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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Distribution of Infiltration in the 216-U-10 and 216-B-3 Pond Systems 1944-1997

A modeling system to simulate fate and transport of contaminant plumes in groundwater beneath the Central Plateau to support the Cumulative Impact Evaluation (CIE) and Composite Analysis (CA) is being developed. The CIE will evaluate the cumulative effect of multiple Central Plateau radiological and chemical sources on groundwater quality (DOE/RL-2018-69, Cumulative Impact Evaluation Technical Approach Document ). This will enable cleanup decisions to be evaluated in the context of all Central Plateau waste sites and existing groundwater conditions. The CA evaluates the cumulative effect of all sources of radioactive materials on radiological dose to future members of the public (DOE/RL-2018-60, Annual Status Report (FY 2018): Composite Analysis of Low Level Waste Disposal in the Central Plateau of the Hanford Site ). Fate and transport modeling will be used to forecast future groundwater concentrations for both the CIE and CA. The CIE/CA subsurface modeling system is divided into source (inventory), vadose zone, and saturated zone facets. Source inventory is a necessary input to the vadose zone facet, and releases from the vadose zone to groundwater are inputs to the saturated zone facet. The vadose zone facet simulates historical and future releases from the vadose zone to groundwater for both liquid waste and solid waste disposal sites. Effluent release volumes and radionuclide inventory estimates for liquid waste sites are contained in ECFHANFORD- 17-0079, Hanford Soil Inventory Model (SIM-v2) Calculated Radionuclide Inventory of Direct Liquid Discharges to Soil in the Hanford Site’s 200 Areas , herein referred to as SIM-v2. The liquid waste sites in SIM-v2 include the 216-B-3 Pond near 200 East Area and the 216-U-10 Pond in 200 West Area. The 216-B-3 Pond system consisted of a main pond and 3 expansion lobes that were operational during overlapping time periods. It also included influent ditches, but these are not listed as liquid waste sources in SIM-v2 and are not included in the CIE/CA modeling. The total effluent volume released to the pond system was measured, but the partitioning of this volume to the various expansion lobes was not determined and is not accounted for in SIM-v2. Regarding the 216-U-10 Pond, it received water from several influent ditches. The pond and the influent ditches are listed as liquid waste sites in SIM-v2 and are included as sources in the CIE/CA modeling. Because water in the ditches flowed into the pond, only a portion of the effluent volumes and contaminant inventories assigned to the ditches in SIM-v2 infiltrated from the ditches. Thus, partitioning of infiltration and contaminant inventories between the ditches and the pond is not fully accounted for in SIM-v2. Further, some of the water in the 216-U-10 Pond entered overflow ditches and the amount of this overflow was not recorded and is not accounted for in SIM-v2. Thus, the purpose of this Environmental Calculation File (ECF) is to partition the infiltration of wastewater and contaminant inventory between the main pond and each expansion lobe of the 216-B-3 Pond system, and to partition the infiltration of wastewater and contaminant inventory between the ditches and pond of the 216-U-10 Pond system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Hourly PM 2.5 Estimates across California from 2018 to 2023

This study presents a new data set of hourly PM 2.5 concentrations across California from 2018 to 2023 at a three-kilometer resolution. This data set was developed by assimilating observations from PurpleAir and the U.S. EPA Air Quality System monitors into wildfire smoke forecasts from the High-Resolution Rapid Refresh Smoke (HRRR-Smoke) model using the Gridpoint Statistical Interpolation (GSI) three-dimensional variational data assimilation framework. Archived forecasts of modeled wildfire smoke PM 2.5 from HRRR-Smoke create the background field for assimilation, which is then corrected using surface observations of total PM 2.5 . The resulting reanalysis from GSI provides an estimate of total PM 2.5 that minimizes error from both the observational and the model data. Validation results indicate strong performance, with monthly R 2 values ranging from 0.73 to 0.91 across the six-year data set, comparable to other PM 2.5 data sets. Case studies are presented for three major fire events, the 2018 Camp Fire, 2019 Kincade Fire, and 2020 Lightning Complex Fires to demonstrate the data set’s fidelity in resolving plume dynamics and local exposure patterns. Root-mean-squared error averaged over each month scales with average PM 2.5 concentrations, resulting in a low error under typical conditions but higher absolute errors during extreme smoke events. This is the first long-term, hourly PM 2.5 data set of its kind for California and enables the generation of subdaily exposure metrics, such as peak hourly concentrations, exceedance durations, and time-of-day exposure peaks. The novelty and strong validation of this data set make it a compelling resource for future studies on the impact and significance of subdaily PM 2.5 exposure.

