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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Clear-Sky Probability for the 2023 Annular Solar Eclipse and the 2024 Total Solar Eclipse Using the NREL National Solar Radiation Database

The National Renewable Energy Laboratory (NREL) and collaborators have created a clear-sky probability analysis to help guide viewers to the 2023 annular solar eclipse and the 2024 total solar eclipse. Using cloud and solar data from the NREL National Solar Radiation Database (NSRDB), the analysis provides cloudless-sky probabilities specific to the date, time, and location of each eclipse with a 4-km resolution. Though not intended to be an eclipse weather forecast, the detailed maps can help guide eclipse enthusiasts to likely optimal viewing locations. Additionally, high-resolution data are presented for the centerline of the path of each eclipse, representing the likelihood for cloudless skies and atmospheric clarity. The NSRDB provides industry, academia, and other stakeholders with high-resolution solar irradiance data to support feasibility analyses for photovoltaic and concentrating solar power generation projects.

14 SOLAR ENERGY↗
DOE OSTI2026

Dynamical Downscaling of Earth System Model Data for Energy System Analysis

Assessing energy resources (e.g., solar, wind, and hydro) under future scenarios requires datasets with sufficient spatial and temporal detail to capture variability and extreme events. While global-scale Earth System Model (ESM) projections are widely used, their coarse resolution limits direct application to regional energy system analyses. Dynamical downscaling offers a robust approach to generate physically consistent, fine-scale datasets that better represent local atmospheric processes impacting energy resources. In this work, we present a two-stage approach for producing high-resolution historical and future projections over the contiguous United States (CONUS). First, we optimize the Weather Research and Forecasting (WRF) model configuration for energy-relevant variables - solar irradiance, wind speed, and precipitation - by conducting ERA5-driven simulations at 8-km and 28-km resolution. Multiple physics schemes and model configurations within the WRF are evaluated against observational datasets including the National Solar Radiation Database (NSRDB), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the Stage IV multi-radar/multi-sensor precipitation product for the CONUS domain. Using the best-performing configuration, we dynamically downscale MPI-ESM1-2-HR simulations for 2000-2060 under SSP2-4.5 and SSP5-8.5 scenarios at 4-km spatial and hourly temporal resolution. This presentation will provide a comprehensive analysis of the results from multiple numerical experiments and high-resolution ESM projections. In addition, we will discuss potential applications of our high-resolution datasets within the energy sector and outline future research avenues dedicated to evaluating how extreme weather events influence system performance and resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains

Gravity waves are a common occurrence in the atmosphere, with a variety of generation mechanisms. Their impact on wind farms has only recently gained attention, with most studies focused on wind farm-induced gravity waves. In this study, the interaction between a wind farm and gravity waves generated by an atmospheric bore event is assessed using multiscale large-eddy simulations. The atmospheric bore is created by a thunderstorm downdraft from a nocturnal mesoscale convective system (MCS). The associated gravity waves impact the wind resource and power production at a nearby wind farm during the American Wake Experiment (AWAKEN) in the US southern Great Plains. A two-domain nested setup (Δx=300 and 20 m) is used in the Weather Research and Forecasting (WRF) model, forced with data from the High-Resolution Rapid Refresh model, to capture both the formation of the bore and its interaction with individual wind turbines. The MCS is resolved on the large outer domain, where the structure of the bore and the associated gravity waves are found to be especially sensitive to parameterized microphysics processes. On the finer inner domain, gravity wave interactions with individual wind turbines are resolved; wake dynamics are captured using a generalized actuator disk parameterization in WRF. The gravity waves are found to have a strong effect on the atmosphere above the wind farm; however, the effect of the waves is more nuanced closer to the surface where there is additional turbulence, both ambient and wake-generated. Notably, the gravity waves modulate the mesoscale environment by weakening and dissipating the preexisting low-level jet, which reduces hub-height wind speed and hence the simulated power output, which is confirmed by the observed supervisory control and data acquisition (SCADA) power data. Additionally, the gravity waves induce local wind direction variations correlated with fluctuations in pressure, which lead to fluctuations in the simulated power output as various turbines within the farm are subjected to waking from nearby turbines.

