R. J. Barthelmie

Cornell University | CU

Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind sp...

The co-occurrence of freezing rain, ice accumulation and wind gusts (FZG) poses a significant hazard to infrastructure and transportation. However, quantification of the frequency and intensity of FZG is challenged by the lack of direct icing measurements. In this work, we evaluate and then apply an energy balance model to high-frequency data colle...

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather R...

A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where blade tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed data sets from lidar (light detection and ranging) de...

Cold-season windstorms represent an important, and potentially changing, geophysical hazard in the Northeastern United States. Here we employ an integrated research methodology including both a storyline approach, where three intense windstorms from the current climate are subjected to pseudo-global warming (PGW) experiments, and a long-term transi...

Low-level jets (LLJs) are examples of non-logarithmic wind speed profiles affecting wind turbine power production, wake recovery, and structural/aerodynamic loading. However, there is no consensus regarding which definition should be applied for jet identification. In this study we argue that a shear definition is more relevant to wind energy than...

Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind sp...

A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed datasets from LiDAR (Light Detection And Ranging) deploymen...

Low-level jets (LLJ) are examples of non-ideal wind speed profiles affecting wind turbine power production, wake recovery, and structural/aerodynamic loading. However, there is no consensus regarding which definition should be applied for jet identification. In this study we argue that a shear definition is more relevant for wind energy than a fall...

Offshore wind energy development along the East Coast of the US is proceeding quickly as a result of large areas with an excellent wind resource, low water depths and proximity to large electricity markets. Careful planning of wind turbine deployments in these offshore wind energy lease areas (LA) is required to maximize power output and to minimiz...

Windstorms resulting from intense synoptic-scale cyclones are an important natural hazard in the current climate of the northeastern United States, but their likely response to global climate non-stationarity is poorly understood. This study investigates the ability of the Weather Research and Forecasting model applied at 3.3 km resolution to simul...

Wind turbine blade leading edge erosion is a major source of power production loss and early detection benefits optimization of repair strategies. Two machine learning (ML) models are developed and evaluated for automated quantification of the areal extent, morphology and nature (deep, shallow) of damage from field images. The supervised ML model e...

Leading edge erosion (LEE) of wind turbine blades causes decreased aerodynamic performance leading to lower power production and revenue and increased operations and maintenance costs. LEE is caused primarily by materials stresses when hydrometeors (rain and hail) impact on rotating blades. The kinetic energy transferred by these impacts is a funct...

New simulations at 12-km grid spacing with the Weather and Research Forecasting (WRF) Model nested in the MPI Earth System Model (ESM) are used to quantify possible changes in wind power generation potential as a result of global warming. Annual capacity factors (CF; measures of electrical power production) computed by applying a power curve to hou...

The Southern Great Plains (SGP) region exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) Model applied at convection-permitting resolution with the Milbrandt–Yau microphysics scheme. Simulation fidelity is evaluated, p...

Projected changes to the El Niño Southern Oscillation (ENSO) climate mode have been explored using global Earth system models (ESMs). Regional expressions of such changes have yet to be fully advanced and may require the use of regional downscaling. Here, we employ regional climate modeling (RCM) using the Weather Research and Forecasting (WRF) mod...

A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located...

Simulations of the New York offshore wind energy lease area are undertaken within the PyWake program using both NOJ wake parameterization and the Fuga model. Seven different wind farm layouts are simulated to evaluate how the potential annual energy production (AEP) and capacity factor (CF) are impacted by the installed capacity (IC) density and th...

Projected power output and wake extents are presented from new simulations with the Weather Research and Forecasting (WRF) model v4.2.2 for the large offshore wind energy lease areas along the U.S. east coast. These simulations assume nearly 2000 IEA 15 MW reference turbines are deployed with a spacing equal to the mean of smaller European offshore...

The American WAKE experimeNt (AWAKEN) is a multi-institutional collaborative field campaign, starting in March 2022, that will gather an unprecedented data set including both atmospheric observations and wind plant operational data. This comprehensive data set will be used to characterize the wind plant performance and turbine loading in different...

Direct and indirect greenhouse gas (GHG) emissions from the ~30+ billion animals consumed as food each year contribute ~14–16% of the global total. The aim of this research is to determine the contribution of meat and animal products to individual GHG footprints. Top-down estimates of GHG emissions from each livestock species are determined using l...

Two years of high-resolution simulations conducted with the Weather Research and Forecasting (WRF) model are used to characterize the frequency, intensity and height of low-level jets (LLJ) over the U.S. Atlantic coastal zone. Meteorological conditions and the occurrence and characteristics of LLJs are described for (i) the centroids of thirteen of...