PM2.5↗
DOE OSTI2020

Micro-Pulse DIAL for Atmospheric Water Vapor Profiling (Phase I Final Report)

Water vapor plays a key role in Earth’s climate and weather patterns. Accurate, high resolution measurements of the water vapor vertical profiles in a region have been found to significantly improve the numerical weather prediction models’ short-term forecasts, precipitation forecasts, as well as severe weather forecasts. However, current measurement techniques provide very coarse resolution in space, time, and/or altitude. A network of continuously sampling water vapor profilers would greatly expand the scientific knowledge of the atmosphere and serve as a valuable input into numerical weather models. This would be of interest not only to the Department of Energy’s Environmental Sciences Division but to other institutions such as the National Weather Service, the National Center for Atmospheric Research, the Federal Emergency Management Agency, and the military.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessing Effects of Climate Change on Legacy Waste at the Enewetak Atoll

The Republic of the Marshall Islands (RMI) is in the central Pacific Ocean ~4,500 km west of Hawaii. The Enewetak Atoll, located in the northwest part of the RMI, was the site for 43 nuclear weapon tests between 1948 and 1958. Fallout and deposition from the tests contaminated the island surfaces, lagoon waters and sediment, and nearby ocean waters at the atoll. In the 1970s, a cleanup effort collected radioactive waste and placed it in the Cactus Crater on Runit Island (also called the Runit Dome). In December 2021, Congress directed the U.S. Department of Energy to study the impacts of climate change on the Runit Dome nuclear waste disposal site. Pacific Northwest National Laboratory (PNNL) assembled a multidisciplinary team of climate scientists, ocean modelers, environmental scientists, and health physicists to assess the likely effects of remaining radionuclides at the Enewetak Atoll. PNNL’s approach focused on effects of tropical cyclones that were postulated to mobilize and transport contaminated lagoon sediments and result in human and biota exposure. PNNL’s study estimated (1) the radionuclide source term, (2) the effects of climate change on severe storms, (3) mobilization and transport of radionuclides, and (4) radiation dose to humans and biota. Radionuclides in the lagoon and/or ocean waters of the Enewetak Atoll were characterized by the U.S. Atomic Energy Commission (AEC) in 1972, Woods Hole Oceanographic Institution in 2015, and Lawrence Livermore National Laboratory in 2018. The RMI Nationwide Radiological Study was conducted in the early 1990s for radionuclides remaining in island soils. The 1972 AEC survey remains the most comprehensive source of radionuclide data on lagoon sediments. Climate change modeling at a regional scale in the central Pacific Ocean is limited. PNNL climate scientists simulated severe historical storms postulated to occur both in a recent climate (2015) and in the future (2090) using the Advanced Research Weather Research and Forecasting (WRF-ARW) model, employing a pseudo-global-warming technique. A postulated complete, future failure of the Runit Dome was also considered. PNNL developed a high-resolution regional ocean hydrodynamics model covering the entire RMI extended economic zone using the Finite Volume Coastal Ocean Model (FVCOM). The FVCOM model was run using global reanalysis data for current climate and WRF-ARW simulation for the future climate. PNNL also developed a radionuclide fate and transport model using the FVCOM Integrated Compartment Model (FVCOM-ICM) to simulate the current and future mobilization and transport of radionuclides sorbed to lagoon sediments and the exchange of radionuclides between the water and sediment. FVCOM-ICM-predicted radionuclide concentrations were then used to estimate radiation dose to humans and biota at all islands of the Enewetak Atoll. Under current climate conditions, annual radiation exposures for the southern islands including Enewetak (Fred) and Medren (Elmer) were below the current U.S. standards. Radiation doses were somewhat elevated starting at Runit Island northward and westward to Enjebi Island (Janet). The islands in the northwest quadrant, particularly Bokoluo (Alice) and Bokombako (Belle), remain relatively contaminated. The islands in the southwestern quadrant have low contamination. The highest contribution to radiation doses comes from consumption of locally grown foods. Two radionuclides, 90Sr and 137Cs, contributed the greatest fraction for most terrestrial foods. In current climate conditions, the storms temporarily increased radionuclide concentrations in the lagoon waters, increasing the radiation dose slightly. In future conditions, doses are expected to be smaller, primarily because of the radioactive decay of the shorter-lived radioisotopes of 90Sr and 137Cs. This could make all islands in the far northwest of the atoll – except Bokombako (Belle) and perhaps Bokoluo (Alice) – suitable for residency. For the f

