Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2307-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-2307-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
28 Nov 2022
Review article |
| 28 Nov 2022
| 28 Nov 2022
Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Larry K. Berg
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Mithu Debnath
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Georgios Deskos
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Caroline Draxl
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Renewable and Sustainable Energy Institute, Boulder, CO 80309, USA
Virendra P. Ghate
Argonne National Laboratory, 9700 South Cass Ave., Lemont, IL 60439, USA
Charlotte B. Hasager
DTU Wind Energy, Technical University of Denmark, Risø Campus,
Roskilde, Denmark
Rao Kotamarthi
Argonne National Laboratory, 9700 South Cass Ave., Lemont, IL 60439, USA
Jeffrey D. Mirocha
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Paytsar Muradyan
Argonne National Laboratory, 9700 South Cass Ave., Lemont, IL 60439, USA
William J. Pringle
Argonne National Laboratory, 9700 South Cass Ave., Lemont, IL 60439, USA
David D. Turner
Global Systems Laboratory, NOAA, Boulder, CO 80305, USA
James M. Wilczak
Physical Sciences Laboratory, NOAA, Boulder, CO 80305, USA
Related authors
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
Short summary
Short summary
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
Short summary
Short summary
Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
Short summary
Short summary
This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
Mithu Debnath, G. Valerio Iungo, Ryan Ashton, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Julie K. Lundquist, William J. Shaw, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 431–444, https://doi.org/10.5194/amt-10-431-2017, https://doi.org/10.5194/amt-10-431-2017, 2017
Short summary
Short summary
Triple RHI scans were performed with three simultaneous scanning Doppler wind lidars and assessed with lidar profiler and sonic anemometer data. This test is part of the XPIA experiment. The scan strategy consists in two lidars performing co-planar RHI scans, while a third lidar measures the transversal velocity component. The results show that horizontal velocity and wind direction are measured with good accuracy, while the vertical velocity is typically measured with a significant error.
G. G. Palancar, B. L. Lefer, S. R. Hall, W. J. Shaw, C. A. Corr, S. C. Herndon, J. R. Slusser, and S. Madronich
Atmos. Chem. Phys., 13, 1011–1022, https://doi.org/10.5194/acp-13-1011-2013, https://doi.org/10.5194/acp-13-1011-2013, 2013
Bianca Adler, David D. Turner, Laura Bianco, Irina V. Djalalova, Timothy Myers, and James M. Wilczak
Atmos. Meas. Tech., 17, 6603–6624, https://doi.org/10.5194/amt-17-6603-2024, https://doi.org/10.5194/amt-17-6603-2024, 2024
Short summary
Short summary
Continuous profile observations of temperature and humidity in the lowest part of the atmosphere are essential for the evaluation of numerical weather prediction models and data assimilation for better weather forecasts. Such profiles can be retrieved from passive ground-based remote sensing instruments like infrared spectrometers and microwave radiometers. In this study, we describe three recent modifications to the retrieval framework TROPoe for improved temperature and humidity profiles.
Tessa E. Rosenberger, David D. Turner, Thijs Heus, Girish N. Raghunathan, Timothy J. Wagner, and Julia Simonson
Atmos. Meas. Tech., 17, 6595–6602, https://doi.org/10.5194/amt-17-6595-2024, https://doi.org/10.5194/amt-17-6595-2024, 2024
Short summary
Short summary
This work used model output to show that considering the changes in boundary layer depth over time in the calculations of variables such as fluxes and variance yields more accurate results than cases where calculations were done at a constant height. This work was done to improve future observations of these variables at the top of the boundary layer.
Tessa E. Rosenberger, Thijs Heus, Girish N. Raghunathan, David D. Turner, Timothy J. Wagner, and Julia M. Simonson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2894, https://doi.org/10.5194/egusphere-2024-2894, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Entrainment is key in understanding temperature and moisture changes within the boundary layer, but it is difficult to observe using ground-based observations. This work used simulations to verify an assumption that simplifies entrainment estimations from ground-based observational data, recognizing that entrainment is the combination of the transfer of heat and moisture from above the boundary layer into it and the change in concentration of heat and moisture as boundary layer depth changes.
Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, and Ethan Young
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-115, https://doi.org/10.5194/wes-2024-115, 2024
Preprint under review for WES
Short summary
Short summary
Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and inter-annual trends in the wind resource to support customers interested in small and midsize wind energy.
Ye Liu, Yun Qian, Larry K. Berg, Zhe Feng, Jianfeng Li, Jingyi Chen, and Zhao Yang
Atmos. Chem. Phys., 24, 8165–8181, https://doi.org/10.5194/acp-24-8165-2024, https://doi.org/10.5194/acp-24-8165-2024, 2024
Short summary
Short summary
Deep convection under various large-scale meteorological patterns (LSMPs) shows distinct precipitation features. In southeastern Texas, mesoscale convective systems (MCSs) contribute significantly to precipitation year-round, while isolated deep convection (IDC) is prominent in summer and fall. Self-organizing maps (SOMs) reveal convection can occur without large-scale lifting or moisture convergence. MCSs and IDC events have distinct life cycles influenced by specific LSMPs.
Laura Bianco, Bianca Adler, Ludovic Bariteau, Irina V. Djalalova, Timothy Myers, Sergio Pezoa, David D. Turner, and James M. Wilczak
Atmos. Meas. Tech., 17, 3933–3948, https://doi.org/10.5194/amt-17-3933-2024, https://doi.org/10.5194/amt-17-3933-2024, 2024
Short summary
Short summary
The Tropospheric Remotely Observed Profiling via Optimal Estimation physical retrieval is used to retrieve temperature and humidity profiles from various combinations of passive and active remote sensing instruments, surface platforms, and numerical weather prediction models. The retrieved profiles are assessed against collocated radiosonde in non-cloudy conditions to assess the sensitivity of the retrievals to different input combinations. Case studies with cloudy conditions are also inspected.
Christopher J. Cox, Janet M. Intrieri, Brian Butterworth, Gijs de Boer, Michael R. Gallagher, Jonathan Hamilton, Erik Hulm, Tilden Meyers, Sara M. Morris, Jackson Osborn, P. Ola G. Persson, Benjamin Schmatz, Matthew D. Shupe, and James M. Wilczak
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-158, https://doi.org/10.5194/essd-2024-158, 2024
Preprint under review for ESSD
Short summary
Short summary
Snow is an essential water resource in the intermountain western United States and predictions are made using models. We made observations to validate, constrain, and develop the models. The data is from the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign in Colorado’s East River Valley, 2021–2023. The measurements include meteorology and variables that quantify energy transfer between the atmosphere and surface. The data are available publicly.
Lindsay M. Sheridan, Dmitry Duplyakin, Caleb Phillips, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, and Larry K. Berg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-37, https://doi.org/10.5194/wes-2024-37, 2024
Preprint under review for WES
Short summary
Short summary
Twelve months of onsite wind measurement is standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, however, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities for distributed wind projects. This study aims to answer the question of how low can you go in terms of the observational time period needed to make impactful improvements to long-term wind speed estimates.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Volker Wulfmeyer, Christoph Senff, Florian Späth, Andreas Behrendt, Diego Lange, Robert M. Banta, W. Alan Brewer, Andreas Wieser, and David D. Turner
Atmos. Meas. Tech., 17, 1175–1196, https://doi.org/10.5194/amt-17-1175-2024, https://doi.org/10.5194/amt-17-1175-2024, 2024
Short summary
Short summary
A simultaneous deployment of Doppler, temperature, and water-vapor lidar systems is used to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of weather forecast and climate models.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
Short summary
Short summary
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Sunil Baidar, Timothy J. Wagner, David D. Turner, and W. Alan Brewer
Atmos. Meas. Tech., 16, 3715–3726, https://doi.org/10.5194/amt-16-3715-2023, https://doi.org/10.5194/amt-16-3715-2023, 2023
Short summary
Short summary
This paper provides a new method to retrieve wind profiles from coherent Doppler lidar (CDL) measurements. It takes advantage of layer-to-layer correlation in wind profiles to provide continuous profiles of up to 3 km by filling in the gaps where the CDL signal is too small to retrieve reliable results by itself. Comparison with the current method and collocated radiosonde wind measurements showed excellent agreement with no degradation in results where the current method gives valid results.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
Short summary
Short summary
The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Y. Joseph Zhang, Tomas Fernandez-Montblanc, William Pringle, Hao-Cheng Yu, Linlin Cui, and Saeed Moghimi
Geosci. Model Dev., 16, 2565–2581, https://doi.org/10.5194/gmd-16-2565-2023, https://doi.org/10.5194/gmd-16-2565-2023, 2023
Short summary
Short summary
Simulating global ocean from deep basins to coastal areas is a daunting task but is important for disaster mitigation efforts. We present a new 3D global ocean model on flexible mesh to study both tidal and nontidal processes and total water prediction. We demonstrate the potential for
seamlesssimulation, on a single mesh, from the global ocean to a few estuaries along the US West Coast. The model can serve as the backbone of a global tide surge and compound flooding forecasting framework.
Christopher R. Williams, Joshua Barrio, Paul E. Johnston, Paytsar Muradyan, and Scott E. Giangrande
Atmos. Meas. Tech., 16, 2381–2398, https://doi.org/10.5194/amt-16-2381-2023, https://doi.org/10.5194/amt-16-2381-2023, 2023
Short summary
Short summary
This study uses surface disdrometer observations to calibrate 8 years of 915 MHz radar wind profiler deployed in the central United States in northern Oklahoma. This study had two key findings. First, the radar wind profiler sensitivity decreased approximately 3 to 4 dB/year as the hardware aged. Second, this drift was slow enough that calibration can be performed using 3-month intervals. Calibrated radar wind profiler observations and Python processing code are available on public repositories.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
Short summary
Short summary
Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Maria P. Cadeddu, Virendra P. Ghate, David D. Turner, and Thomas E. Surleta
Atmos. Chem. Phys., 23, 3453–3470, https://doi.org/10.5194/acp-23-3453-2023, https://doi.org/10.5194/acp-23-3453-2023, 2023
Short summary
Short summary
We analyze the variability in marine boundary layer moisture at the Eastern North Atlantic site on a monthly and daily temporal scale and examine its fundamental role in the control of boundary layer cloudiness and precipitation. The study also highlights the complex interaction between large-scale and local processes controlling the boundary layer moisture and the importance of the mesoscale spatial distribution of vapor to support convection and precipitation.
Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis
Atmos. Meas. Tech., 16, 1195–1209, https://doi.org/10.5194/amt-16-1195-2023, https://doi.org/10.5194/amt-16-1195-2023, 2023
Short summary
Short summary
We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.
Bianca Adler, James M. Wilczak, Jaymes Kenyon, Laura Bianco, Irina V. Djalalova, Joseph B. Olson, and David D. Turner
Geosci. Model Dev., 16, 597–619, https://doi.org/10.5194/gmd-16-597-2023, https://doi.org/10.5194/gmd-16-597-2023, 2023
Short summary
Short summary
Rapid changes in wind speed make the integration of wind energy produced during persistent orographic cold-air pools difficult to integrate into the electrical grid. By evaluating three versions of NOAA’s High-Resolution Rapid Refresh model, we demonstrate how model developments targeted during the second Wind Forecast Improvement Project improve the forecast of a persistent cold-air pool event.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
Short summary
Short summary
As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
Gianluca Di Natale, David D. Turner, Giovanni Bianchini, Massimo Del Guasta, Luca Palchetti, Alessandro Bracci, Luca Baldini, Tiziano Maestri, William Cossich, Michele Martinazzo, and Luca Facheris
Atmos. Meas. Tech., 15, 7235–7258, https://doi.org/10.5194/amt-15-7235-2022, https://doi.org/10.5194/amt-15-7235-2022, 2022
Short summary
Short summary
In this paper, we describe a new approach to test the consistency of the precipitating ice cloud optical and microphysical properties in Antarctica, Dome C, retrieved from hyperspectral measurements in the far-infrared, with the reflectivity detected by a co-located micro rain radar operating at 24 GHz. The retrieved ice crystal sizes were found in accordance with the direct measurements of an optical imager, also installed at Dome C, which can collect the falling ice particles.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022, https://doi.org/10.5194/ascmo-8-205-2022, 2022
Short summary
Short summary
We study wind conditions and their potential future changes across the U.S. via a statistical conditional framework. We conclude that changes between historical and future wind directions are small, but wind speeds are generally weakened in the projected period, with some locations being intensified. Moreover, winter wind speeds are projected to decrease in the northwest, Colorado, and the northern Great Plains (GP), while summer wind speeds over the southern GP slightly increase in the future.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
Short summary
Short summary
Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Heather Guy, David D. Turner, Von P. Walden, Ian M. Brooks, and Ryan R. Neely
Atmos. Meas. Tech., 15, 5095–5115, https://doi.org/10.5194/amt-15-5095-2022, https://doi.org/10.5194/amt-15-5095-2022, 2022
Short summary
Short summary
Fog formation is highly sensitive to near-surface temperatures and humidity profiles. Passive remote sensing instruments can provide continuous measurements of the vertical temperature and humidity profiles and liquid water content, which can improve fog forecasts. Here we compare the performance of collocated infrared and microwave remote sensing instruments and demonstrate that the infrared instrument is especially sensitive to the onset of thin radiation fog.
Caleb Phillips, Lindsay M. Sheridan, Patrick Conry, Dimitrios K. Fytanidis, Dmitry Duplyakin, Sagi Zisman, Nicolas Duboc, Matt Nelson, Rao Kotamarthi, Rod Linn, Marc Broersma, Timo Spijkerboer, and Heidi Tinnesand
Wind Energ. Sci., 7, 1153–1169, https://doi.org/10.5194/wes-7-1153-2022, https://doi.org/10.5194/wes-7-1153-2022, 2022
Short summary
Short summary
Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classic and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data-driven methods are most effective at predicting production at real sites and new models reliably outperform classic methods.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, https://doi.org/10.5194/gmd-15-3433-2022, https://doi.org/10.5194/gmd-15-3433-2022, 2022
Short summary
Short summary
In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
James B. Duncan Jr., Laura Bianco, Bianca Adler, Tyler Bell, Irina V. Djalalova, Laura Riihimaki, Joseph Sedlar, Elizabeth N. Smith, David D. Turner, Timothy J. Wagner, and James M. Wilczak
Atmos. Meas. Tech., 15, 2479–2502, https://doi.org/10.5194/amt-15-2479-2022, https://doi.org/10.5194/amt-15-2479-2022, 2022
Short summary
Short summary
In this study, several ground-based remote sensing instruments are used to estimate the height of the convective planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the CHEESEHEAD19 field campaign. The impact of clouds (particularly boundary layer clouds) on the estimation of the boundary layer depth is also investigated.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
Short summary
Short summary
An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Geng Xia, Caroline Draxl, Michael Optis, and Stephanie Redfern
Wind Energ. Sci., 7, 815–829, https://doi.org/10.5194/wes-7-815-2022, https://doi.org/10.5194/wes-7-815-2022, 2022
Short summary
Short summary
In this study, we propose a new method to detect sea breeze events from the Weather Research and Forecasting simulation. Our results suggest that the method can identify the three different types of sea breezes in the model simulation. In addition, the coastal impact, seasonal distribution and offshore wind potential associated with each type of sea breeze differ significantly, highlighting the importance of identifying the correct type of sea breeze in numerical weather/wind energy forecasting.
