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DOE OSTI2025

A compact x-ray spectrometer for measurements of electron temperature distributions in inertial confinement fusion implosions at OMEGA

The Wedge Range Filter (WRF), commonly used for proton spectroscopy at the OMEGA Laser Facility and National Ignition Facility, is adapted to measure the x-ray continuum spectrum through transmission measurement using a continuous-gradient filter. Continuum x rays emitted from the hotspot of an implosion contain information about the plasma composition and electron temperature. The WRF data are leveraged to probe this distribution, specifically the electron temperature distribution. In this work, the data recorded with the WRF are forward modeled using a temperature distribution model folded with the WRF response function. An uncertainty analysis is conducted through a Bayesian regression algorithm using a Hamiltonian Monte Carlo sampler. This analysis enables the uncertainties in the instrument response to be folded into the uncertainty estimation of the electron temperature and absolute x-ray emission. Data analysis for a series of OMEGA implosions is presented and compared with radiation hydrodynamic simulations.

Lasers↗

DOE OSTI2023

Sensitivity of Pyrocumulus Convection to Tree Mortality During the 2020 Creek Fire in California

Abstract This study investigated the sensitivity of pyrocumulonimbus (PyroCb) induced by the California Creek fire of 2020 to the amount and type of surface fuels, within the WRF‐SFIRE modeling system. Satellite data were used to derive fire arrival times to constrain fire progression, and to augment the fuel characterization with better estimates of combustible vegetation accounting for tree mortality. Machine learning was employed to classify standing dead vegetation from aerial imagery, which was then added as a custom fuel class along with the standard Anderson fuel categories. Simulations using this new fuel class produced a larger and more vigorous PyroCb than the control run, however, still under‐predicted the cloud top. Additional augmentation of fuel mass to represent the accumulation of dead vegetation on the forest floor further improved the simulations, demonstrating the efficacy of representing both dead standing and fallen vegetation to produce more realistic PyroCb and smoke simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

The Contribution of Subtropical Moisture Within an Atmospheric River on Moisture Flux, Cloud Structure, and Precipitation Over the Salmon River Mountains of Idaho Using Moisture Tracers

The impact of an atmospheric river (AR) on the flux of subtropical moisture across Idaho's Salmon River Mountains and precipitation over the mountains is evaluated using the Weather, Research, and Forecasting model with water vapor tracers (WRF-WVT). The AR impacted Idaho between 17 and 19 January 2017 during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) campaign. WRF-WVT is configured to isolate the subtropical moisture contribution to the AR, the moisture flux, and precipitation. Subtropical water vapor advected by the AR into Idaho is tagged and tracked in three-dimensional space throughout the run. This allows the contribution of the subtropical moisture to the vertical distribution of water vapor and the precipitation to be directly calculated. The simulated cloud structure is compared with airborne radar data collected during two SNOWIE intensive operation periods. This study found that more than 70% of the moisture flux and more than 80% of the precipitation across the Idaho Mountains during SNOWIE IOP 4 could be attributed to subtropical moisture within the AR. Nearly all of the moisture flux in the upper cloud and 50% of the moisture in the lower cloud was attributable to the subtropical moisture. The subtropical moisture contribution within the AR to precipitation ranged from 35% in northern Idaho to more than 90% in southern Idaho. Across the entire period of impact of the AR, more than 60% of precipitation in Idaho was attributable to the subtropical moisture within the AR, with this percentage increasing toward the south across the state.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Sensitivity of mesoscale modeling to urban morphological feature inputs and implications for characterizing urban sustainability

We examine the differences in meteorological output from the Weather Research and Forecasting (WRF) model run at 270 m horizontal resolution using 10 m, 100 m and 1 km resolution 3D neighborhood morphological inputs and with no morphological inputs. We find that the spatial variability in temperature, humidity, and other meteorological variables across the city can vary with the resolution and the coverage of the 3D urban morphological input, and that larger differences occur between simulations run without 3D morphological input and those run with some type of 3D morphology. We also find that the inclusion of input-building-defined roughness length calculations would improve simulation results further. We show that these inputs produce different patterns of heat wave spatial heterogeneity across the city of Washington, DC. These findings suggest that understanding neighborhood level urban sustainability under extreme heat waves, especially for vulnerable neighborhoods, requires attention to the representation of surface terrain in numerical weather models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Anatomy of a Summertime Convective Event over the Arabian Region