17 WIND ENERGY↗
DOE OSTI2024

Improved Diagnosis of Precipitation Type with LightGBM Machine Learning

Abstract Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 , as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 . Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2026

A multi‐variable framework for selecting WRF physics configurations at convection‐permitting scales: An Amazon wet‐season case study

Tropical convection over rainforests modulates atmospheric circulation and the energy and hydrological cycles across multiple scales. However, the scarcity of observations still limits our understanding of these processes. Although numerical models are utilized to investigate atmospheric physical processes, their performance depends on the choice of parameterizations and grid resolution. Here, in this work, we introduce and apply a multi‐variable, multi‐physics framework to evaluate and rank the Advanced Research Weather Research and Forecasting (WRF–ARW) configurations at 1‐km resolution over the central Amazon during the wet season. A 48‐member ensemble combines three land‐surface models (LSMs), four planetary boundary layer (PBL), and four microphysics (MP) schemes. Model performance for seven convection‐related near‐surface and boundary‐layer variables is assessed using Taylor diagrams and the Taylor skill score (TSS), analysis of variance (ANOVA)‐based sensitivity metrics, and non‐parametric rank tests. We then construct a combined, weighted TSS to identify configurations that are comparatively robust across variables. Results showed that most configurations reproduce near‐surface temperature, sensible and latent heat fluxes, and boundary‐layer height reasonably well, whereas humidity and rainfall remain challenging. LSM choice has the strongest impact on the surface fluxes and a secondary influence on near‐surface temperature and humidity, PBL schemes dominate boundary‐layer height, and MP schemes exert the largest control on rainfall. No single configuration is optimal for all variables, but the combination of Noah (LSM), Yonsei University (PBL), and Morrison (MP) emerges as the most robust configuration for this case, with the WRF single‐moment six‐class scheme (WSM6) providing a competitive, computationally cheaper MP alternative. The framework is general and can be applied to other regions, seasons, and convective regimes.

Amazon rainforest↗
DOE OSTI2022

Improved Simulations of Atmospheric River Climatology and Variability in High-Resolution CESM

Atmospheric rivers (ARs) can trigger extreme rainfall and snowfall events, causing high risk of flooding. Meanwhile, they are sometimes responsible for the occurrence and break of drought events. So, it is of great importance for weather and climate forecast models to accurately represent ARs. Studies have shown that AR variability is linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, Pacific-North America pattern, providing the potential for predicting ARs at subseasonal-to-decadal timescales. In this study, we examine the impact of climate model resolution on AR simulations and their relationship with major climate modes by comparing a pair of high-resolution and a low-resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results reveal major deficiencies in LR that include underestimated climatological AR strength and associated precipitation, and inaccurate representations of ARs relationships with climate modes. Significant improvements are observed by increasing CESM horizontal resolution, suggesting a path for more accurate prediction of ARs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Soil moisture gradients strengthen mesoscale convective systems by increasing wind shear

Mesoscale convective systems are a class of storm linked to extensive flooding and other destructive hazards in many regions globally. In West Africa, soil moisture impacts provide a valuable source of predictability for mature storm hazards, but little is known about mature storm sensitivity to soil moisture in other climatic regions. Here we use a storm track dataset, satellite observations and reanalysis fields to investigate the response of mature storms to soil moisture in seven global storm hotspots—West Africa, India, South America, South Africa, Australia and the United States Great Plains. We demonstrate that mesoscale soil moisture gradients (~500 km) can enhance storms by driving increased vertical wind shear conditions, a crucial ingredient for storm organization, through the strengthening of atmospheric temperature gradients. This is evidenced by a 10–30% increase in precipitation feature size and rainfall for the largest storms on days with favourable soil moisture gradients compared with unfavourable gradients. Global simulations confirm that soil moisture gradients influence wind shear. The results demonstrate the importance of soil moisture feedbacks for accurate forecasting of mesoscale convective systems and future projections of extreme events under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Slow Wake Recovery and Low Turbulence Behind Wind Farms Parameterized in Mesoscale Simulations