An 11-member ensemble of convection-permitting regional simulations of the fast-moving and destructive derecho of June 29 – 30, 2012 that impacted the northeastern urban corridor of the US is presented. This event generated 1100 reports of damaging winds, significant wind gusts over an extensive area of up to 500,000 km2, caused several fatalities...

We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can captur...

We provide the first quantitative assessment of power production and wake generation from offshore wind energy lease areas along the U.S. east coast. Deploying 15-MW wind turbines, with spacing equal to the European average, yields electricity production of 116 TWh/year or 3% of current national supply. However, power production is reduced by one-t...

Global wind resources greatly exceed current electricity demand and the levelized cost of energy from wind turbines has shown precipitous declines. Accordingly, the installed capacity of wind turbines grew at an annualized rate of about 14% during the last two decades and wind turbines now provide ~6–7% of the global electricity supply. This renewa...

Output from 6 months of high-resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize local low-level jets (LLJs) over Iowa for winter and spring in the contemporary climate. Low-level jets affect rotor plane aerodynamic loading, turbine structural loading and turbine performance, and thus accurate c...

Windstorms are a major natural hazard in many countries. The objective of this study is to identify and characterize intense windstorms during the last 4 decades in the US Northeast and determine both the sources of cyclones responsible for these events and the manner in which those cyclones differ from the cyclone climatology. The windstorm detect...

Cost-effective expansion of the wind energy industry benefits from robust estimates of wind resource and operating conditions. Extreme design loads contribute to wind turbine selection and cost, and are determined in part by the fifty year return period sustained wind speed (U50). Here we derive a global, homogenized and geospatially explicit digit...

The Outer Continental Shelf along the U.S. east coast exhibits abundant wind resources and is now a geographic focus for offshore wind deployments. This analysis derives and presents expected extreme wind and wave conditions for the sixteen lease areas that are currently being developed. Using the homogeneous ERA5 reanalysis dataset it is shown tha...

Windstorms are a major natural hazard in many countries. Windstorms during the last four decades in the U.S. Northeast are identified and characterized using the spatial extent of locally extreme wind speeds at 100 m height from the ERA5 reanalysis database. During all of the top 10 windstorms, wind speeds in excess of their local 99.9th percentile...

ERA5 provides high-resolution, high-quality hourly wind speeds at 100 m and is a unique resource for quantifying temporal variability in likely wind-derived power production across the United States. Gross capacity factors (CF) in seven independent system operators (ISOs) are estimated using the location and rated power of each wind turbine, a simp...

Output from high resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize local low level jets (LLJ) over Iowa. Analyses using a detection algorithm wherein the wind speed above and below the jet maximum must be below 80 % of the jet wind speed within a vertical window of approximately 20 m–530 m a.g...

Wind energy is a virtually carbon-free and pollution-free electricity source, with global wind resources greatly exceeding electricity demand. Accordingly, the installed capacity of wind turbines grew at an annualized rate of >20% from 2000 to 2019 and is projected to increase by a further 50% by the end of 2023. In this Review, we describe the fac...

High-resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize the frequency, intensity, height, and duration of springtime low-level jets (LLJ) and their implications for wind energy resource assessment and planning in Iowa. The time evolution of short-duration LLJ is analyzed to understand wind beha...

As wind turbine average hub-height (H) and rotor diameter (D) grow, it is assumed that the benefit derived from larger swept areas and higher wind speeds at higher altitudes will outweigh any increase in fatigue loading due to higher shear and manufacturing/installation costs. The impact of increasing wind turbine H and D on power production and th...

Leading Edge Erosion (LEE) of wind turbine blades leads to significant degradation of aerodynamic performance. Previous research has suggested kinetic energy transferred to the rotating blades from hydrometeor impacts are an important source of LEE. The Southern Great Plains (SGP) of the United States has substantial wind energy development and exp...

Deep convection and the related occurrence of hail, intense precipitation and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenan...

Continued growth of wind turbine physical dimensions is examined in terms of the implications for wind speed, power and shear across the rotor plane. High-resolution simulations with the Weather Research and Forecasting model are used to generate statistics of wind speed profiles for scenarios of current and future wind turbines. The nine-month sim...

Wind turbine blade leading edge erosion (LEE) is a potentially significant source of revenue loss for wind farm operators. Thus, it is important to advance understanding of the underlying causes, to generate geospatial estimates of erosion potential to provide guidance in pre-deployment planning, and ultimately to advance methods to mitigate this e...

The Weather Research and Forecasting (WRF) Model has been extensively used for wind energy applications, and current releases include a scheme that can be applied to examine the effects of wind turbine arrays on the atmospheric flow and electricity generation from wind turbines. Herein we present a high-resolution simulation using two different win...

