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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Automated Image Registration Using Morphological Region of Interest Feature Extraction

With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

Plaza, Antonio↗
NASA NTRS2008

The Supercritical Pile GRB Model: The Prompt to Afterglow Evolution

The 'Supercritical Pile' is a very economical gamma ray burst (GRB) model that provides for the efficient conversion of the energy stored in the protons of a Relativistic Blast Wave (RBW) into radiation and at the same time produces - in the prompt GRB phase, even in the absence of any particle acceleration - a spectral peak at an energy sim 1 MeV. We extend this model to include also the evolution of the RBW Lorentz factor Gamma and thus follow the spectral and temporal features of this model into the early GRB afterglow stage. One of the novel features of the present treatment is the inclusion of the feedback of the GRB produced radiation on the evolution of Gamma with radius. This feedback and the presence of kinematic and dynamic thresholds in the model can be the sources of rich time evolution which we have begun to explore. In particular, one can this way obtain afterglow light curves with steep decays followed by the more conventional flatter afterglow slopes, while at the same time preserving the desirable features of the model, i.e. the well defined relativistic electron source and radiative processes that produce the proper peak in the nu F spectra. Furthermore, the existence of a kinematic threshold in this model provides for a operational distinction of the prompt and afterglow GRB stages; in fact, the afterglow stage sets in when the RBW Lorentz factor cannot anymore fulfill the kinematic condition for pair formation in the photon - proton pair production reactions that constitute the fundamental process for the dissipation of the blast wave kinetic energy. We present the results of a specific set of parameters of this model with emphasis on the multiwavelength prompt emission and transition to the early afterglow.

Kazanas, Demosthenes↗
NASA NTRS2024

Exploring the ENSO modulation of the QBO periods with GISS E2.2 models

Observational studies have shown that the El Niño–Southern Oscillation (ENSO) exerts an influence on the Quasi-Biennial Oscillation (QBO). The downward propagation of the QBO tends to speed up and slow down during El Niño and La Niña, respectively. Recent results from general circulation models have indicated that the ENSO modulation of the QBO requires a relatively high horizontal resolution, and that it does not show up in the climate models with parameterized but temporally constant gravity wave sources. Here, we demonstrate that the NASA Goddard Institute for Space Studies (GISS) E2.2 models can capture the observed ENSO modulation of the QBO period with a horizontal resolution of 2 ∘ latitude by 2.5 ∘ longitude but with its gravity wave sources being parameterized interactively. This is because El Niño events lead to more vigorous gravity wave sources generating more absolute momentum fluxes over the equatorial belt, as well as less filtering of these waves into the tropical lower stratosphere through a weakening of the Walker circulation. Various components of the ENSO system, such as the sea surface temperatures, the convective activities, and the Walker circulation, are intimately involved in the generation and propagation of parameterized gravity waves, through which ENSO modulates the QBO period in GISS E2.2 models.

El Niño–Southern Oscillation↗
NASA NTRS2009

Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Model Performance in a Real-time Air Quality Estimation System

Aerosol optical depth (AOD), an indirect estimate of particle matter using satellite observations, has shown great promise in improving estimates of PM 2.5 air quality surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM 2.5 surface estimation in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) the approach to integrate satellite measurements with ground measurements in the pollution estimation, and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect on the identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale of within the last 30 days displays the best model performance in this study area using 2004 and 2005 data sets.

Li, Hui↗
NASA NTRS2006

The Supercritical Pile Model for GRBs

We present the spectral and temporal radiative signatures expected within the Supercritical Pile model of Gamma Ray Bursts (GRB). This model is motivated by the need for a process that provides the dissipation necessary in GRB and presents a well defined scheme for converting the energy stored in the relativistic protons of the Relativistic Blast Waves (RBW) associated with GRB into radiation; at the same time it leads to spectra which exhibit a peak in the burst nuF(sub nu) distribution at an energy E(sub p) approximately equal to 1 MeV in the observer s frame, in agreement with observation and largely independent of the Lorentz factor GAMMA of the associated relativistic outflow. Furthermore, this scheme does not require (but does not preclude) acceleration of particles at the shock other than that provided by the isotropization of the flow bulk kinetic energy on the RBW frame. In the present paper we model in detail the evolution of protons, electrons and photons from a RBW to produce detailed spectra of the prompt GRB phase as a function of time from across a very broad range spanning roughly 4 log10 GAMMA decades in frequency. The model spectra are in general agreement with observations and provide a means for the delineating of the model parameters through direct comparison with trends observed in GRB properties.