This study investigates the structure and evolution of a summertime convective event that occurred on 14 July 2015 over the Arabian region. We use the WRF Model with 1-km horizontal grid spacing and test three PBL parameterizations: the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme; the Asymmetrical Convective Model, version 2, (ACM2) scheme; and the quasi-normal scale-elimination (QNSE) scheme. Convection initiates near the Al Hajar Mountains of northern Oman at around 1100 local time (LT; 0700 UTC) and propagates northwestward. A nonorographic convective band along the west coast of the United Arab Emirates (UAE) develops after 1500 LT as a result of the convergence of cold pools with the sea breeze from the Arabian Gulf. The model simulation employing the QNSE scheme simulates the convection initiation and propagation well. Although the MYNN and ACM2 simulations show convective initiation near the Al Hajar Mountains, they fail to simulate the development of the convective band along the UAE west coast. The MYNN run simulates colder near-surface temperatures and a weaker sea breeze, whereas the ACM2 run simulates a stronger sea breeze but a drier lower troposphere. Sensitivity simulations using horizontal grid spacings of 9 and 3 km show that lower-resolution runs develop broader convective structures and weaker cold pools and horizontal wind divergence, affecting the development of convection along the west coast of the UAE. The 1-km run using the QNSE PBL scheme realistically captures the sequence of events that leads to the moist convection over the UAE and adjacent mountains.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

AutoBEM-DynamicArchetypes

Automatic Building Energy Modeling (AutoBEM, https://bit.ly/AutoBEM) has been used to create an OpenStudio and EnergyPlus building energy model of 122.9 million U.S. buildings (https://bit.ly/ModelAmerica). Simulating and analyzing a model of every building for large areas (e.g. cities) is often not feasible. This dynamic archetyping capability uses a representative building and calculates a floor-space multiplier that allows millions of buildings to be represented by less than 100 buildings. This script (WRF_Archetypes_Parallel.py) calculates these building archetypes for each of the grid cells from a Weather Research and Forecasting (WRF) model in a parallel fashion. The script works by looping through each of the grid cells in the shapefile in parallel, spatially joining the building metadata table to each grid cell, aggregating relevant archetypes and calculating necessary statistics related to area and number of buildings in each cell. The output is a table (.csv) in which each row is an archetype building with properties about that building as well as statistics that relate that building to the total cell (such as an area multiplier). The following inputs are required: WRF zone shapefile (.shp) (wrf-grids-origin_Vegas_Select_100.geojson) The projection of the shapefile ("EPSG:XXXX") Input table containing building metadata for area corresponding to shapefile (.csv) (https://zenodo.org/record/4552901#.YZQEotDMJPY - ClarkCounty2.csv) The number of cores that will be parallelized (integer) The output file name for the archetype table (.csv) Sample command line inputs: python3 ~/WRF_Archetypes_Parallel.py -i ~/wrf-grids-origin_Vegas_Select_100.geojson -c ~/ClarkCounty2.csv -o ~/OutputArchetypes.csv -j 72 -e EPSG:4326 Using Geopandas Version 0.9.0

Bass, Brett (0000000240988434)↗

DOE OSTI2023

Biomass-burning smoke's properties and its interactions with marine stratocumulus clouds in WRF-CAM5 and southeastern Atlantic field campaigns

