of the Weibull parameters on wind direction. A Monte Carlo simulation study indicates that our method outperforms two other approaches in estimation efficiency: one that utilizes periodic spline quantile regression and another that generates data from the commonly used Abe-Ley distribution for cylindrical data. We illustrate our method by using the output from a regional climate model to investigate how the joint distribution of wind speed and direction may change under some future climate scenarios. Our method indicates significant changes in the variation of wind speed with respect to some directions.

17 WIND ENERGY↗