Articles | Volume 8, issue 2
https://doi.org/10.5194/amt-8-907-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-8-907-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Feb 2015
Research article |
| 23 Feb 2015
Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics
J. K. Lundquist
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
National Renewable Energy Laboratory, Golden, Colorado, USA
M. J. Churchfield
National Renewable Energy Laboratory, Golden, Colorado, USA
S. Lee
National Renewable Energy Laboratory, Golden, Colorado, USA
A. Clifton
National Renewable Energy Laboratory, Golden, Colorado, USA
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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.
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Revised manuscript under review for WES
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The U.S. offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the U.S. mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are 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 within a wind plant contribute most to that reduction, while wakes between wind plants play a secondary role.
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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
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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.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
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Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Rachel Robey and Julie K. Lundquist
Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, https://doi.org/10.5194/amt-15-4585-2022, 2022
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Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.
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
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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
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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.
Hannah Livingston, Nicola Bodini, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-68, https://doi.org/10.5194/wes-2021-68, 2021
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In this paper, we assess whether hub-height turbulence can easily be quantified from either other hub-height variables or ground-level measurements in complex terrain. We find a large variability across the three considered locations when trying to model hub-height turbulence intensity and turbulence kinetic energy. Our results highlight the nonlinear and complex nature of atmospheric turbulence, so that more powerful techniques should instead be recommended to model hub-height turbulence.
Miguel Sanchez Gomez, Julie K. Lundquist, Petra M. Klein, and Tyler M. Bell
Earth Syst. Sci. Data, 13, 3539–3549, https://doi.org/10.5194/essd-13-3539-2021, https://doi.org/10.5194/essd-13-3539-2021, 2021
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In July 2018, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) hosted a flight week to demonstrate unmanned aircraft systems' capabilities in sampling the atmospheric boundary layer. Three Doppler lidars were deployed during this week-long experiment. We use data from these lidars to estimate turbulence dissipation rate. We observe large temporal variability and significant differences in dissipation for lidars with different sampling techniques.
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
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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.
Alex Rybchuk, Mike Optis, Julie K. Lundquist, Michael Rossol, and Walt Musial
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-50, https://doi.org/10.5194/gmd-2021-50, 2021
Preprint withdrawn
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We characterize the wind resource off the coast of California by conducting simulations with the Weather Research and Forecasting (WRF) model between 2000 and 2019. We compare newly simulated winds to those from the WIND Toolkit. The newly simulated winds are substantially stronger, particularly in the late summer. We also conduct a refined analysis at three areas that are being considered for commercial development, finding that stronger winds translates to substantially more power here.
Tyler M. Bell, Petra M. Klein, Julie K. Lundquist, and Sean Waugh
Earth Syst. Sci. Data, 13, 1041–1051, https://doi.org/10.5194/essd-13-1041-2021, https://doi.org/10.5194/essd-13-1041-2021, 2021
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In July 2018, numerous weather sensing remotely piloted aircraft systems (RPASs) were flown in a flight week called Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). As part of LAPSE-RATE, ground-based remote and in situ systems were also deployed to supplement and enhance observations from the RPASs. These instruments include multiple Doppler lidars, thermodynamic profilers, and radiosondes. This paper describes data from these systems.
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
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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.
Jessica M. Tomaszewski and Julie K. Lundquist
Wind Energ. Sci., 6, 1–13, https://doi.org/10.5194/wes-6-1-2021, https://doi.org/10.5194/wes-6-1-2021, 2021
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We use a mesoscale numerical weather prediction model to conduct a case study of a thunderstorm outflow passing over and interacting with a wind farm. These simulations and observations from a nearby radar and surface station confirm that interactions with the wind farm cause the outflow to reduce its speed by over 20 km h−1, with brief but significant impacts on the local meteorology, including temperature, moisture, and winds. Precipitation accumulation across the region was unaffected.
Gijs de Boer, Adam Houston, Jamey Jacob, Phillip B. Chilson, Suzanne W. Smith, Brian Argrow, Dale Lawrence, Jack Elston, David Brus, Osku Kemppinen, Petra Klein, Julie K. Lundquist, Sean Waugh, Sean C. C. Bailey, Amy Frazier, Michael P. Sama, Christopher Crick, David Schmale III, James Pinto, Elizabeth A. Pillar-Little, Victoria Natalie, and Anders Jensen
Earth Syst. Sci. Data, 12, 3357–3366, https://doi.org/10.5194/essd-12-3357-2020, https://doi.org/10.5194/essd-12-3357-2020, 2020
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This paper provides an overview of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field campaign, held from 14 to 20 July 2018. This field campaign spanned a 1-week deployment to Colorado's San Luis Valley, involving over 100 students, scientists, engineers, pilots, and outreach coordinators. This overview paper provides insight into the campaign for a special issue focused on the datasets collected during LAPSE-RATE.
