Search NASA⌕ Search

SEARCH · Search NASA

Results for “Atmospheric Forecast”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Simulating and Evaluating Atmospheric River-Induced Precipitation Extremes Along the U.S. Pacific Coast: Case Studies From 1980–2017

Atmospheric rivers (ARs) are responsible for a majority of extreme precipitation and floodevents along the U.S. West Coast. To better understand the present-day characteristics of AR-relatedprecipitation extremes, a selection of nine most intense historical AR events during 1980–2017 is simulatedusing a dynamical downscaling modeling framework based on the Weather Research and ForecastingModel. We find that the chosen framework and Weather Research and Forecasting Model configurationreproduces both large-scale atmospheric features—including parent synoptic-scale cyclones—as well asthe filamentary corridors of integrated vapor transport associated with the ARs themselves. The accuracyof simulated extreme precipitation maxima, relative to in situ and interpolated gridded observations,improves notably with increasing model resolution, with improvements as large as 40–60% for fine scale(3 km) relative to coarse-scale (27 km) simulations. A separate set of simulations using smoothedtopography suggests that much of these gains stem from the improved representation of complex terrain.Additionally, using the 12 December 1995 storm in Northern California as an example, we demonstratethat only the highest-resolution simulations resolve important fine-scale features—such as localizedorographically forced vertical motion and powerful near hurricane-force boundary layer winds. Given thedemonstrated ability of a targeted dynamical downscaling framework to capture both local extremeprecipitation and key fine-scale characteristics of the most intense ARs in the historical record, we arguethat such a configuration may be highly conducive to understanding AR-related extremes and associatedchanges in a warming climate

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Convective potential and fuel availability complement near-surface weather in regulating global wildfire activity

Wildfires are favored by hot, dry, windy, rainless conditions—this knowledge about fire weather informs both short-term forecast and long-term prediction of wildfire activity. Yet, wildfires rely on the availability of ignition and fuel, which are underrepresented in fire forecast and prediction practices. By analyzing satellite measurements and atmospheric reanalysis, here we show that near-surface weather only partially captures wildfire occurrence and intensity across the daily to seasonal timescales. Beyond near-surface weather, convection and fuel abundance play a complementary role in regulating burning processes. Specifically, enhanced atmospheric convection is identified for over 40% of the low-human-impact regions and 61% of global burnable areas during wildfire ignition and spreading periods. Meanwhile, 56% of shrublands and 54% of grasslands see higher fuel load with actual occurrence of fire. Our results highlight the role of convection and fuel in wildfire forecast, prompting a revisit of wildfire prediction under intertwined atmospheric and terrestrial changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Accurate and Timely Forecasts of Geologic Carbon Storage using Machine Learning Methods

Carbon capture and storage is one strategy to reduce greenhouse gas emissions. One approach to storing the captured CO2 is to inject it into deep saline aquifers. However, dynamics of the injected CO2 plume is uncertain and the potential for leakage back to the atmosphere must be assessed. Thus, accurate and timely forecasts of CO2 storage via real-time measurements integration becomes very crucial. This study proposes a learning-based, inverse-free prediction method that can accurately and rapidly forecast CO2 movement and distribution with uncertainty quantification based on limited simulation and observation data. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for real-time decision making.

Lu, Dan↗
DOE OSTI2020

Evaluating Implementations of the Immersed Boundary Method in the Weather Research and Forecasting Model

The terrain-following coordinate system used by many atmospheric models can cause numerical instabilities due to discretization errors as resolved terrain slopes increase and the grid becomes highly skewed. The immersed boundary (IB) method, which does not require the grid to conform to the terrain, has been shown to alleviate these errors, and has been used successfully for high-resolution atmospheric simulations over steep terrain, including vertical building surfaces. Since many previous applications of IB methods to atmospheric models have used very fine grid resolution (5 m or less), the present study seeks to evaluate IB method performance over a range of grid resolutions and aspect ratios. Two classes of IB algorithms, velocity reconstruction and shear stress reconstruction, are tested within the common framework of the Weather Research and Forecasting (WRF) Model. Performance is evaluated in two test cases, one with flat terrain and the other with the topography of Askervein Hill, both under neutrally stratified conditions. WRF-IB results are compared to similarity theory, observations, and native WRF results. Despite sensitivity to the location at which the IB intersects the model grid, the velocity reconstruction IB method shows consistent performance when used with a hybrid RANS/LES surface scheme. The shear stress reconstruction IB method is not sensitive to the grid intersection, but is less consistent and near-surface velocity errors can occur at coarse resolutions. This study represents an initial investigation of IB method variability across grid resolutions in WRF. Future work will focus on improving IB method performance at intermediate to coarse resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Influence of background sources and topographic resolution in the Weather Research and Forecasting Model on xenon plume characteristics at monitoring stations