Numerical weather prediction (NWP) and climate models equipped with wind-farm parameterizations (WFPs) can simulate cluster wake effects affecting downstream wind farms in both onshore and offshore environments. This study evaluates wake recovery behind a wind farm represented by the NWP-WFP approach in the Weather Research and Forecasting (WRF) model using either the Fitch et al. (2012) or Ma et al. (2022a, b) WFPs. Results are benchmarked against large-eddy simulations (LES) of an idealized offshore wind farm with aligned and staggered layouts under neutral atmospheric stability. Near-farm wake recovery is underestimated in NWP-WFP simulations due to its representation on a coarse mesoscale grid. This limitation leads to slow wake recovery through two interconnected mechanisms: (i) spatial gradients in the wind velocity field are weaker compared to LES and (ii) turbulence kinetic energy (TKE) remains low not because of excessive dissipation but due to insufficient shear production caused by these weakened gradients. For the scenario considered here, a wind-speed bias develops in the near-farm wake and persists into the far wake. Differences between the NWP-WFP simulations and LES emerge within a short distance downstream of the farm exit, where the mesoscale simulations recover too slowly. This reduced recovery contributes approximately 0.15-0.50 m s-1 to the near-farm wind-speed bias. The bias established in this region is not subsequently compensated for downstream but instead propagates into the far wake, where wind-speed differences of approximately 0.4-0.6 m s-1 remain up to 50 km downstream. Higher-resolution mesoscale simulations partially reduce this bias. Increasing turbine-added TKE or including subgrid wake effects provides additional improvement, but neither fully addresses the underlying cause. The slow wake recovery is not caused by limitations of the WFPs themselves, as it also occurs outside their region of influence, and adding subgrid wake effects does not significantly impact recovery. Rather, the slow wake recovery is a consequence of mesoscale flow representation. This behavior is not limited to regions downstream of the wind farm but is less visible within the farm, where wake recovery occurs simultaneously with turbine-induced momentum extraction. These results highlight the need for improved representations of wake recovery both within and downstream of wind farms. While enhanced subgrid modeling, shear-driven TKE production, and refined WFP formulations may improve intra-farm dynamics, accurately capturing near-farm wake recovery downstream remains challenging, as WFPs do not act in this region.

17 WIND ENERGY↗
DOE OSTI2024

West African Monsoon System’s Responses to Global Ocean–Regional Atmosphere Coupling

This study explores the added value (AV) of a regional Earth system model (ESM) compared to an atmosphere-only regional climate model (RCM) in simulating West African monsoon (WAM) rainfall. The primary goals are to foster discussions on the suitability of coupled RCMs for WAM projections and deepen our understanding of ocean–atmosphere coupling’s influence on the WAM system. The study employs results from dynamical downscaling of the ERA-Interim reanalysis and Max Plank Institute ESM, low resolution (MPI-ESM-LR), by two RCMs, atmosphere only (REMO) and REMO coupled with Max Planck Institute Ocean Model (MPIOM) (ROM), at ~25-km horizontal resolution. Results show that in regions distant from coupling domain boundaries such as West Africa (WA), constraint conditions from ERA-Interim are more beneficial than coupling effects. REMO, reliant on oceanic sea surface temperatures (SSTs) from observations and influenced by ERA-Interim, is biased under coupling conditions, although coupling offers potential advantages in representing heat and mass fluxes. Contrastingly, as intended, coupling improves SSTs and monsoon fluxes’ relationships under ESM-forced conditions. In this latter case, the coupling features a dipole-like spatial structure of AV, improving precipitation over the Guinea Coast but degrading precipitation over half of the Sahel. Our extensive examination of physical processes and mechanisms underpinning the WAM system supports the plausibility of AV. Additionally, we found that the monsoonal dynamics over the ocean respond to convective activity, with the Sahara–Sahel surface temperature gradient serving as the maintenance mechanism. While further efforts are needed to enhance the coupled RCM, we advocate for its use in the context of WAM rainfall forecasts and projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Radar Retrieval Evaluation and Investigation of Dendritic Growth Layer Polarimetric Signatures in a Winter Storm

Abstract This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed K dp and Z DR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using Z DR and K dp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting.