Antonia Englberger, Julie K. Lundquist, and Andreas Dörnbrack
Wind Energ. Sci., 5, 1623–1644, https://doi.org/10.5194/wes-5-1623-2020, https://doi.org/10.5194/wes-5-1623-2020, 2020
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Wind turbines rotate clockwise. The rotational direction of the rotor interacts with the nighttime veering wind, resulting in a rotational-direction impact on the wake. In the case of counterclockwise-rotating blades the streamwise velocity in the wake is larger in the Northern Hemisphere whereas it is smaller in the Southern Hemisphere.
Antonia Englberger, Andreas Dörnbrack, and Julie K. Lundquist
Wind Energ. Sci., 5, 1359–1374, https://doi.org/10.5194/wes-5-1359-2020, https://doi.org/10.5194/wes-5-1359-2020, 2020
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At night, the wind direction often changes with height, and this veer affects structures near the surface like wind turbines. Wind turbines usually rotate clockwise, but this rotational direction interacts with veer to impact the flow field behind a wind turbine. If another turbine is located downwind, the direction of the upwind turbine's rotation will affect the downwind turbine.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
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While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Patrick Murphy, Julie K. Lundquist, and Paul Fleming
Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, https://doi.org/10.5194/wes-5-1169-2020, 2020
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We present and evaluate an improved method for predicting wind turbine power production based on measurements of the wind speed and direction profile across the rotor disk for a wind turbine in complex terrain. By comparing predictions to actual power production from a utility-scale wind turbine, we show this method is more accurate than methods based on hub-height wind speed or surface-based atmospheric characterization.
Paul Fleming, Jennifer King, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, David Jager, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, https://doi.org/10.5194/wes-5-945-2020, 2020
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This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in a companion paper (https://wes.copernicus.org/articles/4/273/2019/). The authors implemented wake steering on two turbine pairs and compared results with the latest FLORIS model of wake steering, showing good agreement in overall energy increase.
Jessica M. Tomaszewski and Julie K. Lundquist
Geosci. Model Dev., 13, 2645–2662, https://doi.org/10.5194/gmd-13-2645-2020, https://doi.org/10.5194/gmd-13-2645-2020, 2020
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Wind farms can briefly impact the nearby environment by reducing wind speeds and mixing warmer air down to the surface. The wind farm parameterization (WFP) in the Weather Research and Forecasting (WRF) model is a tool that numerically simulates wind farms and these meteorological impacts. We highlight the importance of choice in model settings and find that sufficiently fine vertical and horizontal grids with turbine turbulence are needed to accurately simulate wind farm meteorological impacts.
Philipp Gasch, Andreas Wieser, Julie K. Lundquist, and Norbert Kalthoff
Atmos. Meas. Tech., 13, 1609–1631, https://doi.org/10.5194/amt-13-1609-2020, https://doi.org/10.5194/amt-13-1609-2020, 2020
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We present an airborne Doppler lidar simulator (ADLS) based on high-resolution atmospheric wind fields (LES). The ADLS is used to evaluate the retrieval accuracy of airborne wind profiling under turbulent, inhomogeneous wind field conditions inside the boundary layer. With the ADLS, the error due to the violation of the wind field homogeneity assumption used for retrieval can be revealed. For the conditions considered, flow inhomogeneities exert a dominant influence on wind profiling error.
Simon K. Siedersleben, Andreas Platis, Julie K. Lundquist, Bughsin Djath, Astrid Lampert, Konrad Bärfuss, Beatriz Cañadillas, Johannes Schulz-Stellenfleth, Jens Bange, Tom Neumann, and Stefan Emeis
Geosci. Model Dev., 13, 249–268, https://doi.org/10.5194/gmd-13-249-2020, https://doi.org/10.5194/gmd-13-249-2020, 2020
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Wind farms affect local weather and microclimates. These effects can be simulated in weather models, usually by removing momentum at the location of the wind farm. Some debate exists whether additional turbulence should be added to capture the enhanced mixing of wind farms. By comparing simulations to measurements from airborne campaigns near offshore wind farms, we show that additional turbulence is necessary. Without added turbulence, the mixing is underestimated during stable conditions.