For many atmospheric monitoring applications, networks of measurement sites—such as the radionuclide stations of the International Monitoring System—can be sparse. With measurement locations potentially hundreds to thousands of kilometers from a release it is important to quantify the effects of physical processes on transport and dispersion of plumes between source and measurement locations. This study addresses the effects of background sources and topography resolution near the release location of radionuclides. We use the Weather Research and Forecasting (WRF) model with inline chemistry to investigate (1) how an additional, time-varying source of 133 Xe, such as an operational medical isotope production facility, contributes to activity concentration measurements at monitoring sites, and (2) how complex topography influences on atmospheric conditions near emission sources impact plume concentrations at varying distances from the source. Two 133 Xe emission sources, including (1) a high flux rate of short duration representing an explosive event, and (2) a variable and continuous background source, are simulated. The continuous background source contributes significantly to total 133 Xe concentrations at several monitoring stations. Further, a WRF simulation at 9 km horizontal resolution is compared with a nested grid simulation, where the innermost domain has a resolution of 1 km. Increased topographic resolution leads to an improved representation of plume responses to local winds, with topographic influences greatest at locations closest to the sources. Differences between the two domain resolutions decrease at greater distances from the sources, as plumes have time to spread and mix and are influenced by synoptic scale circulation patterns that are represented similarly in both simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

How Do Climate Model Resolution and Atmospheric Moisture Affect the Simulation of Unprecedented Extreme Events Like the 2021 Western North American Heat Wave?

Abstract Although the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Observations of the Marine Atmospheric Boundary Layer’s Response to a Solar Eclipse

The atmospheric response to the solar eclipse of 8 April 2024 in North America is investigated with a specific focus on the marine atmospheric boundary layer (MABL). We leverage measurements collected during the Third Wind Forecast Improvement Project (WFIP3), including Doppler lidars, sonic anemometers, and thermodynamic profiler data to investigate the atmospheric response across sites that experienced partial eclipse conditions with nearly 90% obscuration. Using these measurements, we examine eclipse-induced changes in key meteorological parameters, such as temperature, wind speed, and turbulent fluxes. Most previous eclipse studies have been conducted over land, whereas this study provides new observations for both coastal and marine environments, offering additional insight into eclipse-driven variability in the MABL. The findings confirm a notable decrease in downwelling shortwave radiation during the eclipse, which results in rapid cooling of surface air. The temperature reduction ranges from $1.2^\circ \text {C}$ to $1.4^\circ \text {C}$ in coastal regions and from $0.3^\circ \text {C}$ to $0.5^\circ \text {C}$ over the ocean. This analysis suggests that the MABL’s higher thermal inertia compared to coastal regions moderates the temperature decrease during the eclipse. Wind speed exhibits a more complex behavior, as it is influenced by both the MABL and preexisting synoptic conditions. Although a reduction in wind speed is observable up to approximately 140 m above ground level (AGL) at more inland sites, at other locations closer to the coast, this reduction is constrained to the lowest 100 m AGL. Turbulence parameters retrieved from sonic anemometers, such as turbulence kinetic energy, turbulent heat flux, and friction velocity, decrease during the eclipse at coastal sites, accompanied by a brief transition of atmospheric stability from unstable to neutral or weakly stable conditions. For the open-ocean sites, the variability in turbulence statistics and atmospheric stability is minimal during the occurrence of the eclipse.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Lidar-Based Evaluation of HRRR Performance in California’s Diablo Range

The performance of the NOAA High-Resolution Rapid Refresh (HRRR) model for capturing low-level winds near a wind energy production site during summer 2019 is evaluated. This study catalogs the ability of HRRR to predict boundary layer dynamics relevant to wind energy interests over complex terrain, which has presented challenges for weather and energy forecasting. Performance is evaluated by comparing HRRR output to wind-profiling Doppler lidars at Lawrence Livermore National Laboratory Site 300. HRRR captured the diurnal profile of horizontal winds in the observed 150-m layer, despite strong underpredictions (∼4 m s −1 ) during evening and nighttime hours. These underpredictions may be a result of local speedup flows observed by the lidars, which were unresolved in HRRR due to their small spatial extent. HRRR bias magnitude relative to observations was found to be minimal during days with synoptic-scale troughs and strong 850-hPa geopotential gradients, while bias magnitude was maximal during days with synoptic ridging and weak 850-hPa geopotential gradients. To translate wind speed predictions to energy forecasting, generic turbine models were used to estimate power generation for turbines characteristic of the nearby Altamont Pass Wind Resource Area. Results show that HRRR-based energy estimates predicted daytime power generation adequately relative to lidar-based estimates with an 18-h lead time (bias magnitude < 0.4 MW from 0900 to 1400 LT) but overpredicted power during the rest of the diurnal cycle (bias > 1 MW). These results demonstrate conditions under which HRRR performs well for wind energy applications in complex terrain, while highlighting biases that require further investigation to support usage of a high-resolution model for wind energy forecasts.