Meteorology & Atmospheric Sciences↗
DOE OSTI2026

Observations of Offshore Low‐Level Jets Off the U.S. East Coast Reveal Systematic Biases in ERA5 and HRRR

Low-level jets (LLJs)—wind speed maxima typically occurring a few hundred meters above the surface—are common off the U.S. East Coast and influence many atmospheric processes with societal importance, including cloud formation, aviation safety, and search-and-rescue. However, their vertical structure and frequency remain poorly quantified due to limited offshore observations. This study presents new scanning Doppler LiDAR and infrared spectroradiometer data from the 2024 summer deployment of an offshore barge during the Wind Forecast Improvement Project 3. These coupled wind and temperature profiles provide unprecedented resolution to assess LLJ behavior and model performance. LLJs occurred in over 21% of observed profiles, with a weak diurnal preference for nighttime and early morning hours and maximum winds typically near 300 m. Both ERA5 and High-Resolution Rapid Refresh analysis underestimate jet wind speeds and misrepresent the boundary layer thermal structure. These results highlight persistent model biases and the critical need for high-resolution offshore observations.

17 WIND ENERGY↗
DOE OSTI2025

Cloud-atmosphere impacts on the central Arctic surface energy budget (Final Report)

This project investigated how clouds and atmosphere-sea ice interactions shape the central Arctic surface energy budget using observations from the MOSAiC expedition and coordinated model experiments. Analyses focused on three themes. First, a new method applied to thermistor string data revealed that snow thermal conductivity is systematically higher than assumed in most models, strongly influencing conductive heat flux and sea ice growth, especially under clear skies. Second, a full annual cycle of surface energy budget observations, combined with detailed cloud microphysical retrievals, demonstrated how cloud regimes regulate radiative, turbulent, and conductive fluxes, driving seasonal contrasts in sea ice energy balance: in winter, radiative forcing elicits strong turbulent and conductive responses, while in summer excess energy is primarily partitioned into surface melt. Third, these observations were used to evaluate and improve regional and global forecast models. While some models captured key Arctic cloud-radiation states, most exhibited persistent deficiencies in representing liquid-containing clouds, snow-on-sea-ice processes, and flux responses to radiative forcing. Together, these results provide unprecedented benchmarks for understanding coupled Arctic processes, improve parameterizations of snow, sea ice, and cloud interactions, and highlight critical pathways by which atmospheric variability drives sea ice change in a rapidly evolving Arctic system. Lastly, the project was a resounding success, resulting in 38 peer-reviewed publications and dozens of presentations, while also supporting an early career scientist.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluation of WRF simulation of deep convection in the US Southern Great Plains

The Southern Great Plains (SGP) exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Here, seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) model applied at convection permitting resolution with the Mibrandt-Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization RADAR, gridded data sets and observations at the DoE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios >1 indicate WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mmhr –1 over the domain. Detailed analyses of the ten days with highest spatial coverage of cREF >30 dBZ show spatially similar reflectivity fields and high RR in both RADAR data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (> 25 mmhr –1 ) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Simulating Real Atmospheric Boundary Layers at Gray-Zone Resolutions: How Do Currently Available Turbulence Parameterizations Perform?