Abstract. A large part of the uncertainty in climate projections comes from uncertain aerosol properties and aerosol–cloud interactions as well as the difficulty in remotely sensing them. The southeastern Atlantic functions as a natural laboratory to study biomass-burning smoke and to constrain this uncertainty. We address these gaps by comparing the Weather Research and Forecasting with Chemistry Community Atmosphere Model (WRF-CAM5) to the multi-campaign observations ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS), CLARIFY (CLoud–Aerosol–Radiation Interaction and Forcing), and LASIC (Layered Atlantic Smoke Interactions with Clouds) in the southeastern Atlantic in August 2017 to evaluate a large range of the model's aerosol chemical properties, size distributions, processes, and transport, as well as aerosol–cloud interactions. Overall, while WRF-CAM5 is able to represent smoke properties and transport, some key discrepancies highlight the need for further analysis. Observations of smoke composition show an overall decrease in aerosol mean diameter as smoke ages over 4–12 d, while the model lacks this trend. A decrease in the mass ratio of organic aerosol (OA) to black carbon (BC), OA:BC, and the OA mass to carbon monoxide (CO) mixing ratio, OA:CO, suggests that the model is missing processes that selectively remove OA from the particle phase, such as photolysis and heterogeneous aerosol chemistry. A large (factor of ∼2.5) enhancement in sulfate from the free troposphere (FT) to the boundary layer (BL) in observations is not present in the model, pointing to the importance of properly representing secondary sulfate aerosol formation from marine dimethyl sulfide and gaseous SO2 smoke emissions. The model shows a persistent overprediction of aerosols in the marine boundary layer (MBL), especially for clean conditions, which multiple pieces of evidence link to weaker aerosol removal in the modeled MBL than reality. This evidence includes several model features, such as not representing observed shifts towards smaller aerosol diameters, inaccurate concentration ratios of carbon monoxide and black carbon, underprediction of heavy rain events, and little evidence of persistent biases in modeled entrainment. The average below-cloud aerosol activation fraction (NCLD/NAER) remains relatively constant in WRF-CAM5 between field campaigns (∼0.65), while it decreases substantially in observations from ORACLES (∼0.78) to CLARIFY (∼0.5), which could be due to the model misrepresentation of clean aerosol conditions. WRF-CAM5 also overshoots an observed upper limit on liquid cloud droplet concentration around NCLD= 400–500 cm−3 and overpredicts the spread in NCLD. This could be related to the model often drastically overestimating the strength of boundary layer vertical turbulence by up to a factor of 10. We expect these results to motivate similar evaluations of other modeling systems and promote model development to reduce critical uncertainties in climate simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Evaluation of Model Summertime Boundary Layer Cloud Development over Complex Terrain in New York State

Weather forecasting over complex terrain with diverse land cover is challenging. Utilizing the high-resolution observations from New York State Mesonet (NYSM), we are able to evaluate the surface processes of the Weather Research Forecast (WRF) Model in a detailed, scale-dependent manner. Here, in the study, possible impacts of land–atmosphere interaction on surface meteorology and boundary layer cloud development are investigated with different model resolutions, land surface models (LSMs), and planetary boundary layer (PBL) physical parameterizations. The High-Resolution Rapid Refresh, version 3 (HRRR), forecasting model is used as a reference for the sensitivity evaluation. Results show that over complex terrain, the high-resolution simulations (1 km × 60 vertical levels) generally perform better compared to low-resolution (3 km × 50 levels) in both surface meteorology and cloud fields. LSMs play a more important role in surface meteorology compared to PBL schemes. The NoahMP land surface model exhibits daytime warmer and drier biases compared to the Rapid Update Cycle (RUC) due to better prediction of the Bowen ratio in RUC. The PBL schemes would affect the convective strength in the boundary layer. The Shin–Hong (SH) scale-aware scheme tends to produce the strongest convective strength in the PBL, while the ACM2 PBL scheme rarely resolved convection even at 1-km resolution. By considering the radiation effect of subgrid-scale (SGS) clouds, the Mellor–Yamada–Nakanishi–Niino eddy diffusivity mass flux (MYNN-EDMF) predicted the highest cloud coverage and lowest surface solar radiation bias. The configuration of SGS clouds in MYNN-EDMF would not only significantly reduce shortwave radiation bias, but also affect the convection behaviors through land surface–cloud–radiation interaction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Understanding Processes Controlling the Temporal and Spatial Variations of PBL Structures Over the ARM SGP Site

The surface heat, moisture, and momentum fluxes are transferred to the atmosphere above through the planetary boundary layer (PBL), where vertical mixing due to turbulent eddies of different sizes plays critical roles. Therefore, reliably representing PBL processes in numerical models is critical for weather, climate, and air quality prediction. Currently, there are over ten PBL schemes that are selectable within the advanced research version of the Weather Research and Forecasting (WRF) model, indicative of the challenges in capturing the impacts of turbulence within the PBL in models. Further improvements in PBL parameterizations are needed for both weather and climate models, as emphasized in many recent national reports, but require an advanced understanding of the underlying boundary layer processes from observations. This project takes advantage of Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) investments in the atmospheric boundary layer observations and Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) simulations to characterize PBL structures, understand key physical processes controlling the mixed layer development, and to evaluate PBL parameterization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