Boundary layer↗
DOE OSTI2020

Aerosol Impacts on Mesoscale Convective Systems Forming Under Different Vertical Wind Shear Conditions

Following our previous study of wind shear effect on mesoscale convective system (MCS) organization under a clean atmospheric condition using the Weather Research and Forecasting model coupled with Spectral-Bin Microphysics, we conduct sensitivity simulations by increasing cloud condensation nuclei (CCN) concentration and investigate aerosol impacts on MCSs forming under different wind shear conditions. We find that, aerosols induce stronger updrafts and downdrafts in all MCSs. The stronger updrafts and enlarged convective core area contribute to larger vertical mass fluxes and enhance precipitation, indicating convective invigoration. Increased updraft speed below 8-km altitude mainly results from enhanced condensational heating, and increased water loading effect is not large enough to overcome the increase in thermal buoyancy. Interestingly, above 8-km altitude we see reduced updraft speed by the increased aerosols due to reduced vertical pressure perturbation gradient force, and weakened depositional growth due to competition of water vapor by enhanced condensation. The accumulated rainfall and mean rain rate are increased, with an increased occurrence frequency of heavy rain. The increase of rain rate is seen in both convective and stratiform regions, with a more significant increase in convective rain rate. In general, we see a higher frequency of deep convective cloud in the polluted condition because of invigorated convection, and more stratiform/anvil clouds, but a lower frequency of shallow warm clouds. The unanimously invigorated MCSs by aerosols under various wind shear conditions revealed by this study has an important implication in weather and climate at warm and humid regions that are influenced by pollution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Simulation of Continental Shallow Cumulus Populations Using an Observation-Constrained Cloud-System Resolving Model

Continental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign are simulated by using an observation-constrained cloud-system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four-dimensional ensemble-variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow-to-deep transitions of convective clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Summertime Post-Cold-Frontal Marine Stratocumulus Transition Processes over the Eastern North Atlantic

Abstract Marine boundary layer (MBL) cloud morphology associated with two summertime cold fronts over the eastern North Atlantic (ENA) is investigated using high-resolution simulations from the Weather Research and Forecasting (WRF) Model and observations from the Atmospheric Radiation Measurement (ARM) ENA Climate Research Facility. Lagrangian trajectories are used to study the evolution of post-cold-frontal MBL clouds from solid stratocumulus to broken cumulus. Clouds within specified domains in the vicinity of transitions are classified according to their degree of decoupling, and cloud-base and cloud-top breakup processes are evaluated. The Lagrangian derivative of the surface latent heat flux is found to be strongly correlated with that of the cloud fraction at cloud base in the simulations. Cloud-top entrainment instability (CTEI) is shown to operate only in the decoupled MBL. A new indicator of inversion strength at cloud top that employs the vertical gradients of equivalent potential temperature and saturation equivalent potential temperature, which can be computed directly from soundings, is proposed as an alternative to CTEI. Overall, results suggest that the deepening–warming hypothesis suggested by Bretherton and Wyant explains many of the characteristics of the summertime postfrontal MBL evolution of cloud structure over the ENA, thereby widening the phase space over which the hypothesis may be applied. A subset of the deepening–warming hypothesis involving warming initially dominating over moistening is proposed. It is postulated that changes in climate change–induced modifications in cold-frontal structure over the ENA may be accompanied by coincident changes in the location and timing of MBL cloud transitions in the post-cold-frontal environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Energy Research and Forecasting (ERF) v1

ERF is a next-generation regional atmospheric modeling code for simulation mesoscale atmospheric flows. Its capabilities include the ability to model advection, diffusion, turbulence, terrain and moisture physics.