Kazanas, Demos↗
NASA NTRS2005

The Supercritical Pile Model for GRBs

We present the spectral and temporal radiative signatures expected within the "Supercritical Pile" model of Gamma Ray Bursts (GRB). This model is motivated by the need for a process that provides the dissipation necessary in GRB and presents a well defined scheme for converting the energy stored in the relativistic protons of the Relativistic Blast Waves (RBW) associated with GRB into radiation; at the same time it leads to spectra which exhibit a peak in the burst nuF(sub nu) distribution at an energy E(sub p) approx. equal to 1 MeV in the observer s frame, in agreement with observation and largely independent of the Lorentz factor r of the associated relativistic outflow. Futhermore, this scheme does not require (but does not preclude) acceleration of particles at the shock other than that provided by the isotropization of the flow bulk kinetic energy on the RBW frame. In the present paper we model in detail the evolution of protons, electrons and photons from a RBW to produce detailed spectra of the prompt GRB phase as a function of time from across a very broad range spanning roughly 4 log10Gamma decades in frequency. The model spectra are in general agreement with observations and provide a means for the delineating of the model parameters through direct comparison with trends observed in GRB properties.

Kazanas, D.↗
DOE OSTI2013

Characterizing particle-scale equilibrium adsorption and kinetics of uranium(VI) desorption from U-contaminated sediments

Rates of contaminant U(VI) release from individual size fractions of a composite sediment from the seasonally saturated lower vadose zone of the Hanford 300-Area were examined in flow-through batch reactors to maintain quasi-constant chemical conditions. Variability in equilibrium adsorption among the various size fractions was determined in static batch reactors and analyzed using the surface complexation modeling approach. The estimated stoichiometric coefficients of U(VI) surface complexation reactions with respect to pH and carbonate concentrations varied with size fractions. This source of variability significantly increased the uncertainty in U(VI) conditional equilibrium constants over that estimated from experimental errors alone. A minimum difference between conditional equilibrium constants was established in order to evaluate statistically significant differences between sediment adsorption properties. A set of equilibrium and kinetic expressions for cation exchange, calcite dissolution, aerobic respiration, and silica dissolution were incorporated in a reaction-rate model to describe the temporal evolution of solute concentrations observed during the flow-through batch experiments. Parameters in the reaction-rate model, calibrated using experimental data for select size fractions, predicted the changes in solute concentrations for the bulk, <2 mm, sediment sample. Kinetic U(VI) desorption was well described using a multi-rate surface complexation model with an assumed lognormal distribution for the rate constants. The estimated mean and standard deviation were the same for all < 2mm size fractions, but differed in the 2-8mm size fraction. Micropore volumes in the varied size fractions were also similar as assessed using t-plots to analyze N2 desorption data. These findings provide further support for the link between microporosity and particle-scale mass transfer rates controlling kinetic U(VI) adsorption/desorption and for the utility of N2 desorption isotherms for characterizing pore networks that influence mass transfer rates.

Stoliker, Deborah L.↗
DOE OSTI2015

Utilization of GIS technology to support development of flow and contaminant fate and transport models at US DOE sites - 15457