Lindsay M. Sheridan, Caleb Phillips, Alice C. Orrell, Larry K. Berg, Heidi Tinnesand, Raj K. Rai, Sagi Zisman, Dmitry Duplyakin, and Julia E. Flaherty
Wind Energ. Sci., 7, 659–676, https://doi.org/10.5194/wes-7-659-2022, https://doi.org/10.5194/wes-7-659-2022, 2022
Short summary
Short summary
The small wind community relies on simplified wind models and energy production simulation tools to obtain energy generation expectations. We gathered actual wind speed and turbine production data across the US to test the accuracy of models and tools for small wind turbines. This study provides small wind installers and owners with the error metrics and sources of error associated with using models and tools to make performance estimates, empowering them to adjust expectations accordingly.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
Short summary
Short summary
Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
Irina V. Djalalova, David D. Turner, Laura Bianco, James M. Wilczak, James Duncan, Bianca Adler, and Daniel Gottas
Atmos. Meas. Tech., 15, 521–537, https://doi.org/10.5194/amt-15-521-2022, https://doi.org/10.5194/amt-15-521-2022, 2022
Short summary
Short summary
In this paper we investigate the synergy obtained by combining active (radio acoustic sounding system – RASS) and passive (microwave radiometer) remote sensing observations to obtain temperature vertical profiles through a radiative transfer model. Inclusion of the RASS observations leads to more accurate temperature profiles from the surface to 5 km above ground, well above the maximum height of the RASS observations themselves (2000 m), when compared to the microwave radiometer used alone.
Ye Liu, Yun Qian, and Larry K. Berg
Wind Energ. Sci., 7, 37–51, https://doi.org/10.5194/wes-7-37-2022, https://doi.org/10.5194/wes-7-37-2022, 2022
Short summary
Short summary
Uncertainties in initial conditions (ICs) decrease the accuracy of wind speed forecasts. We find that IC uncertainties can alter wind speed by modulating the weather system. IC uncertainties in local thermal gradient and large-scale circulation jointly contribute to wind speed forecast uncertainties. Wind forecast accuracy in the Columbia River Basin is confined by initial uncertainties in a few specific regions, providing useful information for more intense measurement and modeling studies.
Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 6355–6372, https://doi.org/10.5194/gmd-14-6355-2021, https://doi.org/10.5194/gmd-14-6355-2021, 2021
Short summary
Short summary
Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
Short summary
Short summary
We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
Michael P. Jensen, Virendra P. Ghate, Dié Wang, Diana K. Apoznanski, Mary J. Bartholomew, Scott E. Giangrande, Karen L. Johnson, and Mandana M. Thieman
Atmos. Chem. Phys., 21, 14557–14571, https://doi.org/10.5194/acp-21-14557-2021, https://doi.org/10.5194/acp-21-14557-2021, 2021
Short summary
Short summary
This work compares the large-scale meteorology, cloud, aerosol, precipitation, and thermodynamics of closed- and open-cell cloud organizations using long-term observations from the astern North Atlantic. Open-cell cases are associated with cold-air outbreaks and occur in deeper boundary layers, with stronger winds and higher rain rates compared to closed-cell cases. These results offer important benchmarks for model representation of boundary layer clouds in this climatically important region.
Mithu Debnath, Paula Doubrawa, Mike Optis, Patrick Hawbecker, and Nicola Bodini
Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, https://doi.org/10.5194/wes-6-1043-2021, 2021
Short summary
Short summary
As the offshore wind industry emerges on the US East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. We leverage a year of data of two floating lidars to quantify and characterize the frequent occurrence of high-wind-shear and low-level-jet events, both of which will have a considerable impact on turbine operation. We find that almost 100 independent long events occur throughout the year.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, Robert S. Arthur, and Domingo Muñoz-Esparza
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-57, https://doi.org/10.5194/wes-2021-57, 2021
Revised manuscript not accepted
Short summary
Short summary
Winds decelerate upstream of a wind plant as turbines obstruct and extract energy from the flow. This effect is known as wind plant blockage. We assess how atmospheric stability modifies the upstream wind plant blockage. We find stronger stability amplifies this effect. We also explore different approaches to quantifying blockage from field-like observations. We find different methodologies may induce errors of the same order of magnitude as the blockage-induced velocity deficits.
Mike Optis, Nicola Bodini, Mithu Debnath, and Paula Doubrawa
Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, https://doi.org/10.5194/wes-6-935-2021, 2021
Short summary
Short summary
Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 m. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine-learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
Short summary
Short summary
Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
Short summary
Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Alfredo Peña, Branko Kosović, and Jeffrey D. Mirocha
Wind Energ. Sci., 6, 645–661, https://doi.org/10.5194/wes-6-645-2021, https://doi.org/10.5194/wes-6-645-2021, 2021
Short summary
Short summary
We investigate the ability of a community-open weather model to simulate the turbulent atmosphere by comparison with measurements from a 250 m mast at a flat site in Denmark. We found that within three main atmospheric stability regimes, idealized simulations reproduce closely the characteristics of the observations with regards to the mean wind, direction, turbulent fluxes, and turbulence spectra. Our work provides foundation for the use of the weather model in multiscale real-time simulations.
David D. Turner and Ulrich Löhnert
Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021, https://doi.org/10.5194/amt-14-3033-2021, 2021
Short summary
Short summary
Temperature and humidity profiles in the lowest couple of kilometers near the surface are very important for many applications. Passive spectral radiometers are commercially available, and observations from these instruments have been used to get these profiles. However, new active lidar systems are able to measure partial profiles of water vapor. This paper investigates how the derived profiles of water vapor and temperature are improved when the active and passive observations are combined.
Jaydeep Singh, Narendra Singh, Narendra Ojha, Amit Sharma, Andrea Pozzer, Nadimpally Kiran Kumar, Kunjukrishnapillai Rajeev, Sachin S. Gunthe, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 1427–1443, https://doi.org/10.5194/gmd-14-1427-2021, https://doi.org/10.5194/gmd-14-1427-2021, 2021
Short summary
Short summary
Atmospheric models often have limitations in simulating the geographically complex and climatically important central Himalayan region. In this direction, we have performed regional modeling at high resolutions to improve the simulation of meteorology and dynamics through a better representation of the topography. The study has implications for further model applications to investigate the effects of anthropogenic pressure over the Himalaya.
William J. Pringle, Damrongsak Wirasaet, Keith J. Roberts, and Joannes J. Westerink
Geosci. Model Dev., 14, 1125–1145, https://doi.org/10.5194/gmd-14-1125-2021, https://doi.org/10.5194/gmd-14-1125-2021, 2021
Short summary
Short summary
We improve and test a computer model that simulates tides and storm surge over all of Earth's oceans and seas. The model varies mesh resolution (triangular element sizes) freely so that coastal areas, especially storm landfall locations, are well-described. We develop systematic tests of the resolution in order to suggest good mesh design criteria that balance computational efficiency with accuracy for both global astronomical tides and coastal storm tides under extreme weather forcing.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021, https://doi.org/10.5194/wes-6-45-2021, 2021
Short summary
Short summary
Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and Weather Research and Forecasting model simulations. This research describes how mountain waves form in the Columbia River basin and affect wind energy production and their impact on operational forecasting, wind plant layout, and integration of power into the grid.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Jason Roadman, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci., 5, 1253–1272, https://doi.org/10.5194/wes-5-1253-2020, https://doi.org/10.5194/wes-5-1253-2020, 2020
Short summary
Short summary
A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
Tobias Ahsbahs, Galen Maclaurin, Caroline Draxl, Christopher R. Jackson, Frank Monaldo, and Merete Badger
Wind Energ. Sci., 5, 1191–1210, https://doi.org/10.5194/wes-5-1191-2020, https://doi.org/10.5194/wes-5-1191-2020, 2020
Short summary
Short summary
Before constructing wind farms we need to know how much energy they will produce. This requires knowledge of long-term wind conditions from either measurements or models. At the US East Coast there are few wind measurements and little experience with offshore wind farms. Therefore, we created a satellite-based high-resolution wind resource map to quantify spatial variations in the wind conditions over potential sites for wind farms and found larger variation than modelling suggested.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
Short summary
Short summary
Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Maria P. Cadeddu, Virendra P. Ghate, and Mario Mech
Atmos. Meas. Tech., 13, 1485–1499, https://doi.org/10.5194/amt-13-1485-2020, https://doi.org/10.5194/amt-13-1485-2020, 2020
Short summary
Short summary
A combination of ground-based active and passive observations is used to partition cloud and precipitation liquid water path in precipitating stratocumulous clouds. Results show that neglecting scattering effects from drizzle drops leads to 8–15 % overestimation of the liquid amount in the cloud. In closed-cell systems only ~20 % of the available drizzle in the cloud falls below the cloud base, compared to ~40 % in open-cell systems.
Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Aditya Choukulkar, Larry K. Berg, Harindra J. S. Fernando, Eric P. Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, Mikhail Pekour, Yelena Pichugina, Mark T. Stoelinga, and David D. Turner
Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, https://doi.org/10.5194/gmd-12-4803-2019, 2019
Short summary
Short summary
During the second Wind Forecast Improvement Project, improvements to the parameterizations were applied to the High Resolution Rapid Refresh model and its nested version. The impacts of the new parameterizations on the forecast of 80 m wind speeds and power are assessed, using sodars and profiling lidars observations for comparison. Improvements are evaluated as a function of the model’s initialization time, forecast horizon, time of the day, season, site elevation, and meteorological phenomena.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
Short summary
Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Jiali Wang, Cheng Wang, Vishwas Rao, Andrew Orr, Eugene Yan, and Rao Kotamarthi
Geosci. Model Dev., 12, 3523–3539, https://doi.org/10.5194/gmd-12-3523-2019, https://doi.org/10.5194/gmd-12-3523-2019, 2019
Short summary
Short summary
WRF-Hydro needs to be calibrated to optimize its output with respect to observations. However, when applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed in parallel. This study ported an independent calibration tool (parameter estimation tool – PEST) to high-performance computing clusters and adapted it to work with WRF-Hydro. The results show significant speedup for model calibration.
Keith J. Roberts, William J. Pringle, and Joannes J. Westerink
Geosci. Model Dev., 12, 1847–1868, https://doi.org/10.5194/gmd-12-1847-2019, https://doi.org/10.5194/gmd-12-1847-2019, 2019
Short summary
Short summary
Computer simulations can be used to reproduce the dynamics of the ocean near the coast. These simulations often use a mesh of triangles to represent the domain since they can be orientated and disparately sized in such a way to accurately fit the coastline shape. This paper describes a software package (OceanMesh2D v1.0) that has been developed in order to automatically and objectively design triangular meshes based on geospatial data inputs that represent the coastline and ocean depths.
Nicola Bodini, Julie K. Lundquist, Raghavendra Krishnamurthy, Mikhail Pekour, Larry K. Berg, and Aditya Choukulkar
Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019, https://doi.org/10.5194/acp-19-4367-2019, 2019
Short summary
Short summary
To improve the parameterization of the turbulence dissipation rate (ε) in numerical weather prediction models, we have assessed its temporal and spatial variability at various scales in the Columbia River Gorge during the WFIP2 field experiment. The turbulence dissipation rate shows large spatial variability, even at the microscale, with larger values in sites located downwind of complex orographic structures or in wind farm wakes. Distinct diurnal and seasonal cycles in ε have also been found.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
Short summary
Short summary
This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
Luca Delle Monache, Stefano Alessandrini, Irina Djalalova, James Wilczak, and Jason C. Knievel
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-1214, https://doi.org/10.5194/acp-2017-1214, 2018
Preprint withdrawn
Short summary
Short summary
The authors demonstrate how the analog ensemble (AnEn) can efficiently generate deterministic and probabilistic forecasts of air quality. The method avoids the complexity and real-time computational expense of dynamical (i.e., model-based) ensembles. AnEn deterministic predictions have lower errors and are better correlated with observations. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.
Claire Pettersen, Ralf Bennartz, Aronne J. Merrelli, Matthew D. Shupe, David D. Turner, and Von P. Walden
Atmos. Chem. Phys., 18, 4715–4735, https://doi.org/10.5194/acp-18-4715-2018, https://doi.org/10.5194/acp-18-4715-2018, 2018
Short summary
Short summary
A novel method for classifying Arctic precipitation using ground based remote sensors is presented. The classification reveals two distinct, primary regimes of precipitation over the central Greenland Ice Sheet: snowfall coupled to deep, fully glaciated ice clouds or to shallow, mixed-phase clouds. The ice clouds are associated with low-pressure storm systems from the southeast, while the mixed-phase clouds slowly propagate from the southwest along a quiescent flow.
Robert A. Stillwell, Ryan R. Neely III, Jeffrey P. Thayer, Matthew D. Shupe, and David D. Turner
Atmos. Meas. Tech., 11, 835–859, https://doi.org/10.5194/amt-11-835-2018, https://doi.org/10.5194/amt-11-835-2018, 2018
Short summary
Short summary
This work focuses on making unambiguous measurements of Arctic cloud phase and assessing those measurements within the context of cloud radiative effects. It is found that effects related to lidar data recording systems can cause retrieval ambiguities that alter the interpretation of cloud phase in as much as 30 % of the available data. This misinterpretation of cloud-phase data can cause a misinterpretation of the effect of cloud phase on the surface radiation budget by as much as 10 to 30 %.
Louis Marelle, Jean-Christophe Raut, Kathy S. Law, Larry K. Berg, Jerome D. Fast, Richard C. Easter, Manish Shrivastava, and Jennie L. Thomas
Geosci. Model Dev., 10, 3661–3677, https://doi.org/10.5194/gmd-10-3661-2017, https://doi.org/10.5194/gmd-10-3661-2017, 2017
Short summary
Short summary
We develop the WRF-Chem 3.5.1 model to improve simulations of aerosols and ozone in the Arctic. Both species are important air pollutants and climate forcers, but models often struggle to reproduce observations in the Arctic. Our developments concern pollutant emissions, mixing, chemistry, and removal, including processes related to snow and sea ice. The effect of these changes are quantitatively validated against observations, showing significant improvements compared to the original model.
Jean-Christophe Raut, Louis Marelle, Jerome D. Fast, Jennie L. Thomas, Bernadett Weinzierl, Katharine S. Law, Larry K. Berg, Anke Roiger, Richard C. Easter, Katharina Heimerl, Tatsuo Onishi, Julien Delanoë, and Hans Schlager
Atmos. Chem. Phys., 17, 10969–10995, https://doi.org/10.5194/acp-17-10969-2017, https://doi.org/10.5194/acp-17-10969-2017, 2017
Short summary
Short summary
We study the cross-polar transport of plumes from Siberian fires to the Arctic in summer, both in terms of transport pathways and efficiency of deposition processes. Those plumes containing soot may originate from anthropogenic and biomass burning sources in mid-latitude regions and may impact the Arctic climate by depositing on snow and ice surfaces. We evaluate the role of the respective source contributions, investigate the transport of plumes and treat pathway-dependent removal of particles.
Katherine McCaffrey, Laura Bianco, and James M. Wilczak
Atmos. Meas. Tech., 10, 2595–2611, https://doi.org/10.5194/amt-10-2595-2017, https://doi.org/10.5194/amt-10-2595-2017, 2017
Short summary
Short summary
In this paper, we use two wind profiling radars, operating along side a highly instrumented 300 m meteorological tower, to observe turbulence dissipation rates in the planetary boundary layer from an optimized performance setup. Analysis of post-processing techniques, including spectral averaging and moments' calculation methods, shows the optimal parameters which result in good agreement, especially after bias corrections, with sonic anemometers on the tall tower.
