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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL’s 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis of the causes of high wind speed bias in the 20-year wind resource data set for offshore California that the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California Outer Continental Shelf, near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a 1-year period.

17 WIND ENERGY↗
DOE OSTI2020

Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-based three-dimensional ensemble-variational (GSI-3DEnVar) hybrid data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble Kalman filtering data assimilation system. In addition, our proposed initialization framework discards the empirical intensity correction in the vortex initialization package that is employed by the GSI-3DEnVar initialization framework in operational HWRF. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, minimum sea level pressure (MSLP), and maximum surface wind (MSW) forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. With assimilating high-resolution observations within the hurricane inner-core region, GSI-4DEnVar can produce the initial hurricane intensity reasonably well without the empirical vortex intensity correction. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Urbanization-induced land and aerosol impacts on sea-breeze circulation and convective precipitation

Abstract. Changes in land cover and aerosols resulting from urbanization may impact convective clouds and precipitation. Here we investigate how Houston urbanization can modify sea-breeze-induced convective cloud and precipitation through the urban land effect and anthropogenic aerosol effect. The simulations are carried out with the Chemistry version of the Weather Research and Forecasting model (WRF-Chem), which is coupled with spectral-bin microphysics (SBM) and the multilayer urban model with a building energy model (BEM-BEP). We find that Houston urbanization (the joint effect of both urban land and anthropogenic aerosols) notably enhances storm intensity (by ∼ 75 % in maximum vertical velocity) and precipitation intensity (up to 45 %), with the anthropogenic aerosol effect more significant than the urban land effect. Urban land effect modifies convective evolution: speed up the transition from the warm cloud to mixed-phase cloud, thus initiating surface rain earlier but slowing down the convective cell dissipation, all of which result from urban heating-induced stronger sea-breeze circulation. The anthropogenic aerosol effect becomes evident after the cloud evolves into the mixed-phase cloud, accelerating the development of storm from the mixed-phase cloud to deep cloud by ∼ 40 min. Through aerosol–cloud interaction (ACI), aerosols boost convective intensity and precipitation mainly by activating numerous ultrafine particles at the mixed-phase and deep cloud stages. This work shows the importance of considering both the urban land and anthropogenic aerosol effects for understanding urbanization effects on convective clouds and precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5 ) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µg m –3 for nearly 2 weeks, with localized peaks exceeding 300 µg m –3 . Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5 . Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impacts of long-range-transported mineral dust on summertime convective cloud and precipitation: a case study over the Taiwan region

As one of the most abundant atmospheric aerosols and effective ice nuclei, mineral dust affects clouds and precipitation in the Earth system. Here numerical experiments are carried out to investigate the impacts of dust aerosols on summertime convective clouds and precipitation over the mountainous region of Taiwan by acting as ice-nucleating particles. We run the Weather Research and Forecasting model (WRF) with the Morrison two-moment and spectral-bin microphysics (SBM) schemes at 3 km resolution, using dust number concentrations from a global chemical transport model (GEOS-Chem-APM). The case study indicates that the long-range-transported mineral dust, with relatively low number concentrations, can notably affect the properties of convective clouds (ice and liquid water contents, cloud top height, and cloud coverage) and precipitation (spatial pattern and intensity). The effects of dust are evident during strong convective periods, with significantly increased ice water contents in the mixed-phase regime via the enhanced heterogeneous freezing. With both the Morrison and SBM schemes, we see the invigoration effects of dust aerosols on the convective intensity through enhanced condensation and deposition latent heating. The low-altitude dust particles are uplifted to the freezing level by updrafts, which, in turn, enhance the convective cloud development through immersion freezing and convective invigoration. Compared to the Morrison scheme, the SBM scheme predicts more realistic precipitation and different invigoration effects of dust. The differences are partially attributed to the saturation adjustment approach utilized in the bulk scheme, which leads to a stronger enhancement of condensation at midlatitudes to low altitudes and a weaker deposition increase at the upper level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Increased importance of aerosol–cloud interactions for surface PM 2.5 pollution relative to aerosol–radiation interactions in China with the anthropogenic emission reductions

