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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Challenges and Opportunities in Magnetospheric Space Weather Prediction

Abstract Space Weather is the study of the dynamics of the coupled Solar‐Terrestrial environment, as these dynamics impact technological systems and human activity. This paper reviews a selection of the advances, challenges, and new opportunities for magnetospheric space weather, and while the focus is on specific phenomena, many other aspects of space weather will have similar challenges and needs. As a scientific field with direct applications, the field of space weather is partly driven by imperatives from both policy and operations. We provide an introduction to some of the context in which the field exists and discuss how this might shape future developments and norms within the space weather enterprise. We briefly examine benchmarking, as a policy and operationally driven activity, as it provides immediate societal relevance and an opportunity to stretch scientific understanding. As numerical space weather prediction now becomes routine, and exascale computing is in the near future, we identify challenges relating to computational expense and big data, capturing and accounting for uncertainties, and specification of boundary conditions. Here, as with the observations supporting numerical space weather prediction, the key challenge lies in extending the lead time of predictions. We also discuss the role of data, particularly in regard to model validation and empirical modeling. Due to the growing societal impact of space weather, we also examine the relationships between space weather and its terrestrial counterpart and look at the importance of continuous evaluation, monitoring progress in predictive capability, and communication with researchers, forecasters, and end users.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Quantifying Subsurface Flow and Solute Transport in a Snowmelt‐Recharged Hillslope With Multiyear Water Balance

Abstract Quantifying flow and transport from hillslopes is vital for understanding water quantity and quality in rivers, but remains obscure because of limited subsurface measurements. Using measured hydraulic conductivity K profiles and water balance over a single year to calibrate a transmissivity feedback model for a hillslope in the East River watershed (Colorado) proved unsatisfactory for predicting flow over the subsequent years. Well‐constrained field‐scale K were obtained by optimizing subsurface flux predictions over years having large differences in recharge, and by including estimates of interannual transfer of excess snowmelt recharge. Water and solute exports during high snowmelt recharge occur predominantly via shallow groundwater flow through weathered rock and soil because of their enlarged transmissivities under saturated conditions. Conversely, these shallow pathways are less active in snow drought years when the water table remains deeper within the weathering zone. Hillslope soil water monitoring showed that rainfall does not infiltrate deeply during summer and fall months, and revealed water losses consistent with model ET predictions. By combining water table‐dependent fluxes with pore water chemistry in different zones, time‐dependent rates of solute exports become predictable. As an example, calibrated K were combined with dissolved nitrogen concentrations in pore waters to show the snowmelt‐dependence of reactive nitrogen exported from the hillslope, further supporting the recent finding that the weathering zone is the dominant source of reactive nitrogen at this site. Subsurface export predictions can now be obtained for wide ranges of recharge based on measurements of water table elevation and profiles of pore water chemistry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dynamically Downscaled (WRF) 1km, Hourly Meteorological Conditions 1987-2020. East/Taylor Watersheds

This dataset contains meteorological output from the Weather Research and Forecasting (WRF) version 3.8.1. This dataset has been created to 1) investigate hydrometeorological processes impacting the East River and water-delivery to the Critical Zone, and 2) provide meteorological forcing data for distributed Earth-science modeling applications in the East River watershed. Variables have a 1 kilometer spatial resolution and hourly temporal resolution and encompass a rectangular region encompassing the East and Taylor River watersheds, Colorado, near the town of Crested Butte. WRF was forced using Climate Forecast System Reanalysis (CFSR) lateral boundary conditions. Each .zip file contains one "water year" of data (October 1 -- September 30; i.e. water year 2017 starts October 1, 2016 and ends September 30, 2017). Each zip folder contains 12 netcdf (.nc) files containing one month of hourly data each and are approximately 250mb. Model timestamps are in UTC time.The files contain the following data variables:EAST_MASK: binary mask of the watershed regionTAYLOR_MASK: binary mask of the watershed regionGLW downwelling longwave radiation (w/m2)HR_PRCP: Hourly Precipitation Rate (mm/hr). Includes all hydrometeors (solid+liquid). HFX NoahMP LSM total grid-cell modelled sensible heat flux (w/m2) [positive towards atmosphere]LH NoahMP LSM total grid-cell modelled latent heat flux (w/m2) [positive towards atmosphere; can be converted to ET]PSFC Surface Barometric Pressure (hPa)Q2 Two-meter specific humidity (kg/kg)SWDOWN Downwelling shortwave solar radiation (w/m2)SWNORM Terrain-normal downwelling shortwave radiation (w/m2)T2 Two-meter air temperature (deg K)U10 10-m U-component of wind velocity (m/s)V10 10m V-component of wind velocity (m/s)XLAT Latitude of grid-center point XLONG Longitude of grid-center point XTIME Model timestamp, **in UTC**