ParFlow

ParFlow is an open-source, modular, parallel watershed flow model. It includes fully-integrated overland flow, the ability to simulate complex topography, geology and heterogeneity and coupled land-surface processes including the land-energy budget, biogeochemistry and snow (via CLM). It is multi-platform and runs with a common I/O structure from laptop to supercomputer. ParFlow is the result of a long, multi-institutional development history and is now a collaborative effort between CSM, LLNL, UniBonn and UCB. ParFlow has been coupled to the mesoscale, meteorological code ARPS and the NCAR code WRF.

Smith, Steven↗

DOE OSTI2021

ParFlow

Smith, Steven↗

DOE OSTI2022

Can lidars assess wind plant blockage in simple terrain? A WRF-LES study

Wind plant blockage reduces wind velocity upstream of wind plants, reducing the power generated by turbines adjacent to the inflow, and potentially throughout the plant as well. The nature of the mechanism that amplifies blockage as well as the velocity reductions in both the induction zone and potentially deeper into the array are not well understood. Field observations can provide valuable insight into the characteristics of the induction zone and the mechanisms that amplify it. However, the relatively small velocity reductions that have been measured experimentally pose a challenge in quantifying blockage, especially in onshore environments with flow heterogeneities that may be of the same scale as the blockage effect itself. We simulate the flow around the King Plains wind plant in the relatively simple terrain of Oklahoma, the location of the American WAKE experimeNt, to evaluate wind plant blockage in this environment. Using numerical simulations, we find the largest velocity deceleration (0.64 m s−1; 8%) immediately upstream of the wind plant, and 1% velocity deficits 24 rotor diameters upstream of the first turbine row. We also use virtual measurements upstream of the wind plant to analyze the uncertainties and difficulties in measuring blockage using a scanning lidar on shore. Based on our virtual lidar study, the induction zone of land-based wind plants can be incorrectly estimated using observations if the effects of nonuniform terrain on the flow are not carefully considered. Changes in terrain elevation produce local variations in wind speed (as measured by a scanning lidar) that exceed in magnitude the deceleration within the induction zone. We refer to these local changes in wind speed as terrain effects. A methodology to differentiate between terrain effects and blockage in experimental settings is proposed and evaluated herein, highlighting the difficulties and uncertainties associated with measurement and simulation of blockage in even relatively simple onshore environments.

17 WIND ENERGY↗

DOE OSTI2020

Assimilating Scanning Radar Data into High-Resolution Models

This report documents the findings of a project aiming to improve the initial conditions of km-scale simulations of deep convective storms by assimilating cloud-scale weather radar observations. More accurate numerical analyses will increase the effectiveness of research efforts using LES cloud models as tool to better understand land-atmosphere coupling, boundary layer turbulence, and cloud processes, each used for model parameterization development. Work reported on herein includes: i) assessment of radar data sets for use in data assimilation (‘DA’) experiments, ii) quality control of the radar data set and format conversion to one acceptable to DA schemes utilized by the Weather Research and Forecasting (WRF) model, and iii) examination of the sensitivity of the numerical representation of cloud-scale wind and microphysical features to a variety of tunable DA parameters.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

The sensitivity of the Fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme

Abstract. Wind plant wake impacts can be estimated with a number of simulation methodologies, each with its own fidelity and sensitivity to model inputs. In turbine-free mesoscale simulations, hub-height wind speeds often significantly vary with the choice of a planetary boundary layer (PBL) scheme. However, the sensitivity of wind plant wakes to a PBL scheme has not been explored because, as of the Weather Research and Forecasting model v4.3.3, wake parameterizations were only compatible with one PBL scheme. We couple the Fitch wind farm parameterization with the new NCAR 3DPBL scheme and compare the resulting wakes to those simulated with a widely used PBL scheme. We simulate a wind plant in pseudo-steady states under idealized stable, neutral, and unstable conditions with matching hub-height wind speeds using two PBL schemes: MYNN and the NCAR 3DPBL. For these idealized scenarios, average hub-height wind speed losses within the plant differ between PBL schemes by between −0.20 and 0.22 m s−1, and correspondingly, capacity factors range between 39.5 %–53.8 %. These simulations suggest that PBL schemes represent a meaningful source of modeled wind resource uncertainty; therefore, we recommend incorporating PBL variability into future wind plant planning sensitivity studies as well as wind forecasting studies.