U.S. Department of Energy (DOE) sites have accumulated years of data and millions of spatial and temporal records related to the hydrological cycle, contaminant transport, and parameters of remediation technologies. In addition, numerical models which use field monitoring data to help understand flow and transport produce gigabytes of computed spatial and temporal data for each computation node. There is a need therefore for an advanced spatial data structure that can be used to address the management, processing, and analysis of spatial and temporal numerical modeling data derived from multiple sources. This can be accomplished using a geo-database. The development of an ArcSDE geo-database facilitates the centralized storage, backup, accessibility, organization, and management of model configuration and computed simulation data, putting it into a structured, coherent, and logical computer-supported system. GIS data processing and automation would enable faster and hence more complex analyses of field test data. The Applied Research Center (ARC) at Florida International University (FIU) has provided analysis of flow and transport for several watersheds at DOE's Oak Ridge Reservation (ORR), including East Fork Poplar Creek (EFPC), Upper EFPC in the Y-12 National Security Complex area (Y-12 NSC) and White Oak Creek (WOC). Integrated surface and subsurface flow, fate and transport models were developed to provide analysis of contaminant patterns within each watershed. In addition, digital monitoring data available from the Oak Ridge Environmental Information System (OREIS) related to mercury (Hg) contamination and remediation within these watersheds was used for calibration and verification of the model. Experimental studies were also carried out which provided kinetic and equilibrium data about important parameters related to Hg transport, speciation and methylation/demethylation kinetics within the watershed. Geographic Information Systems (GIS) technology was employed to support the modeling work through storage and geo-processing of spatial and temporal data required by the models and to produce hydrogeological maps for visualization. An ArcGIS geo-database was developed for centralized storage and management of experimental and computed model data and its capabilities were extended over the years using tools such as ModelBuilder combined with Python scripting to automate repetitive tasks, perform statistical analyses and generate maps and reports. The hydrologic geo-database model developed possesses a structure that enables linkage with scalable hydrologic modeling tools/applications to model hydrologic systems, and in this case, enables the testing of the potential impacts of various remediation scenarios on the ORR watersheds. The ArcSDE geo-database can also be used to automate and simplify the process of calling stored GIS and time series data. This geo-database can serve as a tool for contaminant flow and transport analyses which require large amounts of high-quality spatial and temporal data in order to ensure reliability and validity of modeling results. The existing geo-database structure developed for the hydrological modeling at ORR was designed to be replicable for application at other DOE sites. An investigation of downloadable free/open source GIS software along with required security protocols to facilitate online querying of the database was also conducted to determine methods by which project-derived data can be more easily shared with other project stakeholders such as DOE personnel and site contractors. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2004

PARAGON: A Systematic, Integrated Approach to Aerosol Observation and Modeling

Aerosols are generated and transformed by myriad processes operating across many spatial and temporal scales. Evaluation of climate models and their sensitivity to changes, such as in greenhouse gas abundances, requires quantifying natural and anthropogenic aerosol forcings and accounting for other critical factors, such as cloud feedbacks. High accuracy is required to provide sufficient sensitivity to perturbations, separate anthropogenic from natural influences, and develop confidence in inputs used to support policy decisions. Although many relevant data sources exist, the aerosol research community does not currently have the means to combine these diverse inputs into an integrated data set for maximum scientific benefit. Bridging observational gaps, adapting to evolving measurements, and establishing rigorous protocols for evaluating models are necessary, while simultaneously maintaining consistent, well understood accuracies. The Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) concept represents a systematic, integrated approach to global aerosol Characterization, bringing together modern measurement and modeling techniques, geospatial statistics methodologies, and high-performance information technologies to provide the machinery necessary for achieving a comprehensive understanding of how aerosol physical, chemical, and radiative processes impact the Earth system. We outline a framework for integrating and interpreting observations and models and establishing an accurate, consistent and cohesive long-term data record.

PARAGON↗
NASA NTRS2024

Evaluation of Machine Learning and Deep Learning Algorithms for Fire Prediction in Southeast Asia

Vegetation fires are prevalent in South/Southeast Asian countries, making fire prediction crucial due to their potential environmental, economic, and social impacts. Accurate predictions of fires facilitate timely interventions, helping to mitigate uncontrolled fires that can lead to biodiversity loss and air quality issues. In this study, we utilize VIIRS satellite-derived fire data alongside six machine learning and deep learning models—Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM—to determine the most effective fire prediction model, using Root Mean Square Error (RMSE) as the metric. Our results indicate that the CNN model is the most reliable in regions with spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM model excels in countries with complex spatiotemporal dynamics like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models like Persistence and MLP showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating models before implementing any decision support systems (DSS) in fire management. By tailoring models to specific regional fire data, we can enhance prediction accuracy and responsiveness, ultimately improving fire risk management in Southeast Asia and beyond.

Deep learning↗
NASA NTRS2017

Daily Ambient Air Pollution Metrics for Five Cities: Evaluation of Data Fusion-Based Estimates and Uncertainties