Almgren, Ann↗
DOE OSTI2020

Decadal predictability of North Atlantic blocking and the NAO

Can multi-annual variations in the frequency of North Atlantic atmospheric blocking and mid-latitude circulation regimes be skilfully predicted? Recent advances in seasonal forecasting have shown that mid-latitude climate variability does exhibit significant predictability. However, atmospheric predictability has generally been found to be quite limited on multi-annual timescales. New decadal prediction experiments from NCAR are found to exhibit remarkable skill in reproducing the observed multi-annual variations of wintertime blocking frequency over the North Atlantic and of the North Atlantic Oscillation (NAO) itself. This is partly due to the large ensemble size that allows the predictable component of the atmospheric variability to emerge from the background chaotic component. The predictable atmospheric anomalies represent a forced response to oceanic low-frequency variability that strongly resembles the Atlantic Multi-decadal Variability (AMV), correctly reproduced in the decadal hindcasts thanks to realistic ocean initialization and ocean dynamics. The occurrence of blocking in certain areas of the Euro-Atlantic domain determines the concurrent circulation regime and the phase of known teleconnections, such as the NAO, consequently affecting the stormtrack and the frequency and intensity of extreme weather events. Therefore, skilfully predicting the decadal fluctuations of blocking frequency and the NAO may be used in statistical predictions of near-term climate anomalies, and it provides a strong indication that impactful climate anomalies may also be predictable with improved dynamical models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Subseasonal Earth System Prediction with CESM2

Abstract Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available. Significance Statement We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bias Correcting NOAA's High-Resolution Rapid Refresh (HRRR) Wind Resource Data for Grid Integration Applications [Slides]

Many weather years of high-quality wind data are widely accepted in the grid integration community to be important for studying wind energy technical potential, energy system operations, and grid resilience. NREL makes high-quality wind and solar resource data available. NREL's Grid-Atmosphere workshop (March 2024) identified NREL National Solar Radiation Database as widely used in grid integration modeling, but there is less agreement on commonly used wind datasets. One important factor identified by ESIG's 2023 report 'Weather Dataset Needs for Planning and Analyzing Modern Power Systems' for gold standard wind data is regular updates. To address the need for regular updates, NREL's team can now process all currently available and regularly updated High-Resolution Rapid Refresh (HRRR) outputs. HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). One barrier to NREL using HRRR is systematic bias and consistency with NREL's existing wind datasets (e.g. WIND Toolkit, 'WTK') across weather years. To address this barrier, we show that the HRRR can be interpolated and bias-corrected to be consistent with NRE's existing datasets. We call the new dataset BC-HRRR (bias-corrected HRRR). As with historical datasets like the WTK, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling). BC-HRRR's (2015-present) consistency with WTK (2007-2013) allows NREL to extend internal grid integration tooling with 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. The rest of this slide deck documents the BC-HRRR processing methods, validation, and its implications for intended use.

17 WIND ENERGY↗
DOE OSTI2023

Exploring the Meteorological Impacts of Surface and Rooftop Heat Mitigation Strategies Over a Tropical City

Different heat mitigation technologies have been developed to improve the thermal environment in cities. However, the regional impacts of such technologies, especially in the context of a tropical city, remain unclear. The deployment of heat mitigation technologies at city-scale can change the radiation balance, advective flow, and energy balance between urban areas and the overlying atmosphere. We used the mesoscale Weather Research and Forecasting model coupled with a physically based single-layer urban canopy model to assess the impacts of five different heat mitigation technologies on surface energy balance, standard surface meteorological fields, and planetary boundary layer (PBL) dynamics for premonsoon typical hot summer days over a tropical coastal city in the month of April in 2018, 2019, and 2020. Results indicate that the regional impacts of cool materials (CMs), super-cool broadband radiative coolers, green roofs (GRs), vegetation fraction change, and a combination of CMs and GRs (i.e., “Cool city (CC)”) on the lower atmosphere are different at diurnal scale. Results showed that super-cool materials have the maximum potential of ambient temperature reduction of 1.6°C during peak hour (14:00 LT) compared to other technologies in the study. During the daytime hours, the PBL height was considerably lower than the reference scenario with no implementation of strategies by 700 m for super-cool materials and 500 m for both CMs and CC cases; however, the green roofing system underwent nominal changes over the urban area. During the nighttime hours, the PBL height increased by CMs and the CC strategies compared to the reference scenario, but minimal changes were evident for super-cool materials. The changes of temperature on the vertical profile of the heat mitigation implemented city reveal a stable PBL over the urban domain and a reduction of the vertical mixing associated with a pollution dome. This would lead to crossover phenomena above the PBL due to the decrease in vertical wind speed. Therefore, assessing the coupled regional impact of urban heat mitigation over the lower atmosphere at city-scale is urgent for sustainable urban planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