Miguel Sanchez Gomez and Julie K. Lundquist
Wind Energ. Sci., 5, 125–139, https://doi.org/10.5194/wes-5-125-2020, https://doi.org/10.5194/wes-5-125-2020, 2020
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Wind turbine performance depends on various atmospheric conditions. We quantified the effect of the change in wind direction and speed with height (direction and speed wind shear) on turbine power at a wind farm in Iowa. Turbine performance was affected during large direction shear and small speed shear conditions and favored for the opposite scenarios. These effects make direction shear significant when analyzing the influence of different atmospheric variables on turbine operation.
Norman Wildmann, Nicola Bodini, Julie K. Lundquist, Ludovic Bariteau, and Johannes Wagner
Atmos. Meas. Tech., 12, 6401–6423, https://doi.org/10.5194/amt-12-6401-2019, https://doi.org/10.5194/amt-12-6401-2019, 2019
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Turbulence is the variation of wind velocity on short timescales. In this study we introduce a new method to measure turbulence in a two-dimensionial plane with lidar instruments. The method allows for the detection and quantification of subareas of distinct turbulence conditions in the observed plane. We compare the results to point and profile measurements with more established instruments. It is shown that turbulence below low-level jets and in wind turbine wakes can be investigated this way.
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
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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.
Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, https://doi.org/10.5194/wes-4-273-2019, 2019
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Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
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
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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.
Robert Menke, Nikola Vasiljević, Jakob Mann, and Julie K. Lundquist
Atmos. Chem. Phys., 19, 2713–2723, https://doi.org/10.5194/acp-19-2713-2019, https://doi.org/10.5194/acp-19-2713-2019, 2019
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This research utilizes several months of lidar measurements from the Perdigão 2017 campaign to investigate flow recirculation zones that occur at the two parallel ridges at the measurement site in Portugal. We found that recirculation occurs in over 50 % of the time when the wind direction is perpendicular to the direction of the ridges. Moreover, we show three-dimensional changes of the zones along the ridges and the implications of recirculation on wind turbines that are operating downstream.
Joseph C. Y. Lee, M. Jason Fields, and Julie K. Lundquist
Wind Energ. Sci., 3, 845–868, https://doi.org/10.5194/wes-3-845-2018, https://doi.org/10.5194/wes-3-845-2018, 2018
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To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using 37 years of wind and energy data. We conclude that the robust coefficient of variation can effectively assess and correlate wind-speed and energy-production variabilities. We derive adequate results via monthly mean data, whereas uncertainty arises in interannual variability calculations. We find that reliable estimates of wind-speed variability require 10 ± 3 years of monthly mean wind data.
Jessica M. Tomaszewski, Julie K. Lundquist, Matthew J. Churchfield, and Patrick J. Moriarty
Wind Energ. Sci., 3, 833–843, https://doi.org/10.5194/wes-3-833-2018, https://doi.org/10.5194/wes-3-833-2018, 2018
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Wind energy development has increased rapidly in rural locations of the United States, areas that also serve general aviation airports. The spinning rotor of a wind turbine creates an area of increased turbulence, and we question if this turbulent air could pose rolling hazards for light aircraft flying behind turbines. We analyze high-resolution simulations of wind flowing past a turbine to quantify the rolling risk and find that wind turbines pose no significant roll hazards to light aircraft.
Nicola Bodini, Julie K. Lundquist, and Rob K. Newsom
Atmos. Meas. Tech., 11, 4291–4308, https://doi.org/10.5194/amt-11-4291-2018, https://doi.org/10.5194/amt-11-4291-2018, 2018
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Turbulence within the atmospheric boundary layer is critically important to transfer heat, momentum, and moisture. Currently, improved turbulence parametrizations are crucially needed to refine the accuracy of model results at fine horizontal scales. In this study, we calculate turbulence dissipation rate from sonic anemometers and discuss a novel approach to derive turbulence dissipation from profiling lidar measurements.
Rochelle P. Worsnop, Michael Scheuerer, Thomas M. Hamill, and Julie K. Lundquist
Wind Energ. Sci., 3, 371–393, https://doi.org/10.5194/wes-3-371-2018, https://doi.org/10.5194/wes-3-371-2018, 2018
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This paper uses four statistical methods to generate probabilistic wind speed and power ramp forecasts from the High Resolution Rapid Refresh model. The results show that these methods can provide necessary uncertainty information of power ramp forecasts. These probabilistic forecasts can aid in decisions regarding power production and grid integration of wind power.