Impacts from current and future wind turbine (WT) deployments necessary to achieve 20% electricity from wind are analyzed using high resolution numerical simulations over the eastern USA. Theoretical scenarios for future deployments are based on repowering (i.e. replacing with higher capacity WTs) thus avoiding competition for land. Simulations for...

Hail events are relatively rare, but hailstones (solid ice ‘balls’ ranging in diameter from 6 mm (pea) to 110 mm (softball)) have the potential to damage the leading edge of wind turbine blades causing reduced aerodynamic efficiency. Thus, hail is an important component of wind turbine operating conditions. Until recently hail occurrence was poorly...

Wind energy is both a key potential mechanism to reduce climate forcing and a ‘weather-dependent’ energy source. Thus, while wind energy is making an increasing contribution to mitigation of human-induced climate change, climate variability and change have the potential to induce changes in both the average (expected) wind resource, the inter-annua...

This research focuses on developing a computational model for analysing wind turbine blade erosion induced by raindrop impact. A stochastic rain texture model is used to simulated realistic rain events determined by a rain intensity and a rain duration. A new smoothed particle hydrodynamic approach is implemented to study the influence of the raind...

This paper reports results from an engaged learning activity conducted within an integrated, multi-scale, multi-disciplinary project focused on wind turbine blade leading edge erosion. Actions and outcomes of a series of engaged learning activities by undergraduate and graduate students undertaken in spring 2019 are reported. In designing and condu...

High-resolution simulations are conducted with the Weather Research and Forecasting Model to evaluate the sensitivity of wake effects and power production from two wind farm parameterizations [the commonly used Fitch scheme and the more recently developed Explicit Wake Parameterization (EWP)] to the resolution at which the model is applied. The sim...

Improved seismic noise characterization, due to varied sources, may benefit traditional applications. Some examples are earthquake detection, Earth structure research, and nuclear testing. This improvement could also permit use of seismic data in transdisciplinary research such as wind gust detection and wind turbine (WT) condition monitoring. Howe...

Wind gusts are a major cause of damage to property and the natural environment and a source of noise in seismic networks such as the USArray Transportable Array. Wind gusts cause ground motion through shear stresses, pressure fluctuations and vegetation flexing. Herein, we demonstrate the presence of a seismic response signature to wind gusts at si...

Wind turbine blade leading edge erosion (LEE) is a potentially significant source of revenue loss for windfarm operators. Thus, it is important to advance understanding of the underlying causes, to generate geospatial estimates of erosion potential to provide guidance in pre-deployment planning and ultimately to advance methods to mitigate this eff...

Optimization of wind turbine (WT) arrays to maximize system-wide power production (i.e. minimize ‘wind-theft’) requires high-fidelity simulations of array-array interactions at the regional scale. This study systematically compares two parameterizations (Fitch and EWP) developed to describe wind farm impacts on atmospheric flow in the Weather Resea...

During January – June 2017 a unique data set of Doppler lidar observations of a single wind turbine wake was collected within the Perdigão flow in complex terrain experiment conducted as part of the New European Wind Atlas. Over the six-month period, over 19,000 10- minute scans comprising a combination of; Arc Scans at ten elevation angles, Vertic...

An automated wind turbine wake characterization algorithm has been developed and applied to a data set of over 19 000 scans measured by a ground-based scanning Doppler lidar at Perdigão, Portugal, over the period January to June 2017. Potential wake cases are identified by wind speed, direction and availability of a retrieved free-stream wind speed...

Wind gusts are a key driver of aerodynamic loading, especially for tall structures such a bridges and wind turbines. However, gust characteristics in complex terrain are not well understood and common approximations used to describe wind gust behavior may not be appropriate at heights relevant to wind turbines and other structures. Data collected i...

An automated wind turbine wake characterization algorithm has been developed and applied to a dataset of over 19,000 scans measured by scanning Doppler lidar at Perdigão over the period January to June 2017. The algorithm correctly identifies the wake centre position in 62% of possible wake cases, 46% having a clear and well-defined wake centre whi...

Wind turbine performance and condition monitoring play vital roles in detecting and diagnosing suboptimal performance and guiding operations and maintenance. Here, a new seismic‐based approach to monitoring the health of individual wind turbine components is presented. Transfer functions are developed linking key condition monitoring properties (dr...

A grand challenge from the wind energy industry is to provide reliable forecasts on mountain winds several hours in advance at microscale (∼100 m) resolution. This requires better microscale wind-energy physics included in forecasting tools, for which field observations are imperative. While mesoscale (∼1 km) measurements abound, microscale process...