Prasad, Rajiv↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Top-down estimate of methane emissions in California using a mesoscale inverse modeling technique: The South Coast Air Basin

Methane (CH 4 ) is the primary component of natural gas and has a larger global warming potential than CO 2 . Some recent top-down studies based on observations showed CH 4 emissions in California's South Coast Air Basin (SoCAB) were greater than those expected from population-apportioned bottom-up state inventories. In this study, we quantify CH 4 emissions with an advanced mesoscale inverse modeling system at a resolution of 8 km × 8 km, using aircraft measurements in the SoCAB during the 2010 Nexus of Air Quality and Climate Change campaign to constrain the inversion. To simulate atmospheric transport, we use the FLEXible PARTicle-Weather Research and Forecasting (FLEXPART-WRF) Lagrangian particle dispersion model driven by three configurations of the Weather Research and Forecasting (WRF) mesoscale model. We determine surface fluxes of CH 4 using a Bayesian least squares method in a four-dimensional inversion. Simulated CH4 concentrations with the posterior emission inventory achieve much better correlations with the measurements (R2 = 0.7) than using the prior inventory (U.S. Environmental Protection Agency's National Emission Inventory 2005, R 2 = 0.5). The emission estimates for CH 4 in the posterior, 46.3 ± 9.2 Mg CH 4 /h, are consistent with published observation-based estimates. Changes in the spatial distribution of CH 4 emissions in the SoCAB between the prior and posterior inventories are discussed. Missing or underestimated emissions from dairies, the oil/gas system, and landfills in the SoCAB seem to explain the differences between the prior and posterior inventories. Furthermore, we estimate that dairies contributed 5.9 ± 1.7 Mg CH 4 /h and the two sectors of oil and gas industries (production and downstream) and landfills together contributed 39.6 ± 8.1 Mg CH 4 /h in the SoCAB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

A multitower measurement network estimate of California's methane emissions

In this paper, we present an analysis of methane (CH 4 ) emissions using atmospheric observations from five sites in California's Central Valley across different seasons (September 2010 to June 2011). CH 4 emissions for spatial regions and source sectors are estimated by comparing measured CH 4 mixing ratios with transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) predictions based on two 0.1° CH 4 (seasonally varying “California-specific” (California Greenhouse Gas Emission Measurements, CALGEM) and a static global (Emission Database for Global Atmospheric Research, release version 42, EDGAR42)) prior emission models. Region-specific Bayesian analyses indicate that for California's Central Valley, the CALGEM- and EDGAR42-based inversions provide consistent annual total CH 4 emissions (32.87 ± 2.09 versus 31.60 ± 2.17 Tg CO 2 eq yr -1 ; 68% confidence interval (CI), assuming uncorrelated errors between regions). Summing across all regions of California, optimized CH 4 emissions are only marginally consistent between CALGEM- and EDGAR42-based inversions (48.35 ± 6.47 versus 64.97 ± 11.85 Tg CO 2 eq), because emissions from coastal urban regions (where landfill and natural gas emissions are much higher in EDGAR than CALGEM) are not strongly constrained by the measurements. Combining our results with those from a recent study of the South Coast Air Basin narrows the range of estimates to 43–57 Tg CO2eq yr -1 (1.3–1.8 times higher than the current state inventory). Finally, these results suggest that the combination of rural and urban measurements will be necessary to verify future changes in California's total CH 4 emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Achieving Realistic Runoff in the Western United States with a Land Surface Model Forced by Dynamically Downscaled Meteorology