Joseph C. Y. Lee and Julie K. Lundquist
Geosci. Model Dev., 10, 4229–4244, https://doi.org/10.5194/gmd-10-4229-2017, https://doi.org/10.5194/gmd-10-4229-2017, 2017
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We evaluate the wind farm parameterization (WFP) in the Weather Research and Forecasting (WRF) model, a powerful tool to simulate wind farms and their meteorological impacts numerically. In our case study, the WFP simulations with fine vertical grid resolution are skilful in matching the observed winds and the actual power productions. Moreover, the WFP tends to underestimate power in windy conditions. We also illustrate that the modeled wind speed is a critical determinant to improve the WFP.
Nicola Bodini, Dino Zardi, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 2881–2896, https://doi.org/10.5194/amt-10-2881-2017, https://doi.org/10.5194/amt-10-2881-2017, 2017
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Wind turbine wakes have considerable impacts on downwind turbines in wind farms, given their slower wind speeds and increased turbulence. Based on lidar measurements, we apply a quantitative algorithm to assess wake parameters for wakes from a row of four turbines in CWEX-13 campaign. We describe how wake characteristics evolve, and for the first time we quantify the relation between wind veer and a stretching of the wake structures, and we highlight different results for inner and outer wakes.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 2, 295–306, https://doi.org/10.5194/wes-2-295-2017, https://doi.org/10.5194/wes-2-295-2017, 2017
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We use upwind and nacelle-based measurements from a wind turbine and investigate the influence of atmospheric stability and turbulence regimes on nacelle transfer functions (NTFs) used to correct nacelle-mounted anemometer measurements. This work shows that correcting nacelle winds using NTFs results in similar energy production estimates to those obtained using upwind tower-based wind speeds. Further, stability and turbulence metrics have been found to have an effect on NTFs below rated speed.
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
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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
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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
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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.
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
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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
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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
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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.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 1, 221–236, https://doi.org/10.5194/wes-1-221-2016, https://doi.org/10.5194/wes-1-221-2016, 2016
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We use turbine nacelle-based measurements and measurements from an upwind tower to calculate wind turbine power curves and predict the production of energy. We explore how different atmospheric parameters impact these power curves and energy production estimates. Results show statistically significant differences between power curves and production estimates calculated with turbulence and stability filters, and we suggest implementing an additional step in analyzing power performance data.
Nicola Bodini, Julie K. Lundquist, Dino Zardi, and Mark Handschy
Wind Energ. Sci., 1, 115–128, https://doi.org/10.5194/wes-1-115-2016, https://doi.org/10.5194/wes-1-115-2016, 2016
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Year-to-year variability of wind speeds limits the certainty of wind-plant preconstruction energy estimates ("resource assessments"). Using 62-year records from 60 stations across Canada we show that resource highs and lows persist for decades, which makes estimates 2–3 times less certain than if annual levels were uncorrelated. Comparing chronological data records with randomly permuted versions of the same data reveals this in an unambiguous and easy-to-understand way.
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
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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
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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.
Andrew Clifton, Sarah Barber, Andrew Bray, Peter Enevoldsen, Jason Fields, Anna Maria Sempreviva, Lindy Williams, Julian Quick, Mike Purdue, Philip Totaro, and Yu Ding
Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, https://doi.org/10.5194/wes-8-947-2023, 2023
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Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-38, https://doi.org/10.5194/wes-2023-38, 2023
Revised manuscript under review for WES
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The U.S. offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the U.S. mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are 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 within a wind plant contribute most to that reduction, while wakes between wind plants play a secondary role.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
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We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
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This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
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
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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.
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
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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.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
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Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Rachel Robey and Julie K. Lundquist
Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, https://doi.org/10.5194/amt-15-4585-2022, 2022
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Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.
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
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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
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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.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
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This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Hannah Livingston, Nicola Bodini, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-68, https://doi.org/10.5194/wes-2021-68, 2021
Preprint withdrawn
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In this paper, we assess whether hub-height turbulence can easily be quantified from either other hub-height variables or ground-level measurements in complex terrain. We find a large variability across the three considered locations when trying to model hub-height turbulence intensity and turbulence kinetic energy. Our results highlight the nonlinear and complex nature of atmospheric turbulence, so that more powerful techniques should instead be recommended to model hub-height turbulence.
Miguel Sanchez Gomez, Julie K. Lundquist, Petra M. Klein, and Tyler M. Bell
Earth Syst. Sci. Data, 13, 3539–3549, https://doi.org/10.5194/essd-13-3539-2021, https://doi.org/10.5194/essd-13-3539-2021, 2021
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In July 2018, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) hosted a flight week to demonstrate unmanned aircraft systems' capabilities in sampling the atmospheric boundary layer. Three Doppler lidars were deployed during this week-long experiment. We use data from these lidars to estimate turbulence dissipation rate. We observe large temporal variability and significant differences in dissipation for lidars with different sampling techniques.