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Polymers in direct air capture: a mini review

The urgent need to mitigate climate change has intensified interest in direct air capture (DAC) technology, which targets extracting carbon dioxide (CO2) directly from the atmosphere. Among the various sorbents used in DAC, polymers have emerged as a promising solution, either as active sorbents or as structural supports for active DAC materials, due to their customizable properties, scalability and low cost. This mini-review investigates the latest trends in polymer-based materials for DAC and identifies critical research gaps, such as the need for thorough lifecycle assessments and in-depth studies on the degradation of polymeric materials. It also outlines future directions, emphasizing the importance of developing cost-effective, scalable and durable polymers that can perform efficiently across diverse climatic conditions, including the unique challenges presented by cold weather regions abundant in renewable energy. This mini-review aims to inform ongoing efforts in the design and utilization of polymeric sorbents, providing insights that could guide the development of economically viable and environmentally sustainable DAC technologies.

climate change↗
DOE OSTI2023

Wave climate and energy resources in the Mariana Islands from a 42-year high-resolution hindcast

We report a high-resolution wave hindcast from 1979 to 2020 was developed for Guam and the Commonwealth of the Northern Mariana Islands, which are influenced by mesoscale and synoptic weather systems from the western Pacific Rim to the Central Pacific. The spectral wave modeling utilized structured global and unstructured regional grids with improved resolution in nearshore waters, where wave resources development is most feasible. The hindcast demonstrates its capability in capturing multi-modal seas through reproduction of bulk and partitioned wave parameters derived from altimetry and buoy measurements. The local wave climate is influenced by the year-round trade winds and tropical cyclones as well as seasonal monsoons, frontal systems, and mid-latitude high- and low-pressure systems. The strong intra-annual variation of the significant wave height and power flux reflects transitions between local and distant weather systems, which account for the diverse wave conditions comprised of short-period wind waves, intermediate-period seas, and long-period swells around the islands. Among the Pacific island regions, spectral partitioning is particularly important for describing the dominant wave components by period and direction with pertinent information conducive to energy resources characterization and coastal zone management in the Mariana Islands.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Thermal shock resistance of multilayer silicon carbide receiver tubes for 800°C molten salt concentrating solar power application

CSP power tower receiver systems during rapid transient weather periods can be vulnerable to thermal shock conditions from rain that which can facilitate the onset of leaks and failures that can have catastrophic consequences. Silicon carbide (SiC) materials have attractive receiver application characteristics for being light weight, having high-strength and excellent thermal shock resistance performance which make them a particularly good fit for receiver absorber materials in CSP. In this investigation, the performance characteristics of Ceramic Tubular Products (CTP) SiC ceramic matrix composite (CMC), multilayered tubes were explored with respect to thermal shock performance for solar receiver applications in next generation CSP plants. Here, thermal shock testing was performed at the Sandia National Laboratories (SNL) Solar Furnace facility using a dynamic stage and thermal shock tube test setup. The tubes tested under incident solar heat flux of 100 W/cm2 were heated with inner tube temperatures reaching approximately 800 °C, with outer temperatures exceeding or just reaching 1000 El for the multilayer and monolithic SiC tubes respectively. The tubes were then quenched with simulated rain. The tubes were then cooled and subjected to hoop stress analysis using an Instron device to assess their subsequent mechanical strength. Furthermore, the on-sun study experimental results indicate an average of 24.2% and 97% higher hoop strength for the CMC tubes than those composed of monolithic SiC and aluminum oxide (A1203) respectively.

14 SOLAR ENERGY↗
DOE OSTI2023

Projecting Future Energy Production from Operating Wind Farms in North America. Part III: Variability