Abstract In this study, we calibrate a regional climate model’s (RCM) underlying land surface model (LSM). In addition to providing a realistic representation of runoff across the hydroclimatically diverse western United States, this is done to take advantage of the RCM’s ability to physically resolve meteorological forcing data in ungauged regions, and to prepare the calibrated hydrologic model for tight coupling, or the ability to represent land surface–atmosphere interactions, with the RCM. Specifically, we use a 9-km resolution meteorological forcing dataset across the western United States, from the fifth generation ECMWF Reanalysis (ERA5) downscaled by the Weather Research Forecasting (WRF) regional climate model, as an offline forcing for Noah-Multiparameterization (Noah-MP). We detail the steps involved in producing an LSM capable of accurately representing runoff, including physical parameterization selection, parameter calibration, and regionalization to ungauged basins. Based on our model evaluation from 1954 to 2021 for 586 basins with daily natural streamflow, the streamflow bias is reduced from 24.2% to 4.4%, and the median daily Nash–Sutcliffe efficiency (NSE) is improved from 0.12 to 0.36. When validating against basins with monthly natural streamflow data, we obtain a similar reduction in bias and a median monthly NSE improvement from 0.18 to 0.56. In this study, we also discover the optimal setup when using a donor-basin method to regionalize parameters to ungauged basins, which can vary by 0.06 NSE for unique designs of this regionalization method. Significance Statement This study provides useful guidance for improving a land surface model to accurately represent runoff across a spatially extensive and hydroclimatically diverse region (the western United States). The land surface model is updated to represent runoff more accurately at gauged basins, and then additionally updated for basins without observational data using a mathematical approach called the donor-basin method. We make use of a regional climate model’s reanalysis-derived meteorological data and its underlying land surface model to achieve realistic runoff. The calibrated land surface model can thus be tightly coupled in subsequent studies in a manner that should more accurately reflect runoff conditions. Findings from this study will serve as a useful reference for the atmospheric (and hydrologic) modeling communities and their ability to represent large-scale hydrology accurately.

Meteorology & Atmospheric Sciences↗
DOE OSTI2018

Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach

The significantly increasing deployment of wind power necessitates that system operation considers the spatial-temporal correlation of power forecast from different wind power plants. How to model this spatial-temporal correlation in the actual system dispatch is challenging. In this paper, a sparse correlation matrix is utilized to efficiently model the spatial-temporal correlation of wind power forecast in the generation dispatch model. A novel, adjustable, and distributionally-robust chance-constrained multi-interval optimal power flow (ADRCC-MIOPF) model is proposed to obtain reliable economic dispatch (ED) solutions. The spatial-temporal correlation of wind power plants power forecasts is represented by the sparse correlation covariance matrix obtained from historical, time series wind power forecast error data. The chance constraints in the ADRCC-MIOPF model are transformed into a set of second-order-cone (SOC) constraints in which an adjustable coefficient in the chance constraints controls the robustness of the ADRCC-MIOPF model to the wind power forecast errors distribution. Case studies performed on the PJM 5-bus system and IEEE 118-bus system verify the proposed method to improve the system security and reduce the cost especially under the high wind penetration levels. All the cases can be solved within several minutes for both the small and large cases which validates the efficiency of the proposed sparse matrix model. In addition, considering the spatial-temporal correlation of wind power forecast and the distributional robustness of wind power forecast error leads to a more reliable economic dispatch with lower system violations.

17 WIND ENERGY↗
DOE OSTI2026

Iterative ML and Experiments for Emerging VOCs

SAND2026-17074O Iterative ML and Experiments for Emerging VOCs is a tool that analyzes and predicts the behaviors of SARS-CoV-2 variants. It processes experimental data on ACE2 (the receptor for the SARS-CoV-2 virus that allows it to infect the cell) and antibody binding using machine learning models, including neural networks, to forecast ACE2 interactions and variant expression. The tool employs transfer learning and global epistasis modeling, integrating public datasets with proprietary data to enhance prediction accuracy. Additionally, it fits concentration-response curves to determine dissociation constants and generates visualizations to support research findings, thereby aiding in the identification of new antibodies for emerging variants of concern. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Sheffield, Thomas [Sandia National Lab. (SNL-NM), ↗
DOE OSTI2019

Capability of CAM5.1 in simulating maximum air temperature patterns over West Africa during boreal spring