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
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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.
Alex Rybchuk, Mike Optis, Julie K. Lundquist, Michael Rossol, and Walt Musial
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-50, https://doi.org/10.5194/gmd-2021-50, 2021
Preprint withdrawn
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We characterize the wind resource off the coast of California by conducting simulations with the Weather Research and Forecasting (WRF) model between 2000 and 2019. We compare newly simulated winds to those from the WIND Toolkit. The newly simulated winds are substantially stronger, particularly in the late summer. We also conduct a refined analysis at three areas that are being considered for commercial development, finding that stronger winds translates to substantially more power here.
Tyler M. Bell, Petra M. Klein, Julie K. Lundquist, and Sean Waugh
Earth Syst. Sci. Data, 13, 1041–1051, https://doi.org/10.5194/essd-13-1041-2021, https://doi.org/10.5194/essd-13-1041-2021, 2021
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In July 2018, numerous weather sensing remotely piloted aircraft systems (RPASs) were flown in a flight week called Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). As part of LAPSE-RATE, ground-based remote and in situ systems were also deployed to supplement and enhance observations from the RPASs. These instruments include multiple Doppler lidars, thermodynamic profilers, and radiosondes. This paper describes data from these systems.
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
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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.
Jessica M. Tomaszewski and Julie K. Lundquist
Wind Energ. Sci., 6, 1–13, https://doi.org/10.5194/wes-6-1-2021, https://doi.org/10.5194/wes-6-1-2021, 2021
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We use a mesoscale numerical weather prediction model to conduct a case study of a thunderstorm outflow passing over and interacting with a wind farm. These simulations and observations from a nearby radar and surface station confirm that interactions with the wind farm cause the outflow to reduce its speed by over 20 km h−1, with brief but significant impacts on the local meteorology, including temperature, moisture, and winds. Precipitation accumulation across the region was unaffected.
Gijs de Boer, Adam Houston, Jamey Jacob, Phillip B. Chilson, Suzanne W. Smith, Brian Argrow, Dale Lawrence, Jack Elston, David Brus, Osku Kemppinen, Petra Klein, Julie K. Lundquist, Sean Waugh, Sean C. C. Bailey, Amy Frazier, Michael P. Sama, Christopher Crick, David Schmale III, James Pinto, Elizabeth A. Pillar-Little, Victoria Natalie, and Anders Jensen
Earth Syst. Sci. Data, 12, 3357–3366, https://doi.org/10.5194/essd-12-3357-2020, https://doi.org/10.5194/essd-12-3357-2020, 2020
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This paper provides an overview of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field campaign, held from 14 to 20 July 2018. This field campaign spanned a 1-week deployment to Colorado's San Luis Valley, involving over 100 students, scientists, engineers, pilots, and outreach coordinators. This overview paper provides insight into the campaign for a special issue focused on the datasets collected during LAPSE-RATE.
Antonia Englberger, Julie K. Lundquist, and Andreas Dörnbrack
Wind Energ. Sci., 5, 1623–1644, https://doi.org/10.5194/wes-5-1623-2020, https://doi.org/10.5194/wes-5-1623-2020, 2020
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Wind turbines rotate clockwise. The rotational direction of the rotor interacts with the nighttime veering wind, resulting in a rotational-direction impact on the wake. In the case of counterclockwise-rotating blades the streamwise velocity in the wake is larger in the Northern Hemisphere whereas it is smaller in the Southern Hemisphere.
Antonia Englberger, Andreas Dörnbrack, and Julie K. Lundquist
Wind Energ. Sci., 5, 1359–1374, https://doi.org/10.5194/wes-5-1359-2020, https://doi.org/10.5194/wes-5-1359-2020, 2020
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At night, the wind direction often changes with height, and this veer affects structures near the surface like wind turbines. Wind turbines usually rotate clockwise, but this rotational direction interacts with veer to impact the flow field behind a wind turbine. If another turbine is located downwind, the direction of the upwind turbine's rotation will affect the downwind turbine.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
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While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Patrick Murphy, Julie K. Lundquist, and Paul Fleming
Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, https://doi.org/10.5194/wes-5-1169-2020, 2020
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We present and evaluate an improved method for predicting wind turbine power production based on measurements of the wind speed and direction profile across the rotor disk for a wind turbine in complex terrain. By comparing predictions to actual power production from a utility-scale wind turbine, we show this method is more accurate than methods based on hub-height wind speed or surface-based atmospheric characterization.