Surface fine particulate matter (PM 2.5 ) pollution can be enhanced by feedback processes induced by aerosol–radiation interactions (ARIs) and aerosol–cloud interactions (ACIs). Many previous studies have reported enhanced PM 2.5 concentrations induced by ARIs and ACIs for episodic events in China. However, few studies have examined the changes in the ARI- and ACI-induced PM 2.5 enhancements over a long period, though the anthropogenic emissions have changed substantially in the last decade. In this study, we quantify the ARI- and ACI-induced PM 2.5 changes for 2013–2021 under different meteorology and emission scenarios using the Weather Research and Forecasting model with Chemistry (WRF-Chem), and we investigate the driving factors behind the changes. Our results show that, in January 2013, when China suffered from the worst PM 2.5 pollution, the PM 2.5 enhancement induced by ARIs in eastern China (5.59 µg m −3 ) was larger than that induced by ACIs (3.96 µg m −3 ). However, the ACI-induced PM 2.5 enhancement showed a significantly smaller decrease ratio (51 %) than the ARI-induced enhancement (75 %) for 2013–2021, making ACIs more important for enhancing PM 2.5 concentrations in January 2021. Our analyses suggest that the anthropogenic emission reductions played a key role in this shift. Owing to only anthropogenic emission reductions, the ACI-induced PM 2.5 enhancement decreased by 43 % in January, which was lower than the decrease ratio of the ARI-induced enhancement (57 %). The relative change in ARI- and ACI-induced PM 2.5 enhancement in July was similar to the pattern observed in January, caused by anthropogenic emission reductions. The primary reason for this phenomenon is that the decrease in ambient PM 2.5 for 2013–2021 caused a disproportionately small decrease in the liquid water path (LWP) and an increase in the cloud effective radius (Re) under the condition of high PM 2.5 concentrations. Therefore, the surface solar radiation attenuation (and, hence, the boundary layer height reduction) caused by ACIs decreased slower than that caused by ARIs. Moreover, the lower decrease ratio of the ACI-induced PM 2.5 enhancement was dominated by the lower decrease ratio of ACI-induced secondary PM 2.5 component enhancement, which was additionally caused by the smaller decrease ratio of the air temperature reduction and the relative humidity (RH) increase. Our findings indicate that, with the decrease in ambient PM 2.5 , the ACI-induced PM 2.5 enhancement inevitably becomes more important. This needs to be considered in the formulation of control policies to meet the national PM 2.5 air quality standard.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Implications of Sea Breeze Circulations on boundary layer aerosols in the southern coastal Texas region

The Sea Breeze Circulation (SBC) influences atmospheric processes at multiple scales in coastal regions. Understanding how SBCs impact the aerosol number budget and aerosol–cloud interaction processes is essential. This study investigates sea breeze–aerosol interactions (SAIs) during 46 summertime SBC events using data from the TRacking Aerosol Convection Interactions Experiment (TRACER) field campaign across urban (main) and rural (supplemental) coastal sites in southern Texas. Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) simulations complement observations to explore spatio-temporal meteorological controls on boundary layer aerosols. During the summertime, Sea Breeze Fronts (SBF) penetrating inland transported cool, moist air over the land, introducing air masses with distinct properties compared to the preexisting continental air. These SAIs cause variability in number concentrations of up to a factor of two, with events typically lasting ∼ 5 h before returning to background conditions. SAI impact on aerosols varies with site proximity to water and the preceding sea breeze (SB) history, primarily affecting the marine-influenced accumulation mode. The main site, influenced by both Galveston Bay and the Gulf of Mexico, reflects a stronger marine influence. In contrast, a supplemental site, at a similar shoreline distance but exposed only to the Gulf of Mexico and typically upstream of the urban core, samples SB air that has traversed land and partially regained continental characteristics. Simulations show that the regional SAIs extend ∼ 50 km inland and reach up to the boundary layer height. SAIs further decrease cloud condensation nuclei relevant aerosol number concentrations in ∼ 20 % of events during SBF passage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS

Abstract. This study analyzes the quality of simulated historical precipitation across the contiguous United States (CONUS) in a 12 km Weather Research and Forecasting model version 4.2.1 (WRF v 4.2.1)-based dynamical downscaling of the fifth-generation ECMWF atmospheric reanalysis (ERA5). This work addresses the following questions. First, how well are the 3 and 24 h precipitation characteristics (diurnal and annual cycles, precipitation frequency, annual and seasonal mean and maximum precipitation, and distribution of seasonal maximum precipitation) represented in the downscaled simulation, compared to ERA5? And second, how does the performance of the simulated WRF precipitation vary across seasons, regions, and timescales? Performance is measured against the National Centers for Environmental Prediction/Environmental Modeling Center (NCEP/EMC) 4 km Stage IV and Oregon State University Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data on 3 and 24 h timescales, respectively. Our analysis suggests that the 12 km WRF exhibits biases typically found in other WRF simulations, including those at convection-permitting scales. In particular, WRF simulates both the timing and magnitude of the summer diurnal precipitation peak as well as ERA5 over most of the CONUS, except for a delayed diurnal peak over the Great Plains. As compared to ERA5, both the month and the magnitude of the precipitation peak annual cycle are remarkably improved in the downscaled WRF simulation. WRF slightly overestimates 3 and 24 h precipitation maximum over the CONUS, in contrast to ERA5, which generally underestimates these quantities mainly over the eastern half of the CONUS. Notably, WRF better captures the probability density distribution (PDF) of 3 and 24 h annual and seasonal maximum precipitation. WRF exhibits seasonally dependent precipitation biases across the CONUS, while ERA5's biases are relatively consistent year round over most of the CONUS. These results suggest that dynamical downscaling to a higher resolution improves upon some precipitation metrics but is susceptible to common regional climate model biases. Consequently, if used as input data for domain-specific models, we suggest moderate bias correction be applied to the dynamically downscaled product.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Idealized simulations of wind farm interactions with intermittent turbulence in stable boundary layer conditions

Stable atmospheric boundary layer conditions typically correspond to weak turbulence levels, but intermittent periods of elevated turbulence can occur during otherwise quiescent conditions. The interaction between intermittent turbulence and wind turbines is not well understood because of sparse observations, as well as the difficulty in realistically resolving small-scale turbulence during strongly stable conditions with numerical simulations. In this study, an explicit filtering and reconstruction approach for large-eddy simulation (LES) is used to simulate weakly and strongly stable conditions, with surface cooling rates of −0.2 and −2.0 K h −1 , respectively. This approach can sustain resolved background turbulence at relatively coarse grid spacing and stronger stratification compared to conventional closures, permitting more realistic intermittent stable boundary layer (SBL) turbulence. The idealized LES capability of the Weather Research and Forecasting model is employed with turbine rotors parameterized using generalized actuator disks to examine (1) how the presence of turbine wakes affects SBL evolution and (2) the effect of intermittent turbulence on power production and wake recovery. Wakes increase mixing and deepen the SBL, with a stronger effect under strongly stable conditions, primarily because the SBL is shallower and closer to the top of the wind turbine rotor layer. Intermittent turbulence does not have a significant impact on mean power generation and wake recovery because the relevant intermittent turbulent structures in this study only affect the bottom half of the rotor disk. Power production is, however, more variable during periods of elevated turbulence, demonstrating the impact of SBL intermittency. This study uses an idealized configuration, focusing on LES model performance and physical understanding, with the goal of informing future simulations of the conditions observed during the American Wake Experiment.

Energy - Wind↗
DOE OSTI2021

Quantifying wind plant blockage under stable atmospheric conditions

Wind plant blockage reduces the wind velocity upstream undermining turbine performance for the first row of the plant. We assess how atmospheric stability modifies the induction zone of a wind plant in flat terrain. We also explore different approaches to quantifying the magnitude and extent of the induction zone from field-like observations. To investigate the influence from atmospheric stability, we compare simulations of two stable boundary layers using the Weather Research and Forecasting model in large-eddy simulation mode, representing wind turbines using the generalized actuator disk approach. We find a faster cooling rate at the surface, which produces a stronger stably stratified boundary layer, amplifies the induction zone of both an isolated turbine and of a large wind plant. A statistical analysis on the hub-height wind speed field shows wind slowdowns only extend far upstream (up to 15D) of a wind plant in strong stable boundary layers. To evaluate different ways of measuring wind plant blockage from field-like observations, we consider various ways of estimating the freestream velocity upstream of the plant. Sampling a large area upstream is the most accurate approach to estimating the freestream conditions, and thus of measuring the blockage effect. Also, the choice of sampling method may induce errors of the same order as the velocity deficit in the induction zone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers

Abstract. Wind plants slow down the approaching wind, a phenomenon known as blockage. Wind plant blockage undermines turbine performance for front-row turbines and potentially for turbines deeper into the array. We use large-eddy simulations to characterize blockage upstream of a finite-size wind plant in flat terrain for different atmospheric stability conditions and investigate the physical mechanisms modifying the flow upstream of the turbines. To examine the influence of atmospheric stability, we compare simulations of two stably stratified boundary layers using the Weather Research and Forecasting model in large-eddy simulation mode, representing wind turbines using the generalized actuator disk approach. For a wind plant, a faster cooling rate at the surface, which produces stronger stably stratified flow in the boundary layer, amplifies blockage. As a novelty, we investigate the physical mechanisms amplifying blockage by evaluating the different terms in the momentum conservation equation within the turbine rotor layer. The velocity deceleration upstream of a wind plant is caused by an adverse pressure gradient and momentum advection out of the turbine rotor layer. The cumulative deceleration of the flow upstream of the front-row turbines instigates vertical motions. The horizontal flow is diverted vertically, reducing momentum availability in the turbine rotor layer. Although the adverse pressure gradient upstream of the wind plant remains unchanged with atmospheric stability, vertical advection of horizontal momentum is amplified in the more strongly stable boundary layer, mainly by larger shear of the horizontal velocity, thus increasing the blockage effect.

17 WIND ENERGY↗
DOE OSTI2020

Stochastic Mixing WRF Setup

Documents describing how to set up simulations using a novel stochastic mixing scheme within the Weather Research and Forecasting model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Understanding Low Level Jets in the US Atlantic Offshore

Low level jets (LLJs) in the atmosphere exhibit a local windspeed maximum in the boundary layer, with positive shear beneath the jet and negative shear above the jet. Wind turbines tend to experience increased loads with varying wake recovery characteristics in the presence of an LLJ, therefore understanding the mechanism and impact of LLJs is crucial to wind energy development. The US Mid-Atlantic offshore region is a huge potential wind energy resource, yet LLJs in this area are poorly understood. In particular, the coastal offshore environment does not exhibit the same diurnal cycle that leads to strong LLJs in the well-characterized Great Plains region. In this study, we use the Weather Research and Forecasting model (WRF) plus lidar buoy data to identify case studies for LLJ events in 2020 in the New York Bight. A reduced order model is presented to explain the onset of these events based on the competing effects of baroclinicity and eddy diffusivity. Finally, using the macro-scale WRF, we drive a micro-scale large eddy simulation (LES) to generate a more detailed characterization of the Marine Boundary Layer during an LLJ. Gaining a better understanding of LLJs and their impacts on offshore wind in the mid-Atlantic is crucial for a transition toward renewable energy.

atmospheric boundary layer↗
DOE OSTI2022

Mechanisms of Low-Level Jet Formation in the U.S. Mid-Atlantic Offshore

Low level jets (LLJs) in the atmosphere exhibit a local windspeed maximum inthe boundary layer and are commonly observed both over land and in coastal environments. Because LLJs present strong positive shear beneath the maximum (or "jet nose") and negative shear above the nose, they pose a challenge to future offshore wind technology through their impacts on turbine performance and wakes. Summertime LLJs in the Great Plains have been attributed to frictional decoupling triggering an inertial oscillation, whilecoastal LLJs in California or the North Sea have additional driving mechanisms such as baroclinic forcing. By comparison, LLJs in the coastal US Mid-Atlantic have received less attention, with conflicting evidence about the mechanism that triggers these high shear events. Motivated by future wind energy development in the region, this work elucidates the atmospheric mechanisms of LLJ formation in the US Mid-Atlantic. Using observational data from two floating LiDAR buoys deployed bythe New York State Energy Research and Development Authority in the NY Bight, we identify four sustained LLJ events whose windspeeds and directions are well predicted by a WRF simulation (Weather Research and Forecasting Model). From analysis of the WRF data, we find that the four case studies provide evidence of concurrent inertial oscillation, reduced vertical mixing, and baroclinicity. In addition, we use simple atmospheric dynamics and a single-column-model to compare the relative contribution of each of these mechanisms to triggering the LLJ. By improving understanding and predictability of coastal mid-Atlantic LLJs, this work reduces uncertaintiesof wind energy deployment in the region, aiding the US's transition toward renewable energy.

atmospheric boundary layers↗
DOE OSTI2023

Topological Guided Detection of Extreme Wind Phenomena: Implications for Wind Energy: Preprint

Extreme wind phenomena play a crucial role in the efficient operation of wind farms for renewable energy generation. However, existing detection methods are computationally expensive, limited to specific coordinate. In real-world scenarios, understanding the occurrence of these phenomena over a large area is essential. Therefore, there is a significant demand for a fast and accurate approach to forecast such events. In this paper, we propose a novel method for detecting wind phenomena using topological analysis, leveraging the gradient of wind speed or critical points in a topological framework. By extracting topological features from the wind speed profile within a defined region, we employ topological distance to identify extreme wind phenomena. Our results demonstrate the effectiveness of utilizing topological features derived from regional wind speed profiles. We validate our approach using high-resolution simulations with the Weather Research and Forecasting model (WRF) over a month in the US East Coast.