Abstract Daily expected wind power production from operating wind farms across North America are used to evaluate capacity factors (CF) computed using simulation output from the Weather Research and Forecasting (WRF) Model and to condition statistical models linking atmospheric conditions to electricity production. In Parts I and II of this work, we focus on making projections of annual energy production and the occurrence of electrical production drought. Here, we extend evaluation of the CF projections for sites in the Northeast, Midwest, southern Great Plains (SGP), and southwest U.S. coast (SWC) using statewide wind-generated electricity supply to the grid. We then quantify changes in the time scales of CF variability and the seasonality. Currently, wind-generated electricity is lowest in summer in each region except SWC, which causes a substantial mismatch with electricity demand. While electricity of residential heating may shift demand, research presented here suggests that summertime CF are likely to decline, potentially exacerbating the offset between seasonal peak power production and current load. The reduction in summertime CF is manifest for all regions except the SGP and appears to be linked to a reduction in synoptic-scale variability. Using fulfillment of 50% and 90% of annual energy production to quantify interannual variability, it is shown that wind power production exhibits higher (earlier fulfillment) or lower (later fulfillment) production for periods of over 10–30 years as a result of the action of internal climate modes. Significance Statement Electrical power system reassessment and redesign may be needed to aid efficient increased use of variable renewables in the generation of electricity. Currently wind-generated electricity in many regions of North America exhibits a minimum in summertime and hence is not well synchronized with electricity demand, which tends to be maximized in summer. Future projections indicate evidence of reductions in wind power during summer that would amplify this offset. However, electrification of heating may lead to increased wintertime demand, which would lead to greater synchronization.

Coburn, Jacob↗
DOE OSTI2022

Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review

Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. Here, this article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A modeling examination of cloud seeding conditions under the warmer climate in Utah, USA

Assessment of how the wintertime cloud seeding conditions over Utah's mountains may change provides essential information for the state to work on increasing its snowpack in the warmer climate. Here in this study, data from the National Center for Atmospheric Research's high-resolution, convection-permitting Weather Research and Forecasting model are used to evaluate suitable conditions for wintertime cloud seeding over Utah's mountains, and to estimate future changes to these conditions under a pseudo-global warming scenario. While previous studies have examined historical cloud seeding conditions, this is the first study that explores the impact of future climate change on these conditions. Based on two atmospheric variables commonly used in cloud-seeding operations, i.e. average temperature from the surface to 1 km above ground level (-20 °C≤T≤-6 °C) and vertically integrated supercooled liquid water (SLW > 0.01 mm), Utah's mountains are suitable for seeding more than 20% of the time during winter. A greater rate of suitability exists in the northern and Uinta Mountains. In the warmer climate under a high-emissions scenario, these seeding conditions may wane as the percentage of precipitating clouds suitable for seeding, about 60% under current conditions, would decline to about 40% across the state with significance. This projected decrease is due to rising temperatures and a decreased frequency of precipitation events. These findings imply that climate warming will narrow the window of opportunity for winter cloud seeding operation in Utah.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗
DOE OSTI2025

Granger causal inference for climate change attribution

Abstract Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and focus on different types of weather and climate events and each of which are conditional to varying extents. Some methods are based on Pearl causality (direct experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses relative to Pearl-causal attribution and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.

Risser, Mark D. (ORCID:0000000319561783)↗
DOE OSTI2021

Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world.

14 SOLAR ENERGY↗
DOE OSTI2026

Dynamic Transmission Line Switching Amid Wildfire-Prone Weather Under Decision-Dependent Uncertainty

During dry and windy seasons, environmental conditions significantly increase the risk of wildfires, exposing power grids to disruptions caused by transmission line failures. Wildfire propagation exacerbates grid vulnerability, potentially leading to prolonged power outages. To address this challenge, we propose a multistage optimization model that dynamically adjusts transmission grid topology in response to wildfire propagation, aiming to develop an optimal response policy. By accounting for decision-dependent uncertainty, where line survival probabilities depend on usage, we employ distributionally robust optimization to model uncertainty in line survival distributions. We adapt the stochastic nested decomposition algorithm and derive a deterministic upper bound for its finite convergence. To enhance computational efficiency, we exploit the Lagrangian dual problem structure for a faster generation of Lagrangian cuts. Using realistic data from the California transmission grid, we demonstrate the superior performance of dynamic response policies against two-stage alternatives through a comprehensive case study. In addition, after solving the multistage formulation, we construct easy-to-implement policies that significantly reduce computational burden while maintaining good performance in real-time deployment. History: Accepted by Russell Bent, Area Editor for Network Optimization: Algorithms and Applications. Funding: This work was supported by the U.S. Department of Energy, Office of Electricity [Grant DE-AC02-05CH11231]. The work of R. Jiang was supported in part by the U.S. National Science Foundation, Division of Electrical, Communications and Cyber Systems [Grant ECCS-1845980] and the U.S. Air Force Office of Scientific Research [Grant FA9550-23-1-0323]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2025.1210 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1210 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Estrada-Garcia, Juan-Alberto↗
DOE OSTI2021