Paul Fleming, Jennifer King, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, David Jager, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, https://doi.org/10.5194/wes-5-945-2020, 2020
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This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in a companion paper (https://wes.copernicus.org/articles/4/273/2019/). The authors implemented wake steering on two turbine pairs and compared results with the latest FLORIS model of wake steering, showing good agreement in overall energy increase.
Jessica M. Tomaszewski and Julie K. Lundquist
Geosci. Model Dev., 13, 2645–2662, https://doi.org/10.5194/gmd-13-2645-2020, https://doi.org/10.5194/gmd-13-2645-2020, 2020
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Wind farms can briefly impact the nearby environment by reducing wind speeds and mixing warmer air down to the surface. The wind farm parameterization (WFP) in the Weather Research and Forecasting (WRF) model is a tool that numerically simulates wind farms and these meteorological impacts. We highlight the importance of choice in model settings and find that sufficiently fine vertical and horizontal grids with turbine turbulence are needed to accurately simulate wind farm meteorological impacts.
Philipp Gasch, Andreas Wieser, Julie K. Lundquist, and Norbert Kalthoff
Atmos. Meas. Tech., 13, 1609–1631, https://doi.org/10.5194/amt-13-1609-2020, https://doi.org/10.5194/amt-13-1609-2020, 2020
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We present an airborne Doppler lidar simulator (ADLS) based on high-resolution atmospheric wind fields (LES). The ADLS is used to evaluate the retrieval accuracy of airborne wind profiling under turbulent, inhomogeneous wind field conditions inside the boundary layer. With the ADLS, the error due to the violation of the wind field homogeneity assumption used for retrieval can be revealed. For the conditions considered, flow inhomogeneities exert a dominant influence on wind profiling error.
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, https://doi.org/10.5194/wes-5-199-2020, 2020
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This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
Simon K. Siedersleben, Andreas Platis, Julie K. Lundquist, Bughsin Djath, Astrid Lampert, Konrad Bärfuss, Beatriz Cañadillas, Johannes Schulz-Stellenfleth, Jens Bange, Tom Neumann, and Stefan Emeis
Geosci. Model Dev., 13, 249–268, https://doi.org/10.5194/gmd-13-249-2020, https://doi.org/10.5194/gmd-13-249-2020, 2020
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Wind farms affect local weather and microclimates. These effects can be simulated in weather models, usually by removing momentum at the location of the wind farm. Some debate exists whether additional turbulence should be added to capture the enhanced mixing of wind farms. By comparing simulations to measurements from airborne campaigns near offshore wind farms, we show that additional turbulence is necessary. Without added turbulence, the mixing is underestimated during stable conditions.
Miguel Sanchez Gomez and Julie K. Lundquist
Wind Energ. Sci., 5, 125–139, https://doi.org/10.5194/wes-5-125-2020, https://doi.org/10.5194/wes-5-125-2020, 2020
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Wind turbine performance depends on various atmospheric conditions. We quantified the effect of the change in wind direction and speed with height (direction and speed wind shear) on turbine power at a wind farm in Iowa. Turbine performance was affected during large direction shear and small speed shear conditions and favored for the opposite scenarios. These effects make direction shear significant when analyzing the influence of different atmospheric variables on turbine operation.
Norman Wildmann, Nicola Bodini, Julie K. Lundquist, Ludovic Bariteau, and Johannes Wagner
Atmos. Meas. Tech., 12, 6401–6423, https://doi.org/10.5194/amt-12-6401-2019, https://doi.org/10.5194/amt-12-6401-2019, 2019
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Turbulence is the variation of wind velocity on short timescales. In this study we introduce a new method to measure turbulence in a two-dimensionial plane with lidar instruments. The method allows for the detection and quantification of subareas of distinct turbulence conditions in the observed plane. We compare the results to point and profile measurements with more established instruments. It is shown that turbulence below low-level jets and in wind turbine wakes can be investigated this way.
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
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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.
Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, https://doi.org/10.5194/wes-4-273-2019, 2019
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Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
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
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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.
Luis A. Martínez-Tossas, Jennifer Annoni, Paul A. Fleming, and Matthew J. Churchfield
Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, https://doi.org/10.5194/wes-4-127-2019, 2019
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A new control-oriented model is developed to compute the wake of a wind turbine under yaw. The model uses a simplified version of the Navier–Stokes equation with assumptions. Good agreement is found between the model-proposed and large eddy simulations of a wind turbine in yaw.