Spatiotemporal characterization of ambient air pollutant concentrations is increasingly relying on the combination of observations and air quality models to provide well-constrained, spatially and temporally complete pollutant concentration fields. Air quality models, in particular, are attractive, as they characterize the emissions, meteorological, and physiochemical process linkages explicitly while providing continuous spatial structure. However, such modeling is computationally intensive and has biases. The limitations of spatially sparse and temporally incomplete observations can be overcome by blending the data with estimates from a physically and chemically coherent model, driven by emissions and meteorological inputs. We recently developed a data fusion method that blends ambient ground observations and chemical transport-modeled (CTM) data to estimate daily, spatially resolved pollutant concentrations and associatedcorrelations. In this study, we assess the ability of the data fusion method to produce daily metrics (i.e., 1-hr max, 8-hr max, and 24-hr average) of ambient air pollution that capture spatiotemporal air pollution trends for 12 pollutants (CO, NO2, NOx, O3, SO2, PM (sub10), PM (sub 2.5), and five PM (sub 2.5) components) across five metropolitan areas (Atlanta, Birmingham, Dallas, Pittsburgh, and St. Louis), from 2002 to 2008. Three sets of comparisons are performed: (1) the CTM concentrations are evaluated for each pollutant and metropolitan domain, (2) the data fusion concentrations are compared with the monitor data, (3) a comprehensive cross-validation analysis against observed data evaluates the quality of the data fusion model simulations across multiple metropolitan domains. The resulting daily spatial field estimates of air pollutant concentrations and uncertainties are not only consistent with observations, emissions, andmeteorology, but substantially improve CTM-derived results for nearly all pollutants and all cities, with the exception of NO2 for Birmingham. The greatest improvements occur for O3 and PM (sub 2.5). Squared spatiotemporal correlation coefficients range between simulations and observations determined using cross-validation across all cities for air pollutants of secondary and mixed origins are R-squared equal to 0.88-0.93 (O3), 0.81-0.89 (SO4), 0.67-0.83 (PM (sub 2.5)), 0.52-0.72 (NO3), 0.43-0.80 (NH4), 0.32-0.51 (OC), and 0.14-0.71 (PM (sub 10)). Results for relatively homogeneous pollutants of secondary origin, tend to be better than those for more spatially heterogeneous (larger spatial gradients) pollutants of primary origin (NOx, CO, SO2 and EC). Generally, background concentrations and spatial concentration gradients reflect interurban airshed complexity and the effects of regional transport, whereas daily spatial pattern variability shows intra urban consistency in the fused data. With sufficiently high CTM spatial resolution, traffic-related pollutants exhibit gradual concentration gradients that peak toward the urban centers. Ambient pollutant concentration uncertainty estimates for the fused data are both more accurate and smaller than those for either the observations or the model simulations alone.

spatiotemporal fusion↗
DOE OSTI2023

Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution

Abstract Estimating irrigation water use accurately is critical for sustainable irrigation and studying terrestrial water cycle in irrigated croplands. However, irrigation is not monitored in most places, and current estimations of irrigation water use has coarse spatial and/or temporal resolutions. This study aims to estimate irrigation water use at the daily and field scale through the proposed model‐data fusion framework, which is achieved by particle filtering with two configurations (concurrent, CON, and sequential, SEQ) by assimilating satellite‐based evapotranspiration (ET) observations into an advanced agroecosystem model, ecosys . Two types of experiments using synthetic and real ET observations were conducted to study the efficacy of the proposed framework for estimating irrigation water use at the irrigated fields in eastern and western Nebraska, United States. The experiments using synthetic ET observations indicated that, for two major sources of uncertainties of ET difference between observations and model simulations, which are bias and noise, noise had larger impacts on degrading the estimation performance of irrigation water use than bias. For the experiments using real ET observations, monthly and annual estimations of irrigation water use matched well with farmer irrigation records, with Pearson correlation coefficient ( r ) around 0.80 and 0.50, respectively. Although detecting daily irrigation records was very challenging, our method still gave a good performance with RMSE, BIAS, and r around 2.90, 0.03, and 0.4 mm/d, respectively. Our proposed model‐data fusion framework for estimating irrigation water use at high spatio‐temporal resolution could contribute to regional water management, sustainable irrigation, and better tracking terrestrial water cycle.

Environmental Sciences & Ecology↗
DOE OSTI2013

Hiereachical Bayesian Model for Combining Geochemical and Geophysical Data for Environmental Applications Software