Recent computational and modeling advances have led a diverse modeling community to experiment with atmospheric boundary layer (ABL) simulations at subkilometer horizontal scales. Accurately parameterizing turbulence at these scales is a complex problem. The modeling solutions proposed to date are still in the development phase and remain largely unvalidated. This work assesses the performance of methods currently available in the Weather Research and Forecasting (WRF) model to represent ABL turbulence at a gray-zone grid spacing of 333 m. We consider three one-dimensional boundary layer parameterizations (MYNN, YSU and Shin-Hong) and coarse large-eddy simulations (LES). The reference dataset consists of five real-case simulations performed with WRF-LES nested down to 25 m. Results reveal that users should refrain from coarse LES and favor the scale-aware, Shin-Hong parameterization over traditional one-dimensional schemes. Overall, the spread in model performance is large for the cellular convection regime corresponding to the majority of our cases, with coarse LES overestimating turbulent energy across scales and YSU underestimating it and failing to reproduce its horizontal structure. Despite yielding the best results, the Shin-Hong scheme overestimates the effect of grid dependence on turbulent transport, highlighting the outstanding need for improved solutions to seamlessly parameterize turbulence across scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying the Impacts of Land Surface Modeling on Hub-Height Wind Speed under Different Soil Conditions

We investigate the impact of three land surface models (LSMs) on hub-height wind speed under three different soil regimes (dry, wet, and frozen) to understand and improve the physics of wind energy forecasts using the Weather Research and Forecasting (WRF) model. A six-day representative period is selected for each soil condition. The simulated wind speed, surface energy budget and soil properties are compared with the observations collected from the second Wind Forecast Improvement Project (WFIP2). For the selected cases, our simulation results suggest that, the impact of LSMs on hub-height wind speed are sensitive to the soil states but not so much to the choice of LSM. The simulated hub-height wind speed is in much better agreement with the observations for the dry soil case than the wet and frozen soil cases. Over the dry soil, there is a strong physical connection between the land surface and hub-height wind speed through near-surface turbulent mixing. Over the wet soil, the simulated hub-height wind speed is less impacted by land surface due to weaker surface fluxes and more dominated by large-scale synoptic disturbances. Over the frozen soil, the land surface model seems to have limited impact on hub-height wind speed variability due to the decoupling of the land surface with the overlying atmosphere. Two main sources of modeling uncertainties are proposed. The first are the insufficient model physics representing the surface energy budget, especially the ground heat flux, and the second are the inaccurate initial soil states such as soil temperature and soil moisture.

Xia, Geng↗
DOE OSTI2023

Examining the effects of soil entrainment during nuclear cloud rise on fallout predictions using a multiscale atmospheric modeling framework

Current operational models for nuclear cloud rise over land were developed and validated using observations from shallow-buried or surface detonations, where lofted soil quickly mixed with fission products from the detonation. These models poorly predict fallout from elevated detonations near the fallout-free height of burst (FFHOB), where interactions with the ground are limited and the mixing of fission products and lofted soil is incomplete. Fallout-free is a misnomer at this HOB, as fallout was observed in these cases, but was below the levels of concern, especially off-grounds of the nuclear test site. To correctly characterize and model fallout from detonations near the FFHOB, models must be developed which can capture the stratified nature of the particle and activity-size distributions within the cloud. Previously, it was shown that the Weather Research and Forecasting (WRF) model can accurately simulate nuclear cloud rise for airbursts with little to no ground interactions (Arthur et al., 2021). That work is expanded here by (1) using a radiation-hydrodynamics code to improve the fireball initialization in WRF, (2) further developing an aerosol package from WRF-Chem to simulate lofted soil, and (3) combining the WRF cloud rise simulations with the operational models used at the National Atmospheric Release Advisory Center (NARAC) for fallout modeling. Using this combination of codes, the Upshot-Knothole Grable detonation, which was just below the FFHOB, is simulated from seconds after detonation through cloud rise and fallout, and results are compared to historical test data. Here, the results show improved prediction of dose rate and highlight the need to correctly characterize the entrainment of material into the cloud and the subsequent mixing of fission products with entrained material.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