This study classifies maximum air temperature patterns over West Africa into six groups and evaluates the capability of a global climate model (Community Atmospheric Model version 5.1; CAM) to simulate them. We analyzed 45-year (1961–2005) multi-ensemble (50 members) simulations from CAM and compared the results with those of the Climate Research Unit (CRU) and the twentieth Century Reanalysis data sets. Using Self Organizing Map algorithm to classify the spatial patterns of maximum air temperature during boreal spring, the study reveals the temperature patterns that CAM can simulate well and those the model struggles to reproduce. The results show that the best agreements between the composites of observation and CAM occur in the first temperature pattern group (which features positive temperatures anomalies over the Sahel) and Node 2 (which features near-normal temperature) pattern of the third group. CAM succeeded in reproducing some of the associated regional atmospheric dynamics and thermodynamic features in winds (horizontal and vertical), temperature fields, the cloud fractions, and the mean sea-level pressure. Although CAM struggles to capture the relationship between air temperature patterns and tele-connection indices during the boreal spring season over West Africa, it agrees with observations that temperature patterns over the sub-region cannot be associated with a single climate index. An ensemble member (SIM48) captures the inter-annual variation of the observed temperaure patterns with high sycronization (η > 44%), much better than that of ensembles mean (η < 30%). SIM48 also captures adequately four of the spatial patterns in comparison to three captured by the ensembles mean. This indicates that, for better seasonal forecasts and more reliable future climate projections, the practice whereby an ensemble mean is based on uniformly averaging the members rather than the performance of individual ensemble members needs to be reviewed. Finally, the results of the study may be used to improve the perfomance of CAM over West Africa, thereby strengthening the on-going efforts to include CAM as part of multi-model forecasting system over West Africa.

Lawal, Kamoru A. (ORCID:0000000281988844)↗
DOE OSTI2013

Decay-phase cooling and inferred heating of M- and X-class solar flares

In this paper, the cooling of 72 M- and X-class flares is examined using GOES/XRS and SDO/EVE. The observed cooling rates are quantified and the observed total cooling times are compared with the predictions of an analytical zero-dimensional hydrodynamic model. We find that the model does not fit the observations well, but does provide a well-defined lower limit on a flare's total cooling time. The discrepancy between observations and the model is then assumed to be primarily due to heating during the decay phase. The decay-phase heating necessary to account for the discrepancy is quantified and found be ∼50% of the total thermally radiated energy, as calculated with GOES. This decay-phase heating is found to scale with the observed peak thermal energy. It is predicted that approximating the total thermal energy from the peak is minimally affected by the decay-phase heating in small flares. However, in the most energetic flares the decay-phase heating inferred from the model can be several times greater than the peak thermal energy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Developing grain boundary diagrams as a materials science tool: A case study of nickel-doped molybdenum

Impurity-based, premelting-like, grain boundary (GB) ''phases'' (complexions) can form in alloys and influence sintering, creep, and microstructural development. Calculation of Phase Diagrams (CalPhaD) methods and Miedema-type statistical interfacial thermodynamic models are combined to forecast the formation and stability of subsolidus quasiliquid GB phases in binary alloys. This work supports a long-range scientific goal of developing ''GB (phase) diagrams'' as a new materials science tool to help controlling the materials fabrication processing and resultant materials properties. Using nickel-doped molybdenum as a model system, a type of GB diagram (called ''{lambda} diagram'') is computed to represent the temperature- and composition-dependent thermodynamic tendency for general GBs to disorder. Subsequently, controlled sintering experiments are conducted to estimate the GB diffusivity as a function of temperature and overall composition, and the experimental results correlate well with the computed GB diagram. Although they are not yet rigorous GB-phase diagrams with well-defined transition lines, the predictability and usefulness of such {lambda} diagrams are demonstrated. Related interfacial thermodynamic models and computational approaches are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Review of Recent Developments in the Random-Field Ising Model

A lot of progress has been made recently in our understanding of the random-field Ising model thanks to large-scale numerical simulations. In particular, it has been shown that, contrary to previous statements: the critical exponents for different probability distributions of the random fields and for diluted antiferromagnets in a field are the same. Therefore, critical universality, which is a perturbative renormalization-group prediction, holds beyond the validity regime of perturbation theory. Most notably, dimensional reduction is restored at five dimensions, i.e., the exponents of the random-field Ising model at five dimensions and those of the pure Ising ferromagnet at three dimensions are the same.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Learning-Based Building Flexibility Estimation and Control to Improve Microgrid Economics and Resilience: Preprint