Development of a hierarchical Bayesian model to estimate the spatiotemporal distribution of aqueous geochemical parameters associated with in-situ bioremediation using surface spectral induced polarization (SIP) data and borehole geochemical measurements collected during a bioremediation experiment at a uranium-contaminated site near Rifle, Colorado. The SIP data are first inverted for Cole-Cole parameters including chargeability, time constant, resistivity at the DC frequency and dependence factor, at each pixel of two-dimensional grids using a previously developed stochastic method. Correlations between the inverted Cole-Cole parameters and the wellbore-based groundwater chemistry measurements indicative of key metabolic processes within the aquifer (e.g. ferrous iron, sulfate, uranium) were established and used as a basis for petrophysical model development. The developed Bayesian model consists of three levels of statistical sub-models: 1) data model, providing links between geochemical and geophysical attributes, 2) process model, describing the spatial and temporal variability of geochemical properties in the subsurface system, and 3) parameter model, describing prior distributions of various parameters and initial conditions. The unknown parameters are estimated using Markov chain Monte Carlo methods. By combining the temporally distributed geochemical data with the spatially distributed geophysical data, we obtain the spatio-temporal distribution of ferrous iron, sulfate and sulfide, and their associated uncertainity information. The obtained results can be used to assess the efficacy of the bioremediation treatment over space and time and to constrain reactive transport models.

Chen, Jinsong↗
DOE OSTI2021

Self-similar Reynolds-averaged mechanical–scalar turbulence models for Rayleigh–Taylor, Richtmyer–Meshkov, and Kelvin–Helmholtz instability-induced mixing in the small Atwood number limit

Analytical self-similar solutions to two-, three-, and four-equation Reynolds-averaged mechanical–scalar turbulence models describing incompressible turbulent Rayleigh–Taylor, Richtmyer–Meshkov, and Kelvin–Helmholtz instability-induced mixing in planar geometry are derived in the small Atwood number (Boussinesq) limit. The models are based on the turbulent kinetic energy K and its dissipation rate ε, together with the scalar (heavy-fluid mass fraction) variance S and its dissipation rate Χ modeled either differentially or algebraically. The models allow for a simultaneous description of mechanical and scalar mixing, i.e., mixing layer growth and molecular mixing, respectively. Mixing layer growth parameters and other physical observables relevant to each instability are obtained explicitly as functions of the model coefficients. The turbulent fields are also expressed in terms of the model coefficients, with their temporal power-law scalings obtained by requiring that the self-similar equations are explicitly time-independent. The model calibration methodology is described and discussed. Expressions for a subset of the various physical observables are used to calibrate each of the two-, three-, and four-equation models, such that the self-similar solutions are consistent with experimental and numerical simulation data corresponding to these values of the observables and to specific canonical Rayleigh–Taylor, Richtmyer–Meshkov, and Kelvin–Helmholtz turbulent flows. A calibrated four-equation model is then used to reconstruct the mean and turbulent fields, and late-time turbulent equation budgets for each instability-induced flow across the mixing layer. The reference solutions derived here can provide systematic calibrations and better understanding of mechanical–scalar turbulence models and their predictions for instability-induced turbulent mixing in the very large Reynolds number limit.

42 ENGINEERING↗
DOE OSTI2025

pyTCR: A tropical cyclone rainfall model for python

pyTCR is a climatology software package developed in the Python programming language. It integrates the capabilities of several legacy physical models and increases computational efficiency to allow rapid estimation of tropical cyclone (TC) rainfall consistent with the large-scale environment. Specifically, pyTCR implements a horizontally distributed and vertically integrated model [Zhu et al., 2013] for simulating rainfall driven by TCs. Along storm tracks, rainfall is estimated by computing the cross-boundary-layer, upward water vapor transport caused by different mechanisms including frictional convergence, vortex stretching, large-scale baroclinic effect (i.e., wind shear), topographic forcing, and radiative cooling [Lu et al., 2018]. The package provides essential functionalities for modeling and interpreting spatio-temporal TC rainfall data. pyTCR requires a limited number of model input parameters, making it a convenient and useful tool for analyzing rainfall mechanisms driven by TCs. To sample rare (most intense) rainfall events that are often of great societal interest, pyTCR adapts and leverages outputs from a statistical-dynamical TC downscaling model [Lin et al., 2023] capable of rapidly generating a large number of synthetic TCs given a certain climate. As a result, pyTCR significantly reduces computational effort and improves the efficiency in capturing extreme TC rainfall events at the tail of the distributions from limited datasets. Furthermore, the TC downscaling model is forced entirely by large-scale environmental conditions from reanalysis data or coupled General Circulation Models (GCMs), simplifying the projection of TC-induced rainfall and wind speed under future climate using pyTCR. Finally, pyTCR can be coupled with hydrological and wind models to assess risks associated with independent and compound events (e.g., storm surges and freshwater flooding).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Energy Conversion and Electron Acceleration and Transport in 3D Simulations of Solar Flares