MATHEMATICS AND COMPUTING,WIND ENERGY↗
DOE OSTI2022

A Machine‐Learning‐Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection

Abstract A machine‐learning‐assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in the cloud‐base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and the associated cloud‐base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud‐base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden‐layer neural network trained by the evolution of convective cell size distributions in a 1‐km grid‐spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud‐base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and midlatitude domains to test this as a potential approach to scale‐aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden‐Julian Oscillation while maintaining realistic depictions of the diurnal cycle over both land and ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Improved Representations of Land‐Atmosphere Interactions Over the Continental U.S. Through Dynamic Root Modeling

Previous studies have identified the oversimplified root system representation as a key factor leading to inaccuracies in vegetation‐atmosphere feedbacks. In this study, a dynamic root water uptake scheme in the Noah‐MP land surface model has been coupled to the Weather Research and Forecasting (WRF) model to investigate its impact on the surface hydroclimate variables and land‐atmosphere interactions. To evaluate the impact of the dynamic root, two coupled simulations were conducted, one with the dynamic root water uptake scheme (DynRt) and one with the static root water uptake scheme (StcRt), which is based on the default root representation in Noah‐MP, with slight modifications, primarily in vegetation‐related parameters. Both DynRt and StcRt simulations were conducted with a small ensemble of three members to account for variations in physical parameterizations, initial and boundary forcing and model setup. When compared with reference data sets, the DynRt simulations show improved results than the StcRt simulations, reducing biases in the simulated leaf area index, surface energy fluxes, soil moisture and precipitation. Two different mechanisms through which roots affect land‐atmosphere coupling have been identified. Over the transitional climate zone between the dry and wet climate, the dynamic root scheme affects surface climate and land‐atmosphere coupling mainly through changes in soil moisture through hydraulic redistribution by plant root system. Over the energy‐limited mesic zone, the dynamic root affects regional land‐atmosphere coupling mainly through changes in carbon allocation. This work highlights the importance of dynamic root representation in improving vegetation‐atmosphere simulations by enhancing predictions of water, energy, and carbon fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High-Resolution Wind Resource Data Set of the Greater Puerto Rico Region

In February 2022, the U.S. Department of Energy and six national laboratories launched the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100). PR100 aims to provide a comprehensive analysis of possible pathways for Puerto Rico's energy future, with a goal of 100% renewable energy by 2050. As a part of the renewable energy potential assessment in this project, we developed 20 years (2001-2020) of data using a numerical weather prediction (NWP) model for onshore and offshore wind resource assessment for the Puerto Rico. The research steps in developing the long-term wind resource data sets based on the NWP model were: 1. Model wind resource based on the Weather Research and Forecasting (WRF) model. 2. Develop WRF model configurations for Puerto Rico. 3. Test WRF with 11 different physics parameterizations for planetary boundary layer (PBL). 4. Assess WRF output from the different PBL schemes against observations. 5. Select a final model configuration which can produce the modeled wind speed with sufficient accuracy. 6. Produce 20 years wind resource data sets for Puerto Rico region. In the first stage of our framework for developing wind resource data, we developed a WRF model configuration using two nested domains (9 km and 3 km) to cover Puerto Rico and U.S. Virgin Islands and downscale the ERA5 reanalysis data (0.25 degrees x 0.25 degrees; hourly interval) to a 3-km domain. For the second stage, we implemented one-year simulations focused on using 11 different PBL physics parameterizations to find a combination of WRF physics parameterizations that could provide accurately modeled wind speed for Puerto Rico. We also analyzed the sensitivity of the modeled wind speed to PBL schemes for onshore and offshore locations. The WRF output resulting from the 11 WRF experiments using different PBL parameterizations were evaluated against observations obtained from the National Data Buoy Center (NDBC) as well as at hub height for a location for which measurements were available. A final model setup selected through the validation with observational data was used to produce 20 years of data with 3-km spatial and 5-minute temporal resolution. The WRF model output was post-processed to include wind profiles and basic atmospheric variables in a format that can be easily used for downstream modeling. The 20 years of wind resource data will be made available through NREL and support the estimation of wind energy development costs for the PR100 study.

17 WIND ENERGY↗