A Retrospective and Prospective Examination of the 1960s U.S. Northeast Drought

As the most severe drought over the Northeastern United States (NEUS) in the past century, the 1960s drought had pronounced socioeconomic impacts. Although it was followed by a persisting wet period, the conditions leading to the 1960s extreme drought could return in the future, along with its challenges to water management. To project the potential consequences of such a future drought, pseudo-global warming simulations using the Weather Research and Forecasting Model are performed to simulate the dynamical conditions of the historical 1960s drought, but with modified thermodynamic conditions under the shared socioeconomic pathway SSP585 scenario in the early (2021–2027), middle (2041–2047), and late (2091–2097) 21st century. Our analysis focuses on essential hydroclimatic variables including temperature, precipitation, evapotranspiration, soil moisture, snowpack, and surface runoff. In contrast to the historical 1960s drought, similar dynamical conditions will generally produce more precipitation, increased soil moisture and evapotranspiration, and reduced snowpack. However, we also find that although wet months get much wetter, dry months may become drier, meaning that wetting trends are most significant in wet months but are essentially negligible for extremely dry months with negative monthly mean net precipitation. For these months, the trend toward wetting conditions provides little relief from the effects of extreme dry months. These conditions may even aggravate water shortages due to an increasingly rapid transition from wet to dry conditions. Other challenges emerge for residents and stakeholders in this region, including more extreme hot days, record-low snow pack, frozen ground degradation and subsequent decreases in surface runoff.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

COMBLE-Isotopic Links to Atmospheric Water’s Sources (ISLAS) Snow Sampling 2020 Field Campaign Report

Within the European Research Council (ERC)-funded project entitled Isotopic Links to Atmospheric Water’s Sources (ISLAS), researchers from the University of Bergen, Norway, used the stable isotope composition of atmospheric water vapor and precipitation to obtain information about the sources and transport processes of water vapor within arctic weather systems. In particular, during cold-air outbreak conditions, evaporation and precipitation occur in close proximity within the study area. With evaporation studied at a single location (Ny-Ålesund), ISLAS researchers wanted to be able to sample the resulting precipitation at several downstream locations. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) site was one of those locations, included in an entire network of precipitation sampling stations reaching from Ny-Ålesund, where water vapor isotope measurements are conducted near the surface, to Longyearbyen, Tromsø, Andenes, Ålesund, and Bergen. Daily precipitation samples, enhanced by more frequent sampling during intense operational periods (IOPs), were conducted during four weeks from 20 February to 20 March 2020. The contribution from the ARM mobile facility (AMF) observatory, deployed at ANX (Andenes, Norway) during COMBLE, was valuable to understand the spatial coherence of water isotope signals from arctic and extratropical weather systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Graph-based Simulation Framework for Power Resilience Estimation and Enhancement

The increasing frequency of extreme weather events poses significant risks to power distribution systems, leading to widespread outages and severe economic and social consequences. This paper presents a novel simulation framework for assessing and enhancing the resilience of power distribution networks under such conditions. Resilience is estimated through Monte Carlo simulations, which simulate extreme weather scenarios and evaluate the impact on infrastructure fragility. Due to the proprietary nature of power network topology, a distribution network is synthesized using publicly available data. To generate the weather scenarios, an extreme weather generation method is developed. To enhance resilience, renewable resources such as solar panels and energy storage systems (batteries in this study) are incorporated. A customized Genetic Algorithm is proposed to determine the optimal locations and capacities for solar panels and battery installations, maximizing resilience while balancing cost constraints. Experiment results demonstrate that on a large-scale synthetic distribution network with more than 300,000 nodes and 300,000 edges, the proposed framework can efficiently evaluate the resilience, and enhance the resilience through the installations of distributed energy resources (DERs), providing utilities with valuable insights for community-level power system resilience estimation and enhancement.

Wang, Xuesong [Wayne State Univ., Detroit, MI (Uni↗
DOE OSTI2022

The Application of a Genetic Algorithm to the Optimization of a Mesoscale Model for Emergency Response

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such “free” model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process—the genetic algorithm (GA)—has become especially popular. As applied to modeling, GAs represent a Darwinian process: an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as “parents” who pass their parameters onto a new generation. At the U.S. Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to “score” the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions. Significance Statement We wanted to develop a forecasting system by which a weather model is run over the Savannah River Site each day and repeatedly adjusted according to how well it performed the previous day. To run the model, a series of values (parameters) must be set to control how the model will calculate winds, temperatures, and other desired variables. Each day the model was run several times using different combinations of these parameters and later compared with observed meteorological conditions. Parameters that produced the most accurate forecasts were preferentially reused to create the forecasts for the next day. The process was tested for the summer of 2020 and exhibited lower errors than forecasts produced by the model using default values of the parameters.

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↗