Recent observations and simulations indicate that solar flares undergo extremely complex 3D evolution, making 3D particle transport models essential for understanding electron acceleration and interpreting flare emissions. In this study, we investigate this problem by solving Parker’s transport equation with 3D MHD simulations of solar flares. By examining energy conversion in the 3D system, we evaluate the roles of different acceleration mechanisms, including reconnection current sheet (CS), termination shock (TS), and supra-arcade downflows (SADs). We find that large-amplitude turbulent fluctuations are generated and sustained in the 3D system. The model results demonstrate that a significant number of electrons are accelerated to hundreds of keV and even a few MeV, forming power-law energy spectra. These energetic particles are widely distributed, with concentrations at the TS and in the flare looptop region, consistent with results derived from recent hard X-ray (HXR) and microwave (MW) observations. By selectively turning particle acceleration on or off in specific regions, we find that the CS and SADs effectively accelerate electrons to several hundred keV, while the TS enables further acceleration to MeV. However, no single mechanism can independently account for the significant number of energetic electrons observed. Instead, the mechanisms work synergistically to produce a large population of accelerated electrons. Our model provides spatially and temporally resolved electron distributions in the whole flare region and at the flare footpoints, enabling synthetic HXR and MW emission modeling for comparison with observations. These results offer important insights into electron acceleration and transport in 3D solar flare regions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Spatial and temporal variations of new particle formation in East Asia using an NPF-explicit WRF-chem model: North-south contrast in new particle formation frequency

The new particle formation (NPF)-explicit version of the WRF-chem model, which we developed recently, can calculate the growth and sink of nucleated clusters explicitly with 20 aerosol size bins from 1 nm to 10 μm. In this study, the model is used to understand spatial and temporal variations of the frequency of NPF events and the concentrations of aerosols (condensation nuclei, CN) and cloud condensation nuclei (CCN) within the boundary layer in East Asia in spring 2009. Model simulations show distinct north-south contrast in the frequency and mechanism of NPF in East Asia. NPF mostly occurred over limited periods and regions between 30° and 45°N, such as northeast China, Korea, and Japan, including regions around active volcanoes (Miyakejima and Sakurajima). At these latitudes, NPF was considerably suppressed by high concentrations of preexisting particles under stagnant air conditions associated with high-pressure systems, while nucleation occurred more extensively on most days during the simulation period. Conversely, neither nucleation nor NPF occurred frequently south of 30°N because of lower SO2 emissions and H2SO4 concentrations. The period-averaged NPF frequency was 3 times higher at latitudes of 30° - 45°N than at latitudes of 20° - 30°N. The north-south contrast of NPF frequency is validated by surface measurements in outflow regions in East Asia. The period- and domain-averaged contribution of secondary particles is estimated to be 44% for CN (> 10 nm) and 26% for CCN at a supersaturation of 1.0% in our simulation, though the contribution is highly sensitive to the magnitudes and size distributions of primary aerosol emissions and the coefficients in the nucleation parameterizations.

Matsui, H.↗
DOE OSTI2013

One-way coupling of an integrated assessment model and a water resources model: evaluation and implications of future changes over the US Midwest

An integrated model is being developed to advance our understanding of the interactions between human activities, terrestrial system and water cycle, and how system interactions will be affected by a changing climate at the regional scale. As a first step towards that goal, a global integrated assessment model including a waterdemand model is coupled offline with a land surface hydrology – routing – water resources management model. A spatial and temporal disaggregation approach is developed to project the annual regional water demand simulations into a daily time step and subbasin representation. The model demonstrated reasonable ability to represent the historical flow regulation and water supply over the Midwest (Missouri, Upper Mississippi and Ohio). Implications for the future flow regulation, water supply and supply deficit are investigated using a climate change projection with the B1 emission scenario which affects both natural flow and water demand. Over the Midwest, changes in flow regulation are mostly driven by the change in natural flow due to the limited storage capacity over the Ohio and Upper Mississippi river basins. The changes in flow and demand have a combined effect on the Missouri Summer regulated flow. The supply deficit tends to be driven by the change in flow over the region. Spatial analysis demonstrates the relationship between the supply deficit and the change in demand over urban areas not along a main river or with limited storage, and over areas upstream of groundwater dependent fields with therefore overestimated demand.

Environmental Molecular Sciences Laboratory↗