Robert Menke, Nikola Vasiljević, Jakob Mann, and Julie K. Lundquist
Atmos. Chem. Phys., 19, 2713–2723, https://doi.org/10.5194/acp-19-2713-2019, https://doi.org/10.5194/acp-19-2713-2019, 2019
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This research utilizes several months of lidar measurements from the Perdigão 2017 campaign to investigate flow recirculation zones that occur at the two parallel ridges at the measurement site in Portugal. We found that recirculation occurs in over 50 % of the time when the wind direction is perpendicular to the direction of the ridges. Moreover, we show three-dimensional changes of the zones along the ridges and the implications of recirculation on wind turbines that are operating downstream.
Joseph C. Y. Lee, M. Jason Fields, and Julie K. Lundquist
Wind Energ. Sci., 3, 845–868, https://doi.org/10.5194/wes-3-845-2018, https://doi.org/10.5194/wes-3-845-2018, 2018
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To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using 37 years of wind and energy data. We conclude that the robust coefficient of variation can effectively assess and correlate wind-speed and energy-production variabilities. We derive adequate results via monthly mean data, whereas uncertainty arises in interannual variability calculations. We find that reliable estimates of wind-speed variability require 10 ± 3 years of monthly mean wind data.
Jessica M. Tomaszewski, Julie K. Lundquist, Matthew J. Churchfield, and Patrick J. Moriarty
Wind Energ. Sci., 3, 833–843, https://doi.org/10.5194/wes-3-833-2018, https://doi.org/10.5194/wes-3-833-2018, 2018
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Wind energy development has increased rapidly in rural locations of the United States, areas that also serve general aviation airports. The spinning rotor of a wind turbine creates an area of increased turbulence, and we question if this turbulent air could pose rolling hazards for light aircraft flying behind turbines. We analyze high-resolution simulations of wind flowing past a turbine to quantify the rolling risk and find that wind turbines pose no significant roll hazards to light aircraft.
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
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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.
Nicola Bodini, Julie K. Lundquist, and Rob K. Newsom
Atmos. Meas. Tech., 11, 4291–4308, https://doi.org/10.5194/amt-11-4291-2018, https://doi.org/10.5194/amt-11-4291-2018, 2018
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Turbulence within the atmospheric boundary layer is critically important to transfer heat, momentum, and moisture. Currently, improved turbulence parametrizations are crucially needed to refine the accuracy of model results at fine horizontal scales. In this study, we calculate turbulence dissipation rate from sonic anemometers and discuss a novel approach to derive turbulence dissipation from profiling lidar measurements.
Rochelle P. Worsnop, Michael Scheuerer, Thomas M. Hamill, and Julie K. Lundquist
Wind Energ. Sci., 3, 371–393, https://doi.org/10.5194/wes-3-371-2018, https://doi.org/10.5194/wes-3-371-2018, 2018
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This paper uses four statistical methods to generate probabilistic wind speed and power ramp forecasts from the High Resolution Rapid Refresh model. The results show that these methods can provide necessary uncertainty information of power ramp forecasts. These probabilistic forecasts can aid in decisions regarding power production and grid integration of wind power.
Paul Fleming, Jennifer Annoni, Matthew Churchfield, Luis A. Martinez-Tossas, Kenny Gruchalla, Michael Lawson, and Patrick Moriarty
Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, https://doi.org/10.5194/wes-3-243-2018, 2018
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This paper investigates the role of flow structures in wind farm control through yaw misalignment. A pair of counter-rotating vortices is shown to be important in deforming the shape of the wake. Further, we demonstrate that the vortex structures created in wake steering can enable a greater change power generation than currently modeled in control-oriented models. We propose that wind farm controllers can be made more effective if designed to take advantage of these effects.
Joseph C. Y. Lee and Julie K. Lundquist
Geosci. Model Dev., 10, 4229–4244, https://doi.org/10.5194/gmd-10-4229-2017, https://doi.org/10.5194/gmd-10-4229-2017, 2017
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We evaluate the wind farm parameterization (WFP) in the Weather Research and Forecasting (WRF) model, a powerful tool to simulate wind farms and their meteorological impacts numerically. In our case study, the WFP simulations with fine vertical grid resolution are skilful in matching the observed winds and the actual power productions. Moreover, the WFP tends to underestimate power in windy conditions. We also illustrate that the modeled wind speed is a critical determinant to improve the WFP.