17 WIND ENERGY↗

DOE OSTI2021

Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models

In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Effects from Time Dependence of Ice Nucleus Activity for Contrasting Cloud Types

Abstract The role of time-dependent freezing of ice nucleating particles (INPs) is evaluated with the “Aerosol–Cloud” (AC) model in 1) deep convection observed over Oklahoma during the Midlatitude Continental Convective Cloud Experiment (MC3E), 2) orographic clouds observed over North California during the Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), and 3) supercooled, stratiform clouds over the United Kingdom, observed during the Aerosol Properties, Processes And Influences on the Earth’s climate (APPRAISE) campaign. AC uses the dynamical core of the WRF Model and has hybrid bin–bulk microphysics and a 3D mesoscale domain. AC is validated against coincident aircraft, ground-based, and satellite observations for all three cases. Filtered concentrations of ice (>0.1–0.2 mm) agree with those observed at all sampled levels. AC predicts the INP activity of various types of aerosol particles with an empirical parameterization (EP), which follows a singular approach (no time dependence). Here, the EP is modified to represent time-dependent INP activity by a purely empirical approach, using our published laboratory observations of time-dependent INP activity. In all simulated clouds, the inclusion of time dependence increases the predicted INP activity of mineral dust particles by 0.5–1 order of magnitude. However, there is little impact on the cloud glaciation because the total ice is mostly (80%–90%) from secondary ice production (SIP) at levels warmer than about −36°C. The Hallett–Mossop process and fragmentation in ice–ice collisions together initiate about 70% of the total ice, whereas fragmentation during both raindrop freezing and sublimation contributes <10%. Overall, total ice concentrations and SIP are unaffected by time-dependent INP activity. In the simulated APPRAISE case, the main causes of persistence of long-lived clouds and precipitation are predicted to be SIP in weak embedded convection and reactivation following recirculation of dust particles in supercooled layer cloud.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Modeling the smoky troposphere of the southeast Atlantic: a comparison to ORACLES airborne observations from September of 2016

The southeast Atlantic is home to well-defined smoke outflow from Africa coinciding vertically with extensive marine boundary-layer cloud decks, both reaching their climatological maxima in spatial extent around September. A framework is put forth for evaluating the performance of a range of global and regional aerosol models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016. The sparse airborne observations are first aggregated into 2o grid boxes and into three vertical layers: the cloud-topped marine boundary layer (MBL), the layer from cloud top to 3 km, and the 3-6 km layer. Aerosol extensive properties simulated for the entire study region for all September suggest that the 2016 ORACLES observations are reasonably representative of the regional monthly average, with systematic deviations of 30% or less. All six models typically place the bottom of the smoke layer at lower altitudes than do the airborne lidar observations by 300-1400 m, whereas model aerosol top heights are within 0-500 m of the observations. All but one of the models that report carbonaceous aerosol masses underestimate the ratio of particulate extinction to the masses, a proxy for mass extinction efficiency, in 3-6 km. Notable findings on individual models include that WRF-CAM5 predicts the mass of black carbon and organic aerosols with minor (~10% or less) biases. GEOS-5 overestimates the carbonaceous particle masses in the MBL by a factor of 3-6. Extinction coefficients in the free troposphere (FT) and above-cloud aerosol optical depth (ACAOD) are 10-30% lower in WRF-CAM5, 30-50% lower in GEOS-5, 10-40% higher in GEOS-Chem, 10-20% higher in EAM-E3SM except for the practically unbiased 3-6 km extinction, and 20-70% lower in the Unified Model, than the airborne in situ, lidar and sunphotometer measurements. ALADIN-Climate also underestimates the ACAOD, by 30%. GEOS-5 and GEOS-Chem predict carbon monoxide in the MBL with small (10% or less) negative biases, despite their overestimates of carbonaceous aerosol masses. Overall, this study highlights a new approach to utilizing airborne aerosol measurements for model diagnosis.

Shinozuka, Yohei↗

DOE OSTI2023

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.

17 WIND ENERGY↗