Yann Blanchard, Alain Royer, Norman T. O'Neill, David D. Turner, and Edwin W. Eloranta
Atmos. Meas. Tech., 10, 2129–2147, https://doi.org/10.5194/amt-10-2129-2017, https://doi.org/10.5194/amt-10-2129-2017, 2017
Short summary
Short summary
Multiband thermal measurements of zenith sky radiance were used in a retrieval algorithm, to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. The retrieval technique was validated using a synergy lidar and radar data. Inversions were performed across three polar winters and results showed a significant correlation (R2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of thin ice clouds.
Laura Bianco, Katja Friedrich, James M. Wilczak, Duane Hazen, Daniel Wolfe, Ruben Delgado, Steven P. Oncley, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 1707–1721, https://doi.org/10.5194/amt-10-1707-2017, https://doi.org/10.5194/amt-10-1707-2017, 2017
Short summary
Short summary
XPIA is a study held in 2015 at NOAA's Boulder Atmospheric Observatory facility, aimed at assessing remote-sensing capabilities for wind energy applications. We use well-defined reference systems to validate temperature retrieved by two microwave radiometers (MWRs) and virtual temperature measured by wind profiling radars with radio acoustic sounding systems (RASSs). Water vapor density and relative humidity by the MWRs were also compared with similar measurements from the reference systems.
Rob K. Newsom, W. Alan Brewer, James M. Wilczak, Daniel E. Wolfe, Steven P. Oncley, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, https://doi.org/10.5194/amt-10-1229-2017, 2017
Short summary
Short summary
Doppler lidars are remote sensing instruments that use infrared light to measure wind velocity in the lowest 2 to 3 km of the atmosphere. Quantifying the uncertainty in these measurements is crucial for applications ranging from wind resource assessment to model data assimilation. In this study, we evaluate three methods for estimating the random uncertainty by comparing the lidar wind measurements with nearly collocated in situ wind measurements at multiple levels on a tall tower.
Mithu Debnath, Giacomo Valerio Iungo, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Scott Gunter, Julie K. Lundquist, John L. Schroeder, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 1215–1227, https://doi.org/10.5194/amt-10-1215-2017, https://doi.org/10.5194/amt-10-1215-2017, 2017
Short summary
Short summary
The XPIA experiment was conducted in 2015 at the Boulder Atmospheric Observatory to estimate capabilities of various remote-sensing techniques for the characterization of complex atmospheric flows. Among different tests, XPIA provided the unique opportunity to perform simultaneous virtual towers with Ka-band radars and scanning Doppler wind lidars. Wind speed and wind direction were assessed against lidar profilers and sonic anemometer data, highlighting a good accuracy of the data retrieved.
Katherine McCaffrey, Laura Bianco, Paul Johnston, and James M. Wilczak
Atmos. Meas. Tech., 10, 999–1015, https://doi.org/10.5194/amt-10-999-2017, https://doi.org/10.5194/amt-10-999-2017, 2017
Short summary
Short summary
Using an optimized turbulence mode of two wind profiling radars (449 MHz and 915 MHz) during the XPIA field campaign, we present improved measurements of vertical velocity variance at the resolved and unresolved scales, using first and second Doppler spectral moments, and the total variance over all scales. Comparisons with sonic anemometers gave strong results, particularly during the daytime convective period. Profiles up to 2 km are possible with the 449 MHz WPR and 1 km from the 915 MHz WPR.
Mithu Debnath, G. Valerio Iungo, Ryan Ashton, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Julie K. Lundquist, William J. Shaw, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 431–444, https://doi.org/10.5194/amt-10-431-2017, https://doi.org/10.5194/amt-10-431-2017, 2017
Short summary
Short summary
Triple RHI scans were performed with three simultaneous scanning Doppler wind lidars and assessed with lidar profiler and sonic anemometer data. This test is part of the XPIA experiment. The scan strategy consists in two lidars performing co-planar RHI scans, while a third lidar measures the transversal velocity component. The results show that horizontal velocity and wind direction are measured with good accuracy, while the vertical velocity is typically measured with a significant error.
Katherine McCaffrey, Paul T. Quelet, Aditya Choukulkar, James M. Wilczak, Daniel E. Wolfe, Steven P. Oncley, W. Alan Brewer, Mithu Debnath, Ryan Ashton, G. Valerio Iungo, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 393–407, https://doi.org/10.5194/amt-10-393-2017, https://doi.org/10.5194/amt-10-393-2017, 2017
Short summary
Short summary
During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) field campaign, the wake and flow distortion from a 300-meter meteorological tower was identified using pairs of sonic anemometers mounted on opposite sides of the tower, as well as profiling and scanning lidars. Wind speed deficits up to 50% and TKE increases of 2 orders of magnitude were observed at wind directions in the wake, along with wind direction differences (flow deflection) outside of the wake.
Aditya Choukulkar, W. Alan Brewer, Scott P. Sandberg, Ann Weickmann, Timothy A. Bonin, R. Michael Hardesty, Julie K. Lundquist, Ruben Delgado, G. Valerio Iungo, Ryan Ashton, Mithu Debnath, Laura Bianco, James M. Wilczak, Steven Oncley, and Daniel Wolfe
Atmos. Meas. Tech., 10, 247–264, https://doi.org/10.5194/amt-10-247-2017, https://doi.org/10.5194/amt-10-247-2017, 2017
Short summary
Short summary
This paper discusses trade-offs among various wind measurement strategies using scanning Doppler lidars. It is found that the trade-off exists between being able to make highly precise point measurements versus covering large spatial extents. The highest measurement precision is achieved when multiple lidar systems make wind measurements at one point in space, while highest spatial coverage is achieved through using single lidar scanning measurements and using complex retrieval techniques.
Jeffrey S. Reid, Peng Xian, Brent N. Holben, Edward J. Hyer, Elizabeth A. Reid, Santo V. Salinas, Jianglong Zhang, James R. Campbell, Boon Ning Chew, Robert E. Holz, Arunas P. Kuciauskas, Nofel Lagrosas, Derek J. Posselt, Charles R. Sampson, Annette L. Walker, E. Judd Welton, and Chidong Zhang
Atmos. Chem. Phys., 16, 14041–14056, https://doi.org/10.5194/acp-16-14041-2016, https://doi.org/10.5194/acp-16-14041-2016, 2016
Short summary
Short summary
This paper describes aspects of the 2012 7 Southeast Asian Studies (7SEAS) operations period, the largest within the Maritime Continent. Included were an enhanced deployment of Aerosol Robotic Network (AERONET) sun photometers, multiple lidars, and a Singapore supersite. Simultaneously, a ship was dispatched to the Palawan Archipelago and Sulu Sea of the Philippines for September 2012 to observe transported smoke and pollution as it entered the southwest monsoon trough.
Jeffrey S. Reid, Nofel D. Lagrosas, Haflidi H. Jonsson, Elizabeth A. Reid, Samuel A. Atwood, Thomas J. Boyd, Virendra P. Ghate, Peng Xian, Derek J. Posselt, James B. Simpas, Sherdon N. Uy, Kimo Zaiger, Donald R. Blake, Anthony Bucholtz, James R. Campbell, Boon Ning Chew, Steven S. Cliff, Brent N. Holben, Robert E. Holz, Edward J. Hyer, Sonia M. Kreidenweis, Arunas P. Kuciauskas, Simone Lolli, Min Oo, Kevin D. Perry, Santo V. Salinas, Walter R. Sessions, Alexander Smirnov, Annette L. Walker, Qing Wang, Liya Yu, Jianglong Zhang, and Yongjing Zhao
Atmos. Chem. Phys., 16, 14057–14078, https://doi.org/10.5194/acp-16-14057-2016, https://doi.org/10.5194/acp-16-14057-2016, 2016
Short summary
Short summary
This paper describes aspects of the 2012 7 Southeast Asian Studies (7SEAS) operations period, the largest within the Maritime Continent. Included were an enhanced deployment of Aerosol Robotic Network (AERONET) sun photometers, multiple lidars, and a Singapore supersite. Simultaneously, a ship was dispatched to the Palawan Archipelago and Sulu Sea of the Philippines for September 2012 to observe transported smoke and pollution as it entered the southwest monsoon trough.
Ivan Ortega, Sean Coburn, Larry K. Berg, Kathy Lantz, Joseph Michalsky, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, and Rainer Volkamer
Atmos. Meas. Tech., 9, 3893–3910, https://doi.org/10.5194/amt-9-3893-2016, https://doi.org/10.5194/amt-9-3893-2016, 2016
Short summary
Short summary
We present an inherently calibrated retrieval to measure aerosol optical depth (AOD) and the aerosol phase function parameter, g, based on measurements of azimuth distributions of the Raman scattering probability (RSP), the near-absolute rotational Raman scattering (RRS) intensity by the University of Colorado two-dimensional (2-D) MAX-DOAS. The retrievals are maximally sensitive at low AOD and do not require absolute radiance calibration. We compare results with data from independent sensors.
K. K. Shukla, K. Niranjan Kumar, D. V. Phanikumar, R. K. Newsom, V. R. Kotamarthi, T. B. M. J. Ouarda, and M. V. Ratnam
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-162, https://doi.org/10.5194/amt-2016-162, 2016
Revised manuscript not accepted
Short summary
Short summary
Estimation of Cloud base height was carried out by using various ground based instruments (Doppler Lidar and Ceilometer) and satellite datasets (MODIS) over central Himalayan region for the first time. The present study demonstrates the potential of Doppler Lidar in precise estimation of cloud base height and updraft velocities. More such deployments will be invaluable inputs for regional weather prediction models over complex Himalayan terrains.
Chun Zhao, Maoyi Huang, Jerome D. Fast, Larry K. Berg, Yun Qian, Alex Guenther, Dasa Gu, Manish Shrivastava, Ying Liu, Stacy Walters, Gabriele Pfister, Jiming Jin, John E. Shilling, and Carsten Warneke
Geosci. Model Dev., 9, 1959–1976, https://doi.org/10.5194/gmd-9-1959-2016, https://doi.org/10.5194/gmd-9-1959-2016, 2016
Short summary
Short summary
In this study, the latest version of MEGAN is coupled within CLM4 in WRF-Chem. In this implementation, MEGAN shares a consistent vegetation map with CLM4. This improved modeling framework is used to investigate the impact of two land surface schemes on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models.
Claire Pettersen, Ralf Bennartz, Mark S. Kulie, Aronne J. Merrelli, Matthew D. Shupe, and David D. Turner
Atmos. Chem. Phys., 16, 4743–4756, https://doi.org/10.5194/acp-16-4743-2016, https://doi.org/10.5194/acp-16-4743-2016, 2016
Short summary
Short summary
We examined four summers of data from a ground-based atmospheric science instrument suite at Summit Station, Greenland, to isolate the signature of the ice precipitation. By using a combination of instruments with different specialities, we identified a passive microwave signature of the ice precipitation. This ice signature compares well to models using synthetic data characteristic of the site.
Andrew M. Dzambo, David D. Turner, and Eli J. Mlawer
Atmos. Meas. Tech., 9, 1613–1626, https://doi.org/10.5194/amt-9-1613-2016, https://doi.org/10.5194/amt-9-1613-2016, 2016
Short summary
Short summary
Radiosondes are used to characterize the humidity in the middle and upper troposphere, but suffer from a solar radiation induced dry bias. This work investigates the accuracy of two published correction algorithms using comparisons with other instruments.
Y. Feng, V. R. Kotamarthi, R. Coulter, C. Zhao, and M. Cadeddu
Atmos. Chem. Phys., 16, 247–264, https://doi.org/10.5194/acp-16-247-2016, https://doi.org/10.5194/acp-16-247-2016, 2016
Short summary
Short summary
Aerosol radiative effects are of great importance for climate studies over South Asia, such as the weakening of the South Asian monsoon in the 20th century. This study reveals the altitude dependence of commonly underestimated aerosol radiative properties over this region. It further demonstrates the importance of constraining aerosol vertical distributions and partitioning of scattering vs absorbing aerosols in simulating the subsequent regional dynamical and hydrological responses to aerosols.
B. A. Drewniak, U. Mishra, J. Song, J. Prell, and V. R. Kotamarthi
Biogeosciences, 12, 2119–2129, https://doi.org/10.5194/bg-12-2119-2015, https://doi.org/10.5194/bg-12-2119-2015, 2015
L. K. Berg, M. Shrivastava, R. C. Easter, J. D. Fast, E. G. Chapman, Y. Liu, and R. A. Ferrare
Geosci. Model Dev., 8, 409–429, https://doi.org/10.5194/gmd-8-409-2015, https://doi.org/10.5194/gmd-8-409-2015, 2015
Short summary
Short summary
This work presents a new methodology for representing regional-scale impacts of cloud processing on both aerosol and trace gases in sub-grid-scale convective clouds. Using the new methodology, we can better simulate the aerosol lifecycle over large areas. The results presented in this work highlight the potential change in column-integrated amounts of black carbon, organic aerosol, and sulfate aerosol, which were found to range from -50% for black carbon to +40% for sulfate.