Nicola Bodini, Dino Zardi, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 2881–2896, https://doi.org/10.5194/amt-10-2881-2017, https://doi.org/10.5194/amt-10-2881-2017, 2017
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Wind turbine wakes have considerable impacts on downwind turbines in wind farms, given their slower wind speeds and increased turbulence. Based on lidar measurements, we apply a quantitative algorithm to assess wake parameters for wakes from a row of four turbines in CWEX-13 campaign. We describe how wake characteristics evolve, and for the first time we quantify the relation between wind veer and a stretching of the wake structures, and we highlight different results for inner and outer wakes.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 2, 295–306, https://doi.org/10.5194/wes-2-295-2017, https://doi.org/10.5194/wes-2-295-2017, 2017
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We use upwind and nacelle-based measurements from a wind turbine and investigate the influence of atmospheric stability and turbulence regimes on nacelle transfer functions (NTFs) used to correct nacelle-mounted anemometer measurements. This work shows that correcting nacelle winds using NTFs results in similar energy production estimates to those obtained using upwind tower-based wind speeds. Further, stability and turbulence metrics have been found to have an effect on NTFs below rated speed.
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
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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
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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
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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.
Jennifer F. Newman and Andrew Clifton
Wind Energ. Sci., 2, 77–95, https://doi.org/10.5194/wes-2-77-2017, https://doi.org/10.5194/wes-2-77-2017, 2017
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Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.
Javier Sanz Rodrigo, Matthew Churchfield, and Branko Kosovic
Wind Energ. Sci., 2, 35–54, https://doi.org/10.5194/wes-2-35-2017, https://doi.org/10.5194/wes-2-35-2017, 2017
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The series of GABLS model intercomparison benchmarks is revisited in the context of wind energy atmospheric boundary layer (ABL) models. GABLS 1 and 2 are used for verification purposes. Then GABLS 3 is used to develop a methodology for using realistic mesoscale forcing for microscale ABL models. The method also uses profile nudging to dynamically reduce the bias. Different data assimilation strategies are discussed based on typical instrumentation setups of wind energy campaigns.
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
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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
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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
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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.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 1, 221–236, https://doi.org/10.5194/wes-1-221-2016, https://doi.org/10.5194/wes-1-221-2016, 2016
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We use turbine nacelle-based measurements and measurements from an upwind tower to calculate wind turbine power curves and predict the production of energy. We explore how different atmospheric parameters impact these power curves and energy production estimates. Results show statistically significant differences between power curves and production estimates calculated with turbulence and stability filters, and we suggest implementing an additional step in analyzing power performance data.
Nicola Bodini, Julie K. Lundquist, Dino Zardi, and Mark Handschy
Wind Energ. Sci., 1, 115–128, https://doi.org/10.5194/wes-1-115-2016, https://doi.org/10.5194/wes-1-115-2016, 2016
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Year-to-year variability of wind speeds limits the certainty of wind-plant preconstruction energy estimates ("resource assessments"). Using 62-year records from 60 stations across Canada we show that resource highs and lows persist for decades, which makes estimates 2–3 times less certain than if annual levels were uncorrelated. Comparing chronological data records with randomly permuted versions of the same data reveals this in an unambiguous and easy-to-understand way.
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Extended validation of Aeolus winds with wind-profiling radars in Antarctica and Arctic Sweden
The impact of Aeolus winds on near-surface wind forecasts over tropical ocean and high-latitude regions
Long-term validation of Aeolus L2B wind products at Punta Arenas, Chile, and Leipzig, Germany
Daily satellite-based sunshine duration estimates over Brazil: Validation and inter-comparison
Turbulence kinetic energy dissipation rate: assessment of radar models from comparisons between 1.3 GHz wind profiler radar (WPR) and DataHawk UAV measurements
Statistical assessment of a Doppler radar model of TKE dissipation rate for low Richardson numbers (weakly stratified or strongly sheared conditions)
On the Use of Routine Airborne Observations for Evaluation and Monitoring of Satellite Observations of Thermodynamic Profiles
The impacts of assimilating Aeolus horizontal line-of-sight winds on numerical predictions of Hurricane Ida (2021) and a mesoscale convective system over the Atlantic Ocean
Evaluation of tropospheric water vapour and temperature profiles retrieved from MetOp-A by the Infrared and Microwave Sounding scheme
Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics
An improved vertical correction method for the inter-comparison and inter-validation of integrated water vapour measurements
An assessment of reprocessed GPS/MET observations spanning 1995–1997
Turbulence parameters measured by the Beijing mesosphere–stratosphere–troposphere radar in the troposphere and lower stratosphere with three models: comparison and analyses
Comparison of planetary boundary layer height from ceilometer