This paper proposes a learning-based building flexibility estimation and control framework to improve system economics and resilience. A data-driven building load flexibility model consisting of weather forecasting and estimating load consumption is proposed to quantify building heating, ventilation, and air conditioning (HVAC) load flexibility. A reinforcement learning-based microgrid controller is proposed to dispatch distributed generators, distributed energy resources, and build HVAC loads while taking flexibility information as one of the inputs. Simulation analysis is conducted on the model of a real microgrid in California. The effectiveness of the proposed learning-based building flexibility estimation and control in reducing microgrid energy costs and improving the sustainability of critical loads is demonstrated.

building load flexibility↗
DOE OSTI2019

Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods

This paper proposes a novel approach to identify the load patterns considering calendar impact and denoising process, which advances the state of art of load identification and load forecasting to a more flexible and adaptive extent by revealing the correlation between load profile and the substantial influence factors. With the rapid development and the promising application of energy intemet and grid modernization, the accurate load modeling and forecasting especially by load pattern modeling (LPM) have become more and more critical to observe and estimate load profile so as to optimize the operation efficiency of the power grid. Traditional methods mostly focus on the static LPM itself which are unable to account the non-linear and highly dynamic load fluctuations in order to balance the demand of the power grid during various calendar period (VCP) scheduling. In this paper, a load pattern modeling method for power grid considering the impact of typical calendar effects is proposed using a combination of improved variational mode decomposition (IVMD) and deep belief network (DBN) algorithm. Firstly, an index of load curve curvature (LCC) is proposed to improve the selection of value K in the conventional VMD algorithm. The value K of the intrinsic modality is determined by adopting LCC to better quantify the average instantaneous frequency of the modal decomposition of the VMD and to reduce the elevated stochasticity of k. Secondly, an optimization formulation is developed based on adaptive segmented loop decomposition of the typical correlation coefficient to suppress the edge effect in VMD decomposition, which would enable the reconstructed curve to incorporate the variabilities from the initial data. Finally, the IVMD and DBN algorithms are jointly implemented to optimize the accuracy of VCP load pattern modeling curve regression. Numerical examples have been performed with the results proving that the proposed method significantly outperforms the state-of-art in reducing the modeling error of VCP load curve, improving the overall forecasting accuracy, and reducing load dispatching cost.

deep belief network↗
DOE OSTI2022

A new method for inferring city emissions and lifetimes of nitrogen oxides from high-resolution nitrogen dioxide observations: a model study

We present a new method to infer emissions and lifetimes of nitrogen oxides (NO x ) based on tropospheric nitrogen dioxide (NO 2 ) observations together with reanalysis wind fields for cities located in polluted backgrounds. Since the accuracy of the method is difficult to assess due to lack of “true values” that can be used as a benchmark, we apply the method to synthetic NO 2 observations derived from the NASA-Unified Weather Research and Forecasting (NU-WRF) model at a high horizontal spatial resolution of 4 km × 4 km for cities over the continental United States. We compare the inferred emissions and lifetimes with the values given by the NU-WRF model to evaluate the method. The method is applicable to 26 US cities. The derived results are generally in good agreement with the values given by the model, with the relative differences of 2 % ± 17 % (mean ± standard deviation) and 15 % ± 25 % for lifetimes and emissions, respectively. Our investigation suggests that the use of wind data prior to the satellite overpass time improves the performance of the method. The correlation coefficients between inferred and NU-WRF lifetimes increase from 0.56 to 0.79 and for emissions increase from 0.88 to 0.96 when comparing results based on wind fields sampled simultaneously with satellite observations and averaged over 9 h data prior to satellite observations, respectively. We estimate that uncertainties in NO x lifetime and emissions arising from the method are approximately 15 % and 20 %, respectively, for typical (US) cities. The total uncertainties reach up to 43% (lifetimes) and 45% (emissions) by considering the additional uncertainties associated with satellite NO 2 observations and wind data. We expect this new method to be applicable to NO 2 observations from the TROPOspheric Monitoring Instrument (TROPOMI) and geostationary satellites, such as Geostationary Environment Monitoring Spectrometer (GEMS) or the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument, to estimate urban NO x emissions and lifetimes globally.

54 ENVIRONMENTAL SCIENCES↗