D. Müller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid
Atmos. Meas. Tech., 7, 3487–3496, https://doi.org/10.5194/amt-7-3487-2014, https://doi.org/10.5194/amt-7-3487-2014, 2014
E. Kassianov, J. Barnard, M. Pekour, L. K. Berg, J. Shilling, C. Flynn, F. Mei, and A. Jefferson
Atmos. Meas. Tech., 7, 3247–3261, https://doi.org/10.5194/amt-7-3247-2014, https://doi.org/10.5194/amt-7-3247-2014, 2014
A. J. Scarino, M. D. Obland, J. D. Fast, S. P. Burton, R. A. Ferrare, C. A. Hostetler, L. K. Berg, B. Lefer, C. Haman, J. W. Hair, R. R. Rogers, C. Butler, A. L. Cook, and D. B. Harper
Atmos. Chem. Phys., 14, 5547–5560, https://doi.org/10.5194/acp-14-5547-2014, https://doi.org/10.5194/acp-14-5547-2014, 2014
K. Van Tricht, I. V. Gorodetskaya, S. Lhermitte, D. D. Turner, J. H. Schween, and N. P. M. Van Lipzig
Atmos. Meas. Tech., 7, 1153–1167, https://doi.org/10.5194/amt-7-1153-2014, https://doi.org/10.5194/amt-7-1153-2014, 2014
V. S. Manoharan, R. Kotamarthi, Y. Feng, and M. P. Cadeddu
Atmos. Chem. Phys., 14, 1159–1165, https://doi.org/10.5194/acp-14-1159-2014, https://doi.org/10.5194/acp-14-1159-2014, 2014
G. Maschwitz, U. Löhnert, S. Crewell, T. Rose, and D. D. Turner
Atmos. Meas. Tech., 6, 2641–2658, https://doi.org/10.5194/amt-6-2641-2013, https://doi.org/10.5194/amt-6-2641-2013, 2013
M. P. Cadeddu, J. C. Liljegren, and D. D. Turner
Atmos. Meas. Tech., 6, 2359–2372, https://doi.org/10.5194/amt-6-2359-2013, https://doi.org/10.5194/amt-6-2359-2013, 2013
Y. Feng, V. Ramanathan, and V. R. Kotamarthi
Atmos. Chem. Phys., 13, 8607–8621, https://doi.org/10.5194/acp-13-8607-2013, https://doi.org/10.5194/acp-13-8607-2013, 2013
B. Drewniak, J. Song, J. Prell, V. R. Kotamarthi, and R. Jacob
Geosci. Model Dev., 6, 495–515, https://doi.org/10.5194/gmd-6-495-2013, https://doi.org/10.5194/gmd-6-495-2013, 2013
G. G. Palancar, B. L. Lefer, S. R. Hall, W. J. Shaw, C. A. Corr, S. C. Herndon, J. R. Slusser, and S. Madronich
Atmos. Chem. Phys., 13, 1011–1022, https://doi.org/10.5194/acp-13-1011-2013, https://doi.org/10.5194/acp-13-1011-2013, 2013
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines
Mesoscale weather systems and associated potential wind power variations in a midlatitude sea strait (Kattegat)
A large-eddy simulation (LES) model for wind-farm-induced atmospheric gravity wave effects inside conventionally neutral boundary layers
Simulating low-frequency wind fluctuations
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model
The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects
Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production
Improving Wind and Power Predictions via Four-Dimensional Data Assimilation in the WRF Model: Case Study of Storms in February 2022 at Belgian Offshore Wind Farms
Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: a comparison between two-parameter and three-parameter Weibull distributions
Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy
Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers
Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic
Lifetime prediction of turbine blades using global precipitation products from satellites
Evaluation of low-level jets in the southern Baltic Sea: a comparison between ship-based lidar observational data and numerical models
Predicting power ramps from joint distributions of future wind speeds
Research challenges and needs for the deployment of wind energy in hilly and mountainous regions
Observer-based power forecast of individual and aggregated offshore wind turbines
Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)
Jonathan Minz, Axel Kleidon, and Nsilulu T. Mbungu
Wind Energ. Sci., 9, 2147–2169, https://doi.org/10.5194/wes-9-2147-2024, https://doi.org/10.5194/wes-9-2147-2024, 2024
Short summary
Short summary
Estimates of power output from regional wind turbine deployments in energy scenarios assume that the impact of the atmospheric feedback on them is minimal. But numerical models show that the impact is large at the proposed scales of future deployment. We show that this impact can be captured by accounting only for the kinetic energy removed by turbines from the atmosphere. This can be easily applied to energy scenarios and leads to more physically representative estimates.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 1695–1711, https://doi.org/10.5194/wes-9-1695-2024, https://doi.org/10.5194/wes-9-1695-2024, 2024
Short summary
Short summary
In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We have observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, https://doi.org/10.5194/wes-9-1647-2024, 2024
Short summary
Short summary
We introduce a novel way to model the impact of atmospheric gravity waves (AGWs) on wind farms using high-fidelity simulations while significantly reducing computational costs. The proposed approach is validated across different atmospheric stability conditions, and implications of neglecting AGWs when predicting wind farm power are assessed. This work advances our understanding of the interaction of wind farms with the free atmosphere, ultimately facilitating cost-effective research.
Abdul Haseeb Syed and Jakob Mann
Wind Energ. Sci., 9, 1381–1391, https://doi.org/10.5194/wes-9-1381-2024, https://doi.org/10.5194/wes-9-1381-2024, 2024
Short summary
Short summary
Wind flow consists of swirling patterns of air called eddies, some as big as many kilometers across, while others are as small as just a few meters. This paper introduces a method to simulate these large swirling patterns on a flat grid. Using these simulations we can better figure out how these large eddies affect big wind turbines in terms of loads and forces.
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, https://doi.org/10.5194/wes-9-1153-2024, 2024
Short summary
Short summary
Tropical cyclone winds are challenging for wind turbines. We analyze a tropical cyclone before landfall in a mesoscale model. The simulated wind speeds and storm structure are sensitive to the boundary parametrization. However, independent of the boundary layer parametrization, the median change in wind speed and wind direction with height is small relative to wind turbine design standards. Strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1123–1152, https://doi.org/10.5194/wes-9-1123-2024, https://doi.org/10.5194/wes-9-1123-2024, 2024
Short summary
Short summary
This paper introduces the multi-scale coupled (MSC) model, an engineering framework aimed at modeling turbine–wake and wind farm–gravity wave interactions, as well as local and global blockage effects. Comparisons against large eddy simulations show that the MSC model offers a valid contribution towards advancing our understanding of the coupled wind farm–atmosphere interaction, helping refining power estimation methodologies for existing and future wind farm sites.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
Short summary
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Tsvetelina Ivanova, Sara Porchetta, Sophia Buckingham, Jeroen van Beeck, and Wim Munters
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-177, https://doi.org/10.5194/wes-2023-177, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This study explores how wind and power predictions can be improved by introducing local forcing of measurement data in a numerical weather model, while taking into account the presence of neighboring wind farms. Practical implications for the wind energy industry include insights for informed offshore wind farm planning and decision-making strategies using open-source models, even under adverse weather conditions.
Mohammad Golam Mostafa Khan and Mohammed Rafiuddin Ahmed
Wind Energ. Sci., 8, 1277–1298, https://doi.org/10.5194/wes-8-1277-2023, https://doi.org/10.5194/wes-8-1277-2023, 2023
Short summary
Short summary
A robust technique for wind resource assessment with a Bayesian approach for estimating Weibull parameters is proposed. Research conducted using seven sites' data in the tropical region from 1° N to 21° S revealed that the three-parameter (3-p) Weibull distribution with a non-zero shift parameter is a better fit for wind data that have a higher percentage of low wind speeds. Wind data with higher wind speeds are a special case of the 3-p distribution. This approach gives accurate results.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
Short summary
Short summary
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
Short summary
Short summary
The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
Short summary
Short summary
As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
Wind Energ. Sci., 7, 2497–2512, https://doi.org/10.5194/wes-7-2497-2022, https://doi.org/10.5194/wes-7-2497-2022, 2022
Short summary
Short summary
When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
Short summary
Short summary
A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon
Wind Energ. Sci., 7, 2393–2405, https://doi.org/10.5194/wes-7-2393-2022, https://doi.org/10.5194/wes-7-2393-2022, 2022
Short summary
Short summary
The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
Andrew Clifton, Sarah Barber, Alexander Stökl, Helmut Frank, and Timo Karlsson
Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, https://doi.org/10.5194/wes-7-2231-2022, 2022
Short summary
Short summary
The transition to low-carbon sources of energy means that wind turbines will need to be built in hilly or mountainous regions or in places affected by icing. These locations are called
complexand are hard to develop. This paper sets out the research and development (R&D) needed to make it easier and cheaper to harness wind energy there. This includes collaborative R&D facilities, improved wind and weather models, frameworks for sharing data, and a clear definition of site complexity.
Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
Short summary
Short summary
Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
Short summary
Short summary
The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
Cited articles
Abdolali, A., Roland, A., van der Westhuysen, A., Meixner, J., Chawla, A.,
Hesser, T. J., Smith, J. M., and Sikiric, M. D.: Large-scale hurricane
modeling using domain decomposition parallelization and implicit scheme
implemented in WAVEWATCH III wave model, Coast. Eng., 157, 103656,
https://doi.org/10.1016/j.coastaleng.2020.103656, 2020a.
Abdolali, A., Pringle, W. J., Roland, A., and Mehra, A.: Assessment of
Global Wave Models on Unstructured Domains, AGU Fall Meeting I Poster
Sessions, virtual, 1–17 December 2020, OS047-0001, https://doi.org/10.1002/essoar.10505107.1, 2020b.
Allaerts, D. and Meyers, J.: Boundary-layer development and gravity waves in
conventionally neutral wind farms, J. Fluid. Mech., 814, 95–130, https://doi.org/10.1017/jfm.2017.11, 2017.
Allaerts, D. and Meyers, J.: Gravity Waves and Wind-Farm Efficiency in
Neutral and Stable Conditions, Bound.-Lay. Meteorol., 166, 269–299,
https://doi.org/10.1007/s10546-017-0307-5, 2018.
Allaerts, D., Quon, E., Draxl, C., and Churchfield, M. J.: Development of a
time-height profile assimilation technique for large-eddy simulation,
Bound.-Lay. Meteorol.,
176, 329–348, https://doi.org/10.1007/s10546-020-00538-5, 2020.
Andreas, E. L., Mahrt, L., and Vickers, D.: A new drag relation for
aerodynamically rough flow over the ocean, J. Atmos. Sci., 69, 2520–2537,
https://doi.org/10.1175/JAS-D-11-0312.1, 2012.
Angevine, W., Hare, J. E., Fairall, C. W., Wolfe, D. E., Hill, R. J.,
Brewer, W. A., and White, A. B.: Structure and formation of the highly
stable marine boundary layer over the Gulf of Maine, J. Geophys. Res., 111,
D23S22, https://doi.org/10.1029/2006JD007465, 2006.
Annoni, J., Bay, C., Johnson, K., Dall'Anese, E., Quon, E., Kemper, T., and Fleming, P.: Wind direction estimation using SCADA data with consensus-based optimization, Wind Energy Sci., 4, 355–368, https://doi.org/10.5194/wes-4-355-2019, 2019.
Anvari, M., Lohmann, G., Wächter, M., Milan, P., Lorenz, E., Heinemann,
D., Tabar, M. R. R., and Peinke, J.: Short term fluctuations of wind and
solar power systems, New J. Phys., 18, 063027,
https://doi.org/10.1088/1367-2630/18/6/063027, 2016.
Archer, C. L., Colle, B. A., Veron, D. L., Veron, F., and Sienkiewicz, M.
J.: On the predominance of unstable atmospheric conditions in the marine
boundary layer offshore of the US northeastern coast, J. Geophys. Res.-Atmos., 121, 8869–8885, https://doi.org/10.1002/2016JD024896, 2016.
Arya, S. P.: Introduction to Micrometeorology, 2nd ed., Geophysics Series, volume 42, edited by: Dmowska, R. and Holton, J. R., Academic Press, ISBN: 0-12-059354-8,
2001.
ASTM (American Society for Testing and Materials): ASTM G73 – Standard Test Method for Liquid Impingement Erosion Using Rotating Apparatus, ASTM, 1–19, https://doi.org/10.1520/G0073-10R21, 2021.
Bak, C., Forsting, A. M., and Sørensen, N. N.: The influence of leading
edge roughness, rotor control and wind climate on the loss in energy
production, J. Phys. Conf. Ser., 1618, 052050, https://doi.org/10.1088/1742-6596/1618/5/052050, 2020.
Baldauf, M., Seifert, A., Forstner, J., Majewski, D., Raschendorfer, M., and
Reinhardt, T.: Operational convective-scale numerical weather prediction
with the COSMO Model: Description and sensitivities, Mon. Weather Rev.,
139, 3887–3905, 2011.
Balluff, S., Bendfeld, J., and Krauter, S.: Short term wind and energy
prediction for offshore wind farms using neural networks, 2015 International
Conference on Renew. Energ. Research and Applications (ICRERA), Palermo, Italy, 22–25 November 2015,
379–382, https://doi.org/10.1109/ICRERA.2015.7418440, 2015.
Banta, R. M., Pichugina, Y. L., Kelley, N. D., Hardesty, R. M., and Brewer,
W. A.: Wind Energy Meteorology: Insight into Wind Properties in the
Turbine-Rotor Layer of the Atmosphere from High-Resolution Doppler Lidar,
B. Am. Meteorol. Soc., 94, 883–902, https://doi.org/10.1175/BAMS-D-11-00057.1, 2013.
Banta, R. M., Pichugina, Y. L., Brewer, W. A., James, E. P., Olson, J. B.,
Benjamin, S. G., Carley, J. R., Bianco, L., Djalalova, I. V., Wilczak, J.
M., Hardesty, R. M., Cline, J., and Marquis, M. C.: Evaluating and Improving
NWP Forecast Models for the Future: How the Needs of Offshore Wind Energy
Can Point the Way, B. Am. Meteorol. Soc., 99, 1155–1176, https://doi.org/10.1175/BAMS-D-16-0310.1, 2018.
Barthelmie, R. J., Dantuono, K. E., Renner, E. J., Letson, F. L., and Pryor,
S. C.: Extreme Wind and Waves in US East Coast Offshore Wind Energy Lease
Areas, Energies 2021, 14, 1053, https://doi.org/10.3390/en14041053, 2021.
Bech, J. I., Hasager, C. B., and Bak, C.: Extending the life of wind turbine blade leading edges by reducing the tip speed during extreme precipitation events, Wind Energy Sci., 3, 729–748, https://doi.org/10.5194/wes-3-729-2018, 2018.
Berg, L. K., Newsom, R. K., and Turner, D. D.: Year-Long Vertical Velocity
Statistics Derived From Doppler Lidar in the Continental Convective Boundary
Layer, J. Appl. Meteorol. Clim., 56, 2441–2454,
https://doi.org/10.1175/JAMC-D-16-0359.1, 2017.
Bessac, J., Monahan, A. H., Christensen, H. M., and Weitzel, N.: Stochastic
parameterization of subgrid-scale velocity enhancement of sea surface
fluxes, Mon. Weather Rev., 147, 1447–1469, https://doi.org/10.1175/MWR-D-18-0384.1, 2019.
Bianco, L., and Wilczak, J.: Convective Boundary-Layer Depth: Improved
Measurement by Doppler Radar Wind Profile Using Fuzzy Logic Methods, J.
Atmos. Oceanic Tech., 19, 1745–1758, 2002.
Bodini, N., Lundquist, J. K., and Kirincich, A.: Offshore wind turbines will
encounter very low atmospheric turbulence, J. Phys. Conf. Ser., 1452, 012023,
https://doi.org/10.1088/1742-6596/1452/1/012023, 2020.
Borvarán, D., Peña, A., and Gandoin, R.: Characterization of
offshore vertical wind shear conditions in Southern New England, Wind
Energy, 24, 465–480, https://doi.org/10.1002/we.2583, 2021.
Bossanyi, E.: Combining induction control and wake steering for wind farm
energy and fatigue loads optimisation, J. Phys. Conf. Ser., 1037, 032011,
https://doi.org/10.1088/1742-6596/1037/3/032011, 2018.
Bossuyt, J., Meneveau, C., and Meyers, J.: Wind farm power fluctuations and
spatial sampling of turbulent boundary layers, J. Fluid Mech., 823,
329–344, https://doi.org/10.1017/jfm.2017.328, 2017.
Bright, R. J., Lian, X., and Pietrafesa, L. J.: Evidence of the Gulf
Stream's influence on tropical cyclone intensity, Geophys. Res. Lett., 29,
1801–1804, https://doi.org/10.1029/2002GL014920, 2002.
Brower, M. C. (Ed.), Bailey, B. H., Beaucage, P., Bernadett, D. W., Doane, J., Eberhard, M. J., Elsholz, K., V., Filippelli, M. V., Hale, E., Markus, M. J., Ryan, D., Taylor, M. A., and Tensen, J. C.: Wind Resource Assessment: A Practical Guide to Developing a Wind Project, 1st edn., Wiley, 280 pp., ISBN: 978-1-118-02232-0, 2012.
Browne, P. A., de Rosnay, P., Zuo, H., Bennett, A., and Dawson, A.: Weakly
coupled ocean-atmosphere data assimilation in the ECMWF NWP system, Remote
Sens., 11, 234, https://doi.org/10.3390/rs11030234, 2019.
Burk, S. D. and Thompson, W. T.: The summertime low-level jet and marine
boundary layer structure along the California coast, Mon. Weather Rev., 124,
668–686, 1996.
Businger, S., Graziano, T. M., Kaplan, M. L., and Rozumalski, R. A.: Cold-air
cyclogenesis along the Gulf-Stream front: Investigation of diabatic impacts
on cyclone development, frontal structure, and track, Meteor. Atmos. Phys.,
88, 65–90, https://doi.org/10.1007/s00703-003-0050-y, 2005.
Butterfield, S., Musial, W., Jonkman, J., and Sclavounos, P.: Engineering
challenges for floating offshore wind turbines, Tech. rep., National
Renew. Energ. Laboratory (NREL), Golden, CO, https://www.nrel.gov/docs/fy07osti/38776.pdf (last access: 20 November 2022), 2007.
Chalikov, D.: The parameterization of the wave boundary layer, J. Phys. Oceanogr., 25, 1333–1349, 1995.
Chamorro, L. P. and Porté-Agel, F.: A Wind-Tunnel Investigation of
Wind-Turbine Wakes: Boundary-Layer Turbulence Effects, Bound.-Lay. Meteorol., 132, 129–149, https://doi.org/10.1007/s10546-009-9380-8, 2009.
Chamorro, L. P. and Porté-Agel, F.: Turbulent flow inside and above a
wind farm: a wind-tunnel study, Energies, 4, 1916–1936, 2011.
Chamorro, L. P., Lee, S.-J., Olsen, D., Milliren, C., Marr, J., Arndt, R. E.
A., and Sotiropoulos, F.: Turbulence effects on a full-scale 2.5 MW
horizontal-axis wind turbine under neutrally stratified conditions:
Turbulence effects on a full-scale wind turbine, Wind Energy, 18, 339–349,
https://doi.org/10.1002/we.1700, 2015.
Chelton, D. B., Esbensen, S. K., Schlax, M. G., Thum, N., Freilich, M. H.,
Wentz, F. J., Gentemann, C. L., McPhaden, M. J., and Schopf, P. S.:
Observations of coupling between surface wind stress and sea surface
temperature in the eastern tropical Pacific, J. Climate, 14,
1479–1498, 2001.
Chen, S. S., Price, J. F., Zhao, W., Donelan, M. A., and Walsh, E. J.: The
CBLAST-Hurricane program and the next-generation fully coupled
atmosphere-wave-ocean models for hurricane research and prediction, B.
Am. Meteorol. Soc., 88, 311–317, https://doi.org/10.1175/BAMS-88-3-311, 2007.
Christiansen, M. B. and Hasager, C. B.: Wake effects of large offshore wind
farms identified from satellite SAR, Remote Sens. Environ., 98, 251–268,
2005.
Cifuentes-Lorenzen, A., Edson, J., and Zappa, C.: Air–Sea Interaction in
the Southern Ocean: Exploring the Height of the Wave Boundary Layer at the
Air–Sea Interface, Bound.-Lay. Meteorol., 169, 461–482, https://doi.org/10.1007/s10546-018-0376-0, 2018.
Colle, B. A., Sienkiewicz, M. J., Archer, C., Veron, D., Veron, F., Kempton,
W., and Mak, J. E.: Improving the Mapping and Prediction of Offshore Wind
Resources (IMPOWR): Experimental Overview and First Results, B. Am. Meteorol. Soc., 97, 1377–1390, https://doi.org/10.1175/BAMS-D-14-00253.1, 2016.
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017.
Crespo, A. and Hernández, J.: Turbulence characteristics in
wind-turbine wakes, J. Wind Eng. Ind. Aerod.,
61, 71–85, https://doi.org/10.1016/0167-6105(95)00033-X, 1996.
Dashtkar, A., Hadavinia, H., Sahinkaya, M. N., Williams, N. A., Vahid, S.,
Ismail, F., and Turner, M.: Rain erosion-resistant coatings for wind turbine
blades: A review, Polymers and Polymer Composites, Polym. Polym. Compos., 27, 443–475, 2019.
Debnath, M., Doubrawa, P., Optis, M., Hawbecker, P., and Bodini, N.: Extreme wind shear events in US offshore wind energy areas and the role of induced stratification, Wind Energy Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, 2021.
DeCosmo, J., Katsaros, K. B., Smith, S. D., Anderson, R. J., Oost, W. A.,
Bumke, K., and Chadwick, H.: Air-sea exchange of water vapor and sensible
heat: The Humidity Exchange Over the Sea (HEXOS) results, J. Geophys. Res.-Oceans, 101, 12001– 12016, 1996.
Deskos, G., Payne, G. S., Gaurier, B., and Graham, M.: On the spectral
behaviour of the turbulence-driven power fluctuations of horizontal-axis
turbines, J. Fluid Mech., 904, A13, https://doi.org/10.1017/jfm.2020.681,
2020.
Deskos, G., Lee, J. C. Y., Draxl, C., and Sprague, M. A.: Review of
Wind–Wave Coupling Models for Large-Eddy Simulation of the Marine
Atmospheric Boundary Layer, J. Atmos. Sci., 78,
3025–3045, https://doi.org/10.1175/JAS-D-21-0003.1, 2021.
Dettling, S., Kosovic, B., Gagne, D. J., and Haupt, S. E.: Machine-Learning Model for Surface Layer Parameterization at the Air-Sea Interface, 20th Conference on Artificial Intelligence for Environmental Science – Blending Artificial Intelligence with Numerical Weather and Climate Models, AMS Annual Meeting, 12 January 2021, virtual, 6.8, https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/384006 (last access: 20 November 2022), 2021.
Di Giuseppe, F., Riccio, A., Caporaso, L., Bonafé, G., Gobbi, G. P., and
Angelini, F.: Automatic detection of atmospheric boundary layer height using
ceilometer backscatter data assisted by a boundary layer model, Q. J. R.
Meteorol. Soc., 138, 649–663, https://doi.org/10.1002/qj.964, 2012.
Dilip, D. and Porté-Agel, F.: Wind Turbine Wake Mitigation through
Blade Pitch Offset, Energies, 10, 757, https://doi.org/10.3390/en10060757, 2017.
Djalalova, I. V., Olson, J., Carley, J. R., Bianco, L., Wilczak, J. M.,
Pichugina, Y., Banta, R., Marquis, M., and Cline, J.: The POWER Experiment:
impact of assimilation of a network of coastal wind profiling radars on
simulating offshore winds in and above the wind turbine layer, Weather
Forecast., 31, 1071–1091, https://doi.org/10.1175/WAF-D-15-0104.1, 2016.
DNV-GL: NYSERDA Floating LiDAR Buoy Data, DNV-GL [data set], https://oswbuoysny.resourcepanorama.dnvgl.com/download/f67d14ad-07ab-4652-16d2-08d71f257da1 (last access: 16 November 2022), 2020.
DOC/NOAA: NOAA Study to Inform Meteorological Observation for Offshore Wind
Positioning of Offshore Wind Energy Resources (POWER), Department of
Commerce/National Atmospheric and Oceanographic Administration, https://www.esrl.noaa.gov/gsd/renewable/AMR_DOE-FinalReport-POWERproject-1.pdf (last access: 4 October 2021), 2014.
DOE: Workshop on Research Needs for Offshore Wind Resource Characterization:
Summary Report, US Department of Energy, https://doi.org/10.2172/1572142, 2019.
Donelan, M. A.: Air-sea interaction, Ocean Engineering Science, 1st edn., 9B, edited by: Lé Meháute, B. and Hanes, D. M.,
239–292, John Wiley and Sons, ISBN: 978-0471633938, 1990.
Dörenkämper, M., Optis, M., Monahan, A., and Steinfeld, G.: On the
offshore advection of boundary-layer structures and the influence on
offshore wind conditions, Bound.-Lay. Meteorol., 155, 459–482, https://doi.org/10.1007/s10546-015-0008-x, 2015.
Dowell, D. C., Alexander, C. R., James, E. P., Weygandt, S. S., Benjamin, S. G., Manikin, G. S., Blake, B. T., Brown, J. M., Olson, J. B., Hu, M., Smirnova, T. G., Ladwig, T., Kenyon, J. S., Ahmadov, R., Turner, D. D., Duda, J. D., and Alcott, T. I.: The High-Resolution Rapid Refresh (HRRR): An hourly updating convection-allowing forecast 25 model. Part 1: Motivation and system description, Weather Forecast., 37, 1371–1395, https://doi.org/10.1175/WAF-D-21-0151.1, 2022.
Draxl, C., Allaerts, D., Quon, E., and Churchfield, M.: Coupling mesoscale
budget components to large-eddy simulations for wind energy applications, Bound.-Lay. Meteorol., 179, 73–98,
https://doi.org/10.1007/s10546-020-00584-z, 2021.
Edson, J., Paluszkiewicz, T., Sandgathe, S., Vincent, L., Goodman, L.,
Curtin, T., Hollister, J., Colton, M., Anderson, S., Andreas, E., and Burk,
S.: Coupled marine boundary layers and air-sea interaction initiative:
combining process studies, simulations, and numerical models, Office of
Naval Research, https://www.whoi.edu/science/AOPE/dept/r5.pdf (last access: 20 November 2022), 1999.
Edson, J., Crawford, T., Crescenti, J., Farrar, T., Frew, N., Gerbi, G.,
Helmis, C., Hristov, T., Khelif, D., Jessup, A., and Jonsson, H.: The
coupled boundary layers and air–sea transfer experiment in low winds.
B. Am. Meteorol. Soc., 88, 341–356, 2007.
Edson, J. B. and Fairall, C. W.: Similarity relationships in the marine
atmospheric surface layer for terms in the TKE and scalar variance budgets,
J. Atmos. Sci., 55, 2311–2328, 1998.
Edson, J. B., Jampana, V., Weller, R. A., Bigorre, S. P., Plueddemann, A.
J., Fairall, C. W., Miller, S. D., Mahrt, L., Vickers, D., and Hersbach,
H.: On the exchange of momentum over the open ocean, J. Phys. Oceanog.,
43, 1589–1610, https://doi.org/10.1175/JPO-D-12-0173.1, 2013.
Eisenberg, D., Laustsen, S., and Stege, J.: Wind turbine blade coating
leading edge rain erosion model: Development and validation, Wind Energy, 21, 942–951,
1–10, https://doi.org/10.1002/we.2200, 2018.
Emeis, S.: Wind energy meteorology: atmospheric physics for wind power
generation, Green Energy and Technology, 2nd edn., Springer, ISBN: 978-3-030-10278-4, 2018.
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., and Edson, J.
B.: Bulk Parameterization of Air–Sea Fluxes: Updates and Verification for
the COARE Algorithm, J. Climate, 16, 571–591,
https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2, 2003.
Fairman, J. G., Schultz, D. M., Kirshbaum, D. J., Gray, S. L., and Barrett,
A. I.: Climatology of size, shape, and intensity of precipitation features
over Great Britain and Ireland, J. Hydrometeor., 18, 1595–1615, https://doi.org/10.1175/JHM-D-16-0222.1, 2017.
Field, P. R., Cotton, R. J., McBeath, K., Lock, A. P., Webster, S., and
Allan, R. P.:Improving a convection-permitting model simulation of a cold
air outbreak, Quart. J. Roy. Meteor. Soc., 140, 124–138, https://doi.org/10.1002/qj.2116, 2014.
Finger, A.: The role of the research platforms FINO 1–3 in the technical and
ecological support research on offshore wind energy use,
https://www.osti.gov/etdeweb/servlets/purl/21060112 (last access: 21 November 2022), 2007.
Fischer, P., Kerkemeier, S., Min, M., Lan, Y.-H., Phillips, M., Rathnayake,
T., Merzari, E., Tomboulides, A., Karakus, A., Chalmers, N., and Warburton,
T.: NekRS, a GPU-Accelerated Spectral Element Navier-Stokes Solver, ArXiv,
http://arxiv.org/abs/2104.05829 (last access: 21 November 2022), 2021.
Fleming, P., Annoni, J., Shah, J. J., Wang, L., Ananthan, S., Zhang, Z., Hutchings, K., Wang, P., Chen, W., and Chen, L.: Field test of wake steering at an offshore wind farm, Wind Energy Sci., 2, 229–239, https://doi.org/10.5194/wes-2-229-2017, 2017.
Fleming, P., King, J., Dykes, K., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Lopez, H., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1, Wind Energy Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, 2019.
Foreman, R. J. and Emeis, S.: Revisiting the Definition of the Drag
Coefficient in the Marine Atmospheric Boundary Layer, J. Phys. Oceanogr., 40, 2325–2332, 2010.
Frandsen, S. T., Barthelmie, R. J., Rathmann, O., Jørgensen, H. E.,
Badger, J., Hansen, K., Ott, S., Rethore, P. E., Larsen, S. E., and Jensen,
L. E.: Summary report: The shadow effect of large wind farms: measurements,
data analysis and modeling, Risø National Laboratory/DTU,
Risø-R-1615 (EN), 35 pp., https://orbit.dtu.dk/en/publications/summary-report-the-shadow-effect (last access: 21 November 2022), 2007.
Friehe, C. A., Shaw, W. J., Rogers, D. P., Davidson, K. L., Large, W. G.,
Stage, S. A., Crescenti, G. H., Khalsa, S. J. S., Greenhut, G. K., and Li,
F.: Air-sea fluxes and surface layer turbulence around a sea surface
temperature front, J. Geophys. Res.-Oceans, 96,
8593–8609, 1991.
Frolov, S., Bishop, C. H., Holt, T., Cummings, J., and Knuth, D.:
Facilitating strongly coupled ocean-atmosphere data assimilation with an
interface solver, Month. Weather Rev., 144, 3–20, https://doi.org/10.1175/MWR-D-15-0041.1,
2016.
Fytanidis, D. K., Maulik, R., Balakrishnan, R., and Kotamarthi, R.: A
physics-informed data-driven low order model for the wind velocity deficit
at the wake of isolated buildings (Report #ANL-21/24), Argonne National
Laboratory, https://doi.org/10.2172/1782670, 2021.
Gadde, S. N. and Stevens, R. J. A. M.: Interaction between low-level jets
and wind farms in a stable atmospheric boundary layer, Phys. Rev. Fluids, 6, 014603,
https://doi.org/10.1103/PhysRevFluids.6.014603, 2021.
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter,
G., Abbas, N., Meng, F., Bortolotti, P., Skrzypiński, W. R., Scott, G.,
Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.:
Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine, National Renew. Energ. Laboratory, Golden,
CO, NREL/TP-5000-75698, https://www.nrel.gov/docs/fy20osti/75698.pdf (last access: 21 November 2022), 2020.
Gagne, D. J., McCandless, T. C., Kosovic, B., DeCastro, A., Loft, R. D., Haupt, S. E., and Yang, B.: Machine Learning Parameterization of the Surface Layer: Integration with WRF, in: 19th Conference on Artificial Intelligence for Environmental Science, Paper J67.3, 100th American Meteorological Society Annual Meeting, 13–16 January 2020, Boston, https://ams.confex.com/ams/2020Annual/webprogram/Paper366993.html (last access: 22 November 2022), 2020.
Garratt, J. R.: The internal boundary layer – A review, Bound.-Lay.
Meteorol., 50, 171–203, https://doi.org/10.1007/BF00120524, 1990.
Garratt, J. R.: The Atmospheric Boundary Layer, Cambridge Atmospheric and space science series, 1st paperback edn. (with corrections), edited by: Houghton, J. T., Rycroft, M. J., and Dessler, A. J., Cambridge University Press, ISBN: 0-521-46745-4, 1994.
GE Renew. Energ.: Haliade-X offshore wind turbine, https://www.ge.com/renewableenergy/wind-energy/offshore-wind/haliade-x-offshore-turbine (last access: 22 November 2022),
2019.
Geernaert, G. L.: Bulk parameterizations for the wind stress and heat fluxes, in: Surface Waves and Fluxes, 1st edn., Environmental Fluid Mechanics, Vol. 1, edited by: Geernaert, G. L. and Plant, W. J., Kluwer Academic, 91–172, ISBN: 0-7923-0809-3, 1990.
Geernaert, G. L., Katsaros, K. B., and Richter, K.: Variation of the drag
coefficient and its dependence on sea state, J. Geophys. Res.-Oceans, 91, 7667–7679, 1986.
Geernaert, G. L., Larsen, S. E., and Hansen, F.: Measurements of the wind
stress, heat flux, and turbulence intensity during storm conditions over the
North Sea, J. Geophys. Res.-Oceans, 92, 13127–13139,
1987.
Gettelman, A., Gagne, D. J., Chen, C.-C., Christensen, M. W., Lebo, Z. J.,
Morrison, H., and Gantos, G.: Machine learning the warm rain process, J. Adv.
Model. Earth Sy., 13, e2020MS002268, https://doi.org/10.1029/2020MS002268, 2021.
Gionfra, N., Sandou, G., Siguerdidjane, H., Faille, D., and Loevenbruck, P.:
A distributed consensus control under disturbances for wind farm power
maximization, in: 2017 IEEE 56th Annual Conference on Decision and Control
(CDC), IEEE 56th Annual Conference on Decision and Control (CDC),
Melbourne, Australia, 12–15 December 2017, 2015–2020, https://doi.org/10.1109/CDC.2017.8263944,
2017.
Gottschall, J., Wolken-Möhlmann, G., Viergutz, T., and Lange, B.:
Results and Conclusions of a Floating-lidar Offshore Test, Energy Proc.,
53, 156–161, https://doi.org/10.1016/j.egypro.2014.07.224, 2014.
Grossman, R. L. and Betts, A. K.: Air–Sea Interaction during an Extreme
Cold Air Outbreak from the Eastern Coast of the United States, Mon. Weather
Rev., 118, 324–342, 1990.
Gryning, S. E., Batchvarova, E., Brümmer, B., Jørgensen, H., and
Larsen, S.: On the extension of the wind profile over homogeneous terrain
beyond the surface boundary layer, Bound.-Lay. Meteorol., 124,
251–268, 2007.
Gualtieri, G.: A comprehensive review on wind resource extrapolation models
applied in wind energy, Renew. Sust. Energ. Rev., 102,
215–233, 2019.
Hanley, K. E., Belcher, S. E., and Sullivan, P. P.: A global climatology of
wind–wave interaction, J. Phys. Oceanogr., 40,
1263–1282, 2010.
Hao, X., Cao, T., Yang, Z., Li, T., and Shen, L.: Simulation-based study of
wind-wave interaction, Procedia IUTAM, 26, 162–173, https://doi.org/10.1016/j.piutam.2018.03.016, 2018.
Hare, J. E., Hara, T., Edson, J. B., and Wilczak, J. M.: A similarity
analysis of the structure of airflow over surface waves, J. Phys. Oceanogr., 27, 1018–1037, 1997.
Hasager, C. B., Nygaard, N. G., Volker, P. J. H., Karagali, I., Andersen, S.
J., and Badger, J.: Wind Farm Wake: The 2016 Horns Rev Photo Case, Energies,
10, 317, https://doi.org/10.3390/en10030317, 2017.
Hasager, C. B., Vejen, F., Bech, J. I., Skrzypiński, W. R., Tilg, A.-M.,
and Nielsen, M.: Assessment of the rain and wind climate with focus on wind
turbine blade leading edge erosion rate and expected lifetime in Danish
Seas, Renew. Energ., 149, 91–102, https://doi.org/10.1016/j.renene.2019.12.043,
2020.
Hasager, C. B., Vejen, F., Skrzypiński, W. R., and Tilg, A.-M.: Rain
Erosion Load and Its Effect on Leading-Edge Lifetime and Potential of
Erosion-Safe Mode at Wind Turbines in the North Sea and Baltic Sea, Energies,
14, 1959, https://doi.org/10.3390/en14071959, 2021.
Haupt, S. E., Kosovic, B., Shaw, W., Berg, L. K., Churchfield, M., Cline,
J., Draxl, C., Ennis, B., Koo, E., Kotamarthi, R., Mazzaro, L., Mirocha, J.,
Moriarty, P., Muñoz-Esparza, D., Quon, E., Rai, R. K., Robinson, M., and
Sever, G.: On Bridging a Modeling Scale Gap: Mesoscale to Microscale
Coupling for Wind Energy, B. Am. Meteorol. Soc., 100, 2533–2550, https://doi.org/10.1175/BAMS-D-18-0033.1, 2019.
Helmis, C. G., Wang, Q., Sgouros, G., Wang, S., and Halios, C.:
Investigating the Summertime Low-Level Jet Over the East Coast of the
USA.: A Case Study, Bound.-Lay. Meteorol., 149, 259–276, https://doi.org/10.1007/s10546-013-9841-y, 2013.
Herring, R., Dyer, K., Martin, F., and Ward, C.: The increasing importance
of leading edge erosion and a review of existing protection solutions,
Renew. Sust. Energ. Rev., 115, 109382, https://doi.org/10.1016/j.rser.2019.109382, 2019.
Holtslag, M. C., Bierbooms, W. A. A. M., and van Bussel, G. J. W.: Extending
the diabatic surface layer wind shear profile for offshore wind energy,
Renew. Energ., 101, 96–110, 2017.
Hong, X., Martin, P. J., Wang, S., and Rowley, C.: High SST variability
south of Martha's Vineyard, J. Mar. Sys., 78, 59–76, 2009.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., and Xie,
P.: NASA Global Precipitation Measurement Integrated Multi-satellitE
Retrievals for GPM (IMERG), Algorithm Theoretical Basis Doc., version 4.4,
30 pp., https://pps.gsfc.nasa.gov/Documents/IMERG_ATBD_V4.pdf (last access: 22 November 2022), 2014.
Husain, N. T., Hara, T., Buckley, M. P., Yousefi, K., Veron, F., and
Sullivan, P. P.: Boundary Layer Turbulence over Surface Waves in a Strongly
Forced Condition: LES and Observation, J. Phys. Oceanogr., 49, 1997–2015,
https://doi.org/10.1175/JPO-D-19-0070.1, 2019.
Igel, A. L., van den Heever, S., and Johnson, J. S.: Meteorological and Land
Surface Properties Impacting Sea Breeze Extent and Aerosol Distribution in a
Dry Environment, J. Geophys. Res., 123, 22–37, https://doi.org/10.1002/2017JD027339, 2017.
International Electrotechnical Commission: IEC 61400-1:2019-02: Wind energy generation systems – Part 1: Design Requirements, 4th edn.,
https://standards.iteh.ai/catalog/standards/iec/3454e370-7ef2-468e-a074-7a5c1c6cb693/iec-61400-1-2019
(last access: 15 July 2020), 2019.
IRENA: Future of Wind: Deployment, investment, technology, grid integration
and socio-economic aspects, International Renew. Energ. Agency,
https://www.irena.org/publications/2019/Oct/Future-of-wind (last access: 22 November 2022), 2019.
Jacob, R., Larson, J., and Ong, E.: M×N communication and
parallel interpolation in community climate system model version 3 using the
model coupling toolkit, Int. J. High Perform. Comput. Appl., 19,
293–307, https://doi.org/10.1177/1094342005056116, 2005.
Jacox, M., Alexander, M. A., and Stock, C. A.: On the skill of seasonal sea
surface temperature forecasts in the California Current System and its
connection to ENSO variability, Clim. Dyn., 53, 7519–7533, https://doi.org/10.1007/s00382-017-3608-y, 2019.
James, E. P. Alexander, C. R., Dowell, D. C., Weygandt, S. S., Benjamin, S. G., Manikin, G. S., Brown, J. M., Olson, J. B., Hu, M., Smirnova, T. G., Ladwig, T., Kenyon, J. S., and Turner, D. D.: The High-Resolution Rapid Refresh (HRRR): An hourly updating convection-allowing forecast model. Part II: Forecast performance, Weather Forecast., 37, 1397–1417, https://doi.org/10.1175/WAF-D-21-0130.1, 2022.
Janssen, P. A. E. M.: The interaction of ocean waves and wind, Cambridge University
Press, Cambridge, UK, https://doi.org/10.1017/CBO9780511525018, 2004.
Jiang, G. Q., Xu, J., and Wei, J.: A Deep Learning Algorithm of Neural
Network for the Parameterization of Typhoon-Ocean Feedback in Typhoon
Forecast Models, Geophys. Res. Lett., 45, 3706–3716, https://doi.org/10.1002/2018GL077004, 2018.
Jiang, H. and Edson, J. B.: Characterizing marine atmospheric boundary
layer to support offshore wind energy research, J. Phys.
Conf. Ser., 1452, 012027, https://doi.org/10.1088/1742-6596/1452/1/012027, 2020.
Jiménez, P. A. and Dudhia, J.: On the Need to Modify the Sea Surface
Roughness Formulation over Shallow Waters, J. Appl. Meteorol. Clim., 57, 1101–1110, 2018.
Kalverla, P. C., Steeneveld, G.-J., Ronda, R. J., and Holtslag, A. A. M.: An
observational climatology of anomalous wind events at offshore meteomast
IJmuiden (North Sea), J. Wind Eng. and Ind. Aerodyn., 165, 86–89, https://doi.org/10.1016/j.jweia.2017.03.008, 2017.
Kalvig, S., Gudmestad, O. T., and Winther, N.: Exploring the gap between
`best knowledge' and `best practice' in boundary layer meteorology for
offshore wind energy, Wind Energy, 17, 161–171, https://doi.org/10.1002/we.1572, 2014.
Kapoor, A., Ouakka, S., Arwade, S. R., Lundquist, J. K., Lackner, M. A., Myers, A. T., Worsnop, R. P., and Bryan, G. H.: Hurricane eyewall winds and structural response of wind turbines, Wind Energy Sci., 5, 89–104, https://doi.org/10.5194/wes-5-89-2020, 2020.
Kathiravelu, G., Lucke, T., and Nichols, P.: Rain Drop Measurement
Techniques: A Review, Water-SUI, 8, 29, https://doi.org/10.3390/w8010029, 2016.
Keegan, M. H., Nash, D. H., and Stack, M. M.: On erosion issues associated
with the leading edge of wind turbine blades, J. Phys. D Appl. Phys., 46, 383001, https://doi.org/10.1088/0022-3727/46/38/383001, 2013.
Kelley, N. D.: Turbulence-Turbine Interaction: The Basis for the Development
of the TurbSim Stochastic Simulator, Tech. Rep. NREL/TP-5000-52353, https://doi.org/10.2172/1031981,
2011.
Kelley, N. D., Jonkman, B. J., and Scott, G. N.: The Great Plains turbulence
environment: its origins,
impact and simulation, AWEA 2006 WindPower Conference, Pittsburgh, Pennsylvania, 4–7 June 2006,
NREL/CP-500-40176, https://www.nrel.gov/docs/fy07osti/40176.pdf (last access: 24 November 2022), 2006.
Khairoutdinov, M., Randall, D., and DeMott, C.: Simulations of the
Atmospheric General Circulation Using a Cloud-Resolving Model as a
Superparameterization of Physical Processes, J. Atmos. Sci., 62, 2136–2154,
https://doi.org/10.1175/JAS3453.1, 2005.
Khain, A. P. and Lynn, B.: Simulation of a super cell storm in clean and
dirty atmosphere, J. Geophys. Res., 114, D19209,
https://doi.org/10.1029/2009JD011827, 2009.
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O.,
Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., van den
Heever, A. C., and Yano, J.-I.: Representation of microphysical processes in
cloud-resolving models: Spectral (bin) microphysics versus bulk
parameterization, Rev. Geophys., 53, 247–322, https://doi.org/10.1002/2014RG000468, 2015.
Kim, E., Manuel, L., Curcic, M., Chen, S. S., Phillips, C., and Veers, P.: On the use of coupled wind, wave, and current fields in the simulation of loads on bottom-supported offshore wind turbines during hurricanes: March 2012–September 2015, Tech. rep. NREL/TP-5000-65283, https://doi.org/10.2172/1266702, 2016.
Kirincich, A.: A metocean reference station for offshore wind energy
research in the US, J. Phys. Conf. Ser., 1452, 012028, https://doi.org/10.1088/1742-6596/1452/1/012028, 2020.
Lagerquist, R., Turner, D. D., Ebert-Uphoff, I., Stewart, J., and Hagerty,
V.: Using deep learning to emulate and accelerate a radiative transfer
model, J. Atmos. Ocean. Technol., 38, 1673–1696,
https://doi.org/10.1175/JTECH-D-21-0007.1, 2021.
Lambaerts, J., Lapeyre, G., Plougonven, R., and Klein, P.: Atmospheric
response to sea surface temperature mesoscale structures, J. Geophys. Res.-Atmos., 118, 9611–9621, https://doi.org/10.1002/jgrd.50769,
2013.
Larson, J., Jacob, R., and Ong, E.: The model coupling toolkit: A new
Fortran90 toolkit for building multiphysics parallel coupled models, Int. J.
High Perform. Comput. Appl., 19, 277–292, https://doi.org/10.1177/1094342005056115,
2005.
Le, M., and Chandrasekar, V.: An algorithm for drop-size distribution retrieval from GPM dual-frequency precipitation radar, IEEE T. Geosci. Remote Sens., 52, 7170–7185, https://doi.org/10.1109/TGRS.2014.2308475, 2014.
Lee, S., Churchfield, M. J., Moriarty, P. J., Jonkman, J., and Michalakes,
J.: A Numerical Study of Atmospheric and Wake Turbulence Impacts on Wind
Turbine Fatigue Loadings, J. Sol. Energy Eng., 135, 031001, https://doi.org/10.1115/1.4023319,
2013.
Letson, F., Barthelmie, R. J., and Pryor, S. C.: Radar-derived precipitation climatology for wind turbine blade leading edge erosion, Wind Energy Sci., 5, 331–347, https://doi.org/10.5194/wes-5-331-2020, 2020a.
Letson, F., Shepherd, T. J., Barthelmie, R. J., and Pryor, S. C.: Modelling
Hail and Convective storms with WRF for Wind Energy Applications, J. Phys.
Conf. Ser., 1452 012051, https://doi.org/10.1088/1742-6596/1452/1/012051, 2020b.
Li, G., Curcic, M., Iskandarani, M., Chen, S. S., and Knio, O. M.:
Uncertainty propagation in coupled atmosphere-wave-ocean prediction system:
A study of Hurricane Earl (2010), Mon. Weather Rev., 147, 221–245,
https://doi.org/10.1175/MWR-D-17-0371.1, 2019.
Li, X., Tao, W.-K., Khain, A. P., Simpson, J., and Johnson, D. E.:
Sensitivity of a cloud-resolving model to bulk and explicit bin
microphysical schemes. Part I: Validation with a PRE-STORM case, J. Atmos.
Sci., 66, 3–21, https://doi.org/10.1175/2008JAS2646.1, 2009a.
Li, X., Tao, W.-K., Khain, A. P., Simpson, J., and Johnson, D. E.:
Sensitivity of a cloud-resolving model to bulk and explicit bin
microphysical schemes. Part II: Cloud microphysics and storm dynamics
interactions, J. Atmos. Sci., 66, 22–40. 2009b.
Lin, Z., Liu, X., and Collu, M.: Wind power prediction based on
high-frequency SCADA data along with isolation forest and deep learning
neural networks, Int. J. Elec. Power, 118, 105835, https://doi.org/10.1016/j.ijepes.2020.105835, 2020.
Liu, B., Liu, H., Xie, L., Guan, C., and Zhao, D.: A Coupled
atmosphere-wave-ocean modeling system: simulation of the intensity of an
idealized tropical cyclone, Mon. Weather Rev., 139, 132–152,
https://doi.org/10.1175/2010MWR3396.1, 2011.
Loftus, A. M. and Cotton, W. R.: Examination of CCN impacts on hail in a
simulated supercell storm with triple-moment hail bulk microphysics, Atmos.
Res., 147–148, 183–204, 2014.
Lukassen, L. J., Stevens, R. J. A. M., Meneveau, C., and Wilczek, M.:
Modeling space-time correlations of velocity fluctuations in wind farms,
Wind Energy, 21, 474–487, https://doi.org/10.1002/we.2172, 2018.
Lundquist, J. K., DuVivier, K. K., Kaffine, D., and Tomaszewski, J. M.:
Costs and consequences of wind turbine wake effects arising from
uncoordinated wind energy development, Nature Energy, 4, 26–34, https://doi.org/10.1038/s41560-018-0281-2, 2019.
Luo, T., Yuan, R., and Wang, Z.: Lidar-based remote sensing of atmospheric boundary layer height over land and ocean, Atmos. Meas. Tech., 7, 173–182, https://doi.org/10.5194/amt-7-173-2014, 2014.
Magnusson, M. and Smedman, A.-S.: Influence of Atmospheric Stability on
Wind Turbine Wakes, Wind Eng., 18, 139–152, 1994.
Mahrt, L.: Stratified atmospheric boundary layers, Bound.-Layer Meteorol., 90,
375–396, 1999.
Mahrt, L., Vickers, D., Edson, J., Wilczak, J. M., Hare, J., and Hojstrup,
J.: Vertical Structure of Turbulence In Offshore Flow During Rasex,
Bound.-Lay. Meteorol., 100, 47–61, 2001.
Mahrt, L., Vickers, D., and Andreas, E. L.: Low-level wind maxima and
structure of the stably stratified boundary layer in the coastal zone, J.
Appl. Meteorol. Climat., 53, 363–376, 2014.
McCandless, T. C., Gagne, D. J., Kosovic, B., Haupt, S. E., Yang, B.,
Becker, C., and Schreck, J.: Machine Learning for Improving Surface Layer
Flux Estimates, Bound.-Lay. Meteorol., 185, 199–228, https://doi.org/10.1007/s10546-022-00727-4, 2022.
Milbrandt, J. A. and Yau, M. K.: A multimoment bulk microphysics
parameterization. Part III: Control simulation of a hailstorm, J. Atmos. Sci., 63, 3114–3136, 2006.
Miller, S. C., Friehe, C., Hristov, T., Edson, J. and Wetzel, S.: Wind and turbulent profiles in the surface layer over ocean waves, Wind-Over-Wave Couplings: Perspectives and Prospects, Institute of Mathematics and its Applications Conference Series, edited by: Sajjadi, S. G., Thomas, N. H., and Hunt, J. C. R., Clarendon Press, 91–98, ISBN: 9780198501923, 1999.
Mishnaevsky Jr., L.: Repair of wind turbine blades: Review of methods and
related computational mechanics problems, Renew. Energ., 140,
828–839, 2019.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Contrib. Geophys. Inst. Acad. Sci. USSR, 151, 163–187, 1954.
Mora, E. B., Spelling, J., van der Weijde, A. H., and Pavageau, E.-M.: The
effects of mean wind speed uncertainty on project finance debt sizing for
offshore wind farms, Appl. Energ., 252, 113419,
https://doi.org/10.1016/j.apenergy.2019.113419, 2019.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of cloud microphysics
on the development of trailing stratiform precipitation in a simulated
squall line: Comparison of one- and two-moment schemes, Mon. Weather Rev., 137, 991–1007, 2009.
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Millbrandt, J.
A., Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I.,
van Diedenhoven, B., and Xue, L.: Confronting the challenge of modeling cloud
and precipitation microphysics, JAMES, 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020.
Munters, W. and Meyers, J.: Dynamic Strategies for Yaw and Induction Control
of Wind Farms Based on Large-Eddy Simulation and Optimization, Energies, 11,
177, https://doi.org/10.3390/en11010177, 2018.
Murphy, P., Lundquist, J. K., and Fleming, P.: How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine, Wind Energy Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, 2020.
Musial, W. D., Beiter, P. C., Spitsen, P., Nunemaker, J., and Gevorgian, V.: 2018 Offshore Wind Technologies Market Report, US Department of Energy Office of Energy Efficiency & Renewable Energy, Washington, DC , Tech. Rep. DOE/GO-102019-5192, https://doi.org/10.2172/1572771, 2019.
Norin, L.: A quantitative analysis of the impact of wind turbines on operational Doppler weather radar data, Atmos. Meas. Tech., 8, 593–609, https://doi.org/10.5194/amt-8-593-2015, 2015.
Nygaard, N. G.: Wakes in very large wind farms and the effect of
neighbouring wind farms, J. Phys. Conf. Ser., 524, 012162, https://doi.org/10.1088/1742-6596/524/1/012162, 2014.
O'Neill, L., Chelton, D. B., and Esbensen, S. K.: Covariability of surface
wind and stress responses to sea surface temperature fronts, J. Climate,
25, 5916–5942, https://doi.org/10.1175/JCLI-D-11-00230.1, 2012.
Obukhov, A. M.: Turbulence in an atmosphere with non-uniform temperature,
Tr. Inst. Teor. Geofiz. Akad. Nauk. SSSR, 1, 95–115, 1946.
Olson, J. B., Kenyon, J. S., Djalalova, I., Bianco, L., Turner, D. D.,
Pichugina, Y., Choukulkar, A., Toy, M. D., Brown, J. M., Angevine, W. M.,
and Akish, E.: Improving wind energy forecasting through numerical weather
prediction model development, B. Am. Meteorol. Soc., 100, 2201–2220,
https://doi.org/10.1175/BAMS-D-18-0040.1, 2019.
Oost, W. A., Komen, G. J., Jacobs, C. M. J., and Van Oort, C.: New evidence
for a relation between wind stress and wave age from measurements during
asgamage, Bound.-Lay. Meteorol., 103, 409–438, 2002.
Palm, S. P., Selmer, P., Yorks, J., Nicholls, S., and Nowottnick, E.:
Planetary boundary layer height estimates from ICESat-2 and CATS backscatter
measurements, Front. Remote Sens., 13, 716951, https://doi.org/10.3389/frsen.2021.716951, 2021.
Patton, E. G., Sullivan, P. P., Kosović, B., Dudhia, J., Mahrt, L.,
Žagar, M., and Marić, T.: On the influence of swell propagation
angle on surface drag, J. Appl. Meteorol. Climatol., 58, 1039–1059,
https://doi.org/10.1175/JAMC-D-18-0211.1, 2019.
Peña, A., Gryning, S. E. and Hasager, C. B.: Measurements and modelling of
the wind speed profile in the marine atmospheric boundary layer,
Bound.-Lay. Meteorol., 129, 479–495, 2008.
Peña, A., Gryning, S. E. and Hasager, C. B.: Comparing mixing-length
models of the diabatic wind profile over homogeneous terrain. Theor. Appl. Climatol., 100, 325–335, 2010.
Piazza, M., Terray, L., Boé, J., Maisonnave, E., and Sanchez-Gomez, E.:
Influence of small-scale North Atlantic sea surface temperature patterns on
the marine boundary layer and free troposphere: A study using the
atmospheric ARPEGE model, Clim, Dynam., 46, 1699–1717, 2016.
Pichugina, Y. L., Banta, R. M., Brewer, W. A., Sandberg, S. P., and
Hardesty, R. M.: Doppler Lidar–Based Wind-Profile Measurement System
for Offshore Wind-Energy and Other Marine Boundary Layer Applications,
J. Appl. Meteorol. Clim., 51, 327–349, https://doi.org/10.1175/JAMC-D-11-040.1, 2012.
Pichugina, Y. L., Brewer, W. A., Banta, R. M., Choukulkar, A., Clack, C. T.
M., Marquis, M. C., McCarty, B. J., Weickmann, A. M., Sandberg, S. P.,
Marchbanks, R. D., and Hardesty, R. M.: Properties of the offshore low level jet
and rotor layer wind shear as measured by scanning Doppler lidar, Wind
Energy, 20, 987–1002, https://doi.org/10.1002/we.2075, 2017.
Platis, A., Siedersleben, S. K., Bange, J., Lampert, A., Bärfuss, K.,
Hankers, R., Cañadillas, B., Foreman, R., Schulz-Stellenfleth, J.,
Djath, B., Neumann, T., and Emeis, S.: First in situ evidence of wakes in
the far field behind offshore wind farms, Sci. Rep., 8, 2163, https://doi.org/10.1038/s41598-018-20389-y,
2018.
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-Turbine and
Wind-Farm Flows: A Review, Bound.-Lay. Meteorol., 174, 1–59,
https://doi.org/10.1007/s10546-019-00473-0, 2020.
Pringle, W. J. and Kotamarthi, V. R.: Coupled Ocean Wave-Atmosphere Models
for Offshore Wind Energy, Tech. Report #ANL/EVS-21/8, Argonne National
Laboratory, Lemont, IL, https://doi.org/10.2172/1829093, 2021.
Pryor, S. C., Shepherd, T. J., Volker, P. J. H., Hahmann, A. N., and
Barthelmie, R. J.: “Wind Theft” from Onshore Wind Turbine Arrays:
Sensitivity to Wind Farm Parameterization and Resolution, J. Appl. Meteor.
Climatol., 59, 153–174, https://doi.org/10.1175/JAMC-D-19-0235.1, 2020.
Ramirez, L., Fraile, D., and Brindley, G.: Offshore wind in Europe: Key
trends and statistics 2019, https://windeurope.org/wp-content/uploads/files/about-wind/statistics/WindEurope-Annual-Offshore-Statistics-2019.pdf, last access: 5 November 2020.
Richter, D. H. and Sullivan, P. P.: Sea surface drag and the role of spray,
Geophys. Res. Lett., 40, 656–660, https://doi.org/10.1002/grl.50163, 2013.
Rios Gaona, M. F., Overeem, A., Leijnse, H., and Uijlenhoet, R.: First-Year
Evaluation of GPM Rainfall over the Netherlands: IMERG Day 1 Final Run
(V03D), J. Hydrometeorol., 17, 11, https://doi.org/10.1175/JHM-D-16-0087.1,
2016.
Rutgersson A., Smedman, A.-S., and Högström, U.: Use of conventional stability parameters
during swell, J. Geophys. Res., 106, 117–27, 134, https://doi.org/10.1029/2000JC000543, 2001.
Sanderse, B., van der Pijl, S. P., and Koren, B.: Review of computational
fluid dynamics for wind turbine wake aerodynamics, Wind Energy, 14,
799–819, https://doi.org/10.1002/we.458, 2011.
Sathe, A., Gryning, S. E., and Peña, A.: Comparison of the atmospheric
stability and wind profiles at two wind farm sites over a long marine fetch
in the North Sea, Wind Energy, 14, 767–780, 2011.
Sauer, J. and Muñoz-Esparza, D.: The FastEddy®
Resident-GPU Accelerated Large-Eddy Simulation Framework: Model Formulation,
Dynamical-Core Validation and Performance Benchmarks, J. Adv. Model. Earth Sy., 12, e2020MS002100, https://doi.org/10.1029/2020MS002100, 2020.
Savelyev, S. and Taylor, P. A.: Internal boundary-layers I – modified height
formulae in neutral and diabatic conditions, Bound.-Lay. Meteorol.,
115, 1–25, https://doi.org/10.1007/s10546-004-2122-z, 2005.
Schaller, E.: Time and height variability of the sensible heat flux in the
surface layer, Bound.-Lay. Meteorol., 11, 329–354, 1977.
Scher, S.: Toward data-driven weather and climate forecasting: Approximating
a simple general circulation model with deep learning, Geophys. Res. Lett.,
45, 12616–12622, 2018.
Schneemann, J., Theuer, F., Rott, A., Dörenkämper, M., and Kühn, M.: Offshore wind farm global blockage measured with scanning lidar, Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, 2021.
Sebastian, T. and Lackner, M. A.: Development of a free vortex wake method
code for offshore floating wind turbines, Renew. Energ., 46, 269–275,
https://doi.org/10.1016/j.renene.2012.03.033, 2012.
Seifert, J. K., Kraft, M., Kühn, M., and Lukassen, L. J.: Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data, Wind Energ. Sci., 6, 997–1014, https://doi.org/10.5194/wes-6-997-2021, 2021.
Semedo, A., Saetra, Ø., Rutgersson, A., Kahma, K. K., and Pettersson, H.:
Wave-induced wind in the marine boundary layer, J. Atmos. Sci., 66, 2256–2271, 2009.
Semedo, A., Sušelj, K., Rutgersson, A., and Sterl, A.: A global view on
the wind sea and swell climate and variability from ERA-40, J. Climate, 24, 1461–1479, 2011.
Seroka, G., Fredj, E., Kohut, J., Dunk, R., Miles, T., and Glenn, S.: Sea
breeze sensitivity to coastal upwelling and synoptic flow using Lagrangian
methods, J. Geophys. Res.-Atmos., 123,
9443–9461, 2018.
Shaw, W. J., Berg, L. K., Cline, J., Draxl, C., Djalalova, I., Grimit, E.
P., Lundquist, J. K., Marquis, M., McCaa, J., Olson, J. B., Sivaraman, C.,
Sharp, J., and Wilczak, J. M.: The Second Wind Forecast Improvement Project
(WFIP2): general overview, B. Am. Meteorol. Soc., 100, 1687–1699, https://doi.org/10.1175/BAMS-D-18-0036.1, 2019.
Shaw, W. J., Draher, J., Garcia Medina, G., Gorton, A. M., Krishnamurthy,
R., Newsom, R. K., Pekour, M. S., Sheridan, L. M., and Yang, Z.: General
Analysis of Data Collected from DOE Lidar Buoy Deployments Off Virginia and
New Jersey, PNNL-29823, Pacific Northwest National Laboratory, Richland, WA,
https://doi.org/10.2172/1632348, 2020.
Shimada, S., Ohsawa, T., Kogaki, T., Steinfeld, G., and Heinemann, D.:
Effects of sea surface temperature accuracy on offshore wind resource
assessment using a mesoscale model, Wind Energy, 18, 1839–1854, 2015.
Shutt, M. and Seim, H.: Assessment of Stability-Based Characterizations of
North Carolina's Offshore Wind Resource Using a Nested Boundary Layer
Method, J. Energy Power Tech., 2, 006, https://doi.org/10.21926/jept.2002006, 2020.
Siedersleben, S. K., Lundquist, J. K., Platis, A., Lampert, A., Bärfuss,
K., Cañadillas, B., Djath, B., Schulz-Stellenfleth, J., Neumann, T.,
Bange, J., and Emeis, S.: Micrometeorological impacts of offshore wind farms as
seen in observations and simulations, Environ. Res. Lett., 13, 124012, https://doi.org/10.1088/1748-9326/aaea0b, 2018.
Siedersleben, S. K., Platis, A., Lundquist, J. K., Djath, B., Lampert, A., Bärfuss, K., Cañadillas, B., Schulz-Stellenfleth, J., Bange, J., Neumann, T., and Emeis, S.: Turbulent kinetic energy over large offshore wind farms observed and simulated by the mesoscale model WRF (3.8.1), Geosci. Model Dev., 13, 249–268, https://doi.org/10.5194/gmd-13-249-2020, 2020.
Sjöblom, A. and Smedman, A. S.: Vertical structure in the marine
atmospheric boundary layer and its implication for the inertial dissipation
method, Bound.-Lay. Meteorol., 109, 1–25, 2003.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M. G., Huang, X.-Y.,Wang, W., and Powers, J. G.: A description of the advanced research WRF version 3, National Center for Atmospheric Research, Boulder, CO, Report No. NCAR/TN-4751STR, https://doi.org/10.5065/D68S4MVH, 2008.
Skrzypiński, W. R., Bech, J. I., Hasager, C. B., Tilg, A.-M., and
Bak, F. V.: Optimization of the erosion-safe operation of the IEA Wind 15 MW
Reference Wind Turbine, J. Phys. Conf. Ser., 1618, 052034,
https://doi.org/10.1088/1742-6596/1618/5/052034, 2020.
Skyllingstad, E. D., Vickers, D., Mahrt, L., and Samelson, R.: Effects of
mesoscale sea-surface temperature fronts on the marine atmospheric boundary
layer, Bound.-Lay. Meteorol., 123, 219–237, 2007.
Slot, H. M., Gelinck, E. R. M., Rentrop, C., and van der Heide, E.: Leading edge
erosion of coated wind turbine blades: review of coating life models, Renew.
Energy, 80, 387–848, https://doi.org/10.1016/j.renene.2015.02.036, 2015.
Small, R. D., deSzoeke, S. P., Xie, S. P., O'Neill, L., Seo, H., Song, Q.,
Cornillon, P., Spall, M., and Minobe, S.: Air–sea interaction over ocean
fronts and eddies, Dyn. Atmos. Oceans, 45,
274–319, https://doi.org/10.1016/j.dynatmoce.2008.01.001, 2008.
Smedman, A., Högström, U., Sahlee, E., Drennan, W. M., Kahma, K. K.,
Pettersson, H., and Zhang, F.: Observational study of marine atmospheric
boundary layer characteristics during swell, J. Atmos. Sci., 66, 2747–2763,
https://doi.org/10.1175/2009JAS2952.1, 2009.
Smith, S. D., Anderson, R. J., Oost, W. A., Kraan, C., Maat, N., De Cosmo,
J., Katsaros, K. B., Davidson, K. L., Bumke, K., Hasse, L., and Chadwick, H.
M.: Sea surface wind stress and drag coefficients: The hexos results,
Bound.-Lay. Meteorol., 60, 109–142, 1992.
Spall, M. A.: Midlatitude wind stress–sea surface temperature coupling in
the vicinity of oceanic fronts, J. Climate, 20, 3785–3801,
2007.
Sprague, M., Ananthan, S., Vijayakumar, G., and Robinson, M.: ExaWind: A
multifidelity modeling and simulation environment for wind energy, J. Phys.,
1452, 012071, https://doi.org/10.1088/1742-6596/1452/1/012071, 2020.
Steinbuch, M., de Boer, W. W., Bosgra, O. H., Peters, S. A. W. M., and
Ploeg, J.: Optimal control of wind power plants, J. Wind Eng. Ind. Aerod., 27, 237–246,
https://doi.org/10.1016/0167-6105(88)90039-6, 1988.
Stevens, R., Taylor V., Nichols, J., Maccabe, A. B., Yelick, K., and Brown,
D.: Report on the Department of Energy (DOE) Town Halls on Artificial
Intelligence (AI) for Science, US DOE Office of Science, https://www.anl.gov/ai-for-science-report (last access: 22 November 2022), 2020.
Strobach, E., Sparling, L. C., Rabenhorst, S. D., and Demoz, B.: Impact of
Inland Terrain on Mid-Atlantic Offshore Wind and Implications for Wind
Resource Assessment: A Case Study, J. Appl. Meteorol. Clim., 57, 777–796, https://doi.org/10.1175/JAMC-D-17-0143.1, 2018.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Atmospheric Sciences Library, 1st edn., Kluwer Academic Publishers, Dordrecht, The Netherlands, ISBN: 9027727686, 1988.
Sullivan, P. P., Edson, J. B., Hristov, T., and McWilliams, J. C.:
Large-eddy simulations and observations of atmospheric ma rine boundary
layers above nonequilibrium surface waves, J. Atmos. Sci., 65, 1225–1245, 2008.
Sullivan, P. P. and McWilliams, J. C.: Dynamics of Winds and Currents
Coupled to Surface Waves, Annu. Rev. Fluid Mech., 42, 19–42,
https://doi.org/10.1146/annurev-fluid-121108-145541, 2010.
Sullivan, P. P., McWilliams, J. C., and Patton, E. G.: Large-eddy simulation
of marine atmospheric boundary layers above a spectrum of moving waves, J.
Atmos. Sci., 71, 4001–4027, https://doi.org/10.1175/JAS-D-14-0095.1, 2014.
Sullivan, P. P., Banner, M. L., Morison, R. P., and Peirson, W. L.: Impacts
of wave age on turbulent flow and drag of steep waves, Procedia IUTAM, 26,
174–183, 2018a.
Sullivan, P. P., Banner, M. L., Morison, R. P., and Peirson, W. L.:
Turbulent flow over steep steady and unsteady waves under strong wind
forcing, J. Phys. Oceanogr., 48, 3–27,
https://doi.org/10.1175/JPO-D-17-0118.1, 2018b.
SWAN Team: SWAN: Scientific and technical documentation (SWAN Cycle III
version 41.31A), Delft University of Technology, Delft, The Netherlands,
http://swanmodel.sourceforge.net/download/zip/swantech.pdf (last access: 22 November 2022),
2020.
Tang, Q., Mu, L., Sidorenko, D., Goessling, H., Semmle, T., and Nerger, L.:
Improving the ocean and atmosphere in a coupled ocean-atmosphere model by
assimilating satellite sea-surface temperature and subsurface profile data,
Q. J. Roy. Meteor. Soc., 146, 4014–4029, https://doi.org/10.1002/qj.3885, 2020.
Taylor, P. K. and Yelland, M. J.: The Dependence of Sea Surface Roughness
on the Height and Steepness of the Waves, J. Phys. Oceanogr.,
31, 572–590, 2001.
Tilg, A.-M., Hasager, C. B., Kirtzel, H.-J., and Hummelshøj, P.: Brief communication: Nowcasting of precipitation for leading-edge-erosion-safe mode, Wind Energ. Sci., 5, 977–981, https://doi.org/10.5194/wes-5-977-2020, 2020a.
Tilg, A.-M., Vejen, F., Hasager, C. B., and Nielsen, M.: Rainfall Kinetic
Energy in Denmark: Relationship with Drop Size, Wind Speed, and Rain Rate,
J. Hydrometeorol., 21, 1621–1637, https://doi.org/10.1175/JHM-D-19-0251.1, 2020b.
Tobin, N., Zhu, H. and Chamorro, L. P.: Spectral behaviour of the
turbulence-driven power fluctuations of wind turbines, J.
Turbul., 16, 832–846, 2015.
Turner, D. D., Wulfmeyer, V., Berg, L. K., and Schween, J. H.: Water vapor
turbulence profiles in stationary continental convective mixed layers, J.
Geophys. Res., 119, 11151–11165, https://doi.org/10.1002/2014JD022202, 2014.
Varlas, G., Katsafados, P., Papadopoulos, A., and Korres, G.: Implementation
of a two-way coupled atmosphere-ocean wave modeling system for assessing
air-sea interaction over the Mediterranean Sea, Atmos. Res., 208,
201–217. https://doi.org/10.1016/j.atmosres.2017.08.019, 2018.
Veers, P., Dykes K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O., Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomāki, V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarity, P., Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J., Robertson, A., Rodrigo, J. S., Sempreviva, A. M., Smith, J. C., Tuohy, A., and Wiser, R.: Grand challenges in the science of wind energy, Science, 366, eaau2027, https://doi.org/10.1126/science.aau2027, 2019.
Velarde, J. and Bachynski, E. E.: Design and fatigue analysis of monopile
foundations to support the DTU 10 MW offshore wind turbine, Energy Proc.,
137, 3–13, https://doi.org/10.1016/j.egypro.2017.10.330, 2017.
Viselli, A., Filippelli, M., Pettigrew, N., Dagher, H., and Faessler, N.:
Validation of the first LiDAR wind resource assessment buoy system offshore
the Northeast United States, Wind Energy, 22, 1548–1562, https://doi.org/10.1002/we.2387, 2019.
Vorpahl, F., Schwarze, H., Fischer, T., Seidel, M., and Jonkman, J.:
Offshore wind turbine environment, loads, simulation, and design, WIREs
Energy Environ., 2, 548–570, https://doi.org/10.1002/wene.52, 2013.
Wagner, R., Courtney, M., Gottschall, J., and Lindelöw-Marsden, P.:
Accounting for speed shear in power performance measurement, Wind Energy,
14, 993–1004, https://doi.org/10.1002/we.509, 2011.
Wang, C., Campagnolo, F., Sharma, A., and Bottasso, C. L.: Effects of
dynamic induction control on power and loads, by LES-ALM simulations and
wind tunnel experiments, J. Phys. Conf. Ser., 1618, 022036, https://doi.org/10.1088/1742-6596/1618/2/022036, 2020.
Wang, J., Balaprakash, P., and Kotamarthi, R.: Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model, Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, 2019.
Wang, Q., Alappattu, D. P., Billingsley, S., Blomquist, B., Burkholder, R. J., Christman, A. J., Creegan, E. D., de Paolo, T., Eleuterio, D. P., Fernando, H. J. S., Franklin, K. B., Grachev, A. A., Haack, T., Hanley, T. R., Hocut, C. M., Holt, T. R., Horgan, K., Jonsson, H. H., Hale, R. A., Kalogiros, J. A., Khelif, D., Leo, L. S., Lind, R. J., Lozovatsky, I., Planella-Morato, J., Mukherjee, S., Nuss, W. A., Pozderac, J., Rogers, L. T., Savelyev, I., Savidge, D. K., Shearman, R. K., Shen, L., Terrill, E., Ulate, A. M., Wang, Q., Wendt, R. T., Wiss, R., Woods, R. K., Xu, L., Yamaguchi, R. T., and Yardim, C.: CASPER: Coupled Air–Sea Processes and Electromagnetic Ducting Research, B. Am. Meteorol. Soc., 99, 1449–1471, https://doi.org/10.1175/BAMS-D-16-0046.1, 2018.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a
Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modeling System,
Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010.
Wenegrat, J. O. and Arthur, R. S.: Response of the Atmospheric Boundary
Layer to Submesoscale Sea Surface Temperature Fronts, Geophys. Res. Lett., 45, 13505–13512, https://doi.org/10.1029/2018GL081034, 2018.
Wise, A. S. and Bachynski, E. E.: Wake meandering effects on floating wind
turbines, Wind Energy, 23, 1266–1285, https://doi.org/10.1002/we.2485, 2020.
Wood, R. L.: Stratocumulus Clouds, Mon. Weather Rev., 140, 2373–2423, https://doi.org/10.1175/MWR-D-11-00121.1, 2012.
Wood, R. and Bretherton, C. S.: Boundary layer depth, entrainment, and
decoupling in the cloud-capped subtropical and tropical marine boundary
layer, J. Climate, 17, 3576–3588, https://doi.org/10.1175/1520-0442(2004)017<3576:BLDEAD>2.0.CO;2, 2004.
Wilczak, J. M., Stoelinga, M., Berg, L. K., Sharp, J., Draxl, C., McCaffrey, K., Banta, R. M., Bianco, L., Djalalova, I., Lundquist, J. K., Muradyan, P., Choukulkar, A., Leo, L., Bonin, T., Pichugina, Y., Eckman, R., Long, C. N., Cline, J., Cook, D. R., Fernando, H. J. S., Friedrich, K., Krishnamurthy, R., Shaw, W. J., Wharton, S., and White, A. B.: The Second Wind Forecast
Improvement Project (WFIP2): Observational field campaign, B. Am. Meteorol. Soc., 100, 1701–1723, https://doi.org/10.1175/BAMS-D-18-0035.1, 2019.
Wu, K. L. and Porté-Agel, F.: Flow adjustment inside and around large finite-size wind farms, Energies, 10, 2164, https://doi.org/10.3390/en10122164, 2017.
Wu, L., Breivik, Ø., and Rutgersson, A.: Ocean-Wave-Atmosphere
Interaction Processes in a Fully Coupled Modeling Systemc, J. Adv. Model. Earth Sy., 11, 3852–3874, https://doi.org/10.1029/2019MS001761, 2019.
Wu, W.-C., Wang, T., Yang, Z., and García-Medina, G.: Development and
validation of a high-resolution regional wave hindcast model for US West
Coast wave resource characterization, Renew. Energ., 152, 736–753, 2020.
Wu, Y.-T. and Porté-Agel, F.: Atmospheric Turbulence Effects on
Wind-Turbine Wakes: An LES Study, Energies, 5, 5340–5362,
https://doi.org/10.3390/en5125340, 2012.
WW3DG: The WAVEWATCH III Development Group: User manual and system documentation of WAVEWATCH III
version 6.07, 333, NOAA/NWS/NCEP/MMAB, College Park, MD, USA,
https://www.researchgate.net/publication/336069899_User_manual_and_system_documentation_of_WAVEWATCH_III_R_version_607 (last access: 22 November 2022), 2019.
Yang, D., Meneveau, C., and Shen, L.: Dynamic modelling of sea-surface
roughness for large-eddy simulation of wind over ocean wavefield, J. Fluid Mech., 726, 62–99, 2013.
Yang, D., Meneveau, C., and Shen, L.: Effect of downwind swells on offshore
wind energy harvesting - A large-eddy simulation study, Renew. Energy, 70,
11–23, https://doi.org/10.1016/j.renene.2014.03.069, 2014.
Yang, Z., Deng, B.-Q., and Shen, L.: Direct numerical simulation of wind
turbulence over breaking waves, J. Fluid Mech., 850, 120–155,
https://doi.org/10.1017/jfm.2018.466, 2018.
Zeng, X., Brunke, M. A., Zhou, M., Fairall, C., Bond, N. A., and Lenschow,
D. H.: Marine atmospheric boundary layer height over the eastern Pacific:
data analysis and model evaluation, J. Climate, 17, 4159–4170, https://doi.org/10.1175/JCLI3190.1., 2004.
Zhang, J., Huang, L., Wen, Y., and Deng, J.: A distributed coupled
atmosphere-wave-ocean model for typhoon wave numerical simulation, Int. J.
Comput. Math., 86, 2095–2103, https://doi.org/10.1080/00207160802047632, 2009.
Zhang, S., Liu, Z., Zhang, X., Wu, X., Han, G., Zhao, Y., Yu, X., Liu, C.,
Liu, Y., Wu, S., Lu, F., Li, M., and Deng, X.: Coupled data assimilation and
parameter estimation in coupled ocean–atmosphere models: a review, Clim.
Dynam., 54, 5127–5144, https://doi.org/10.1007/s00382-020-05275-6, 2020.
Download
- Article
(3971 KB) - Full-text XML
Short summary
This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
This paper provides a review of prominent scientific challenges to characterizing the offshore...
Altmetrics
Final-revised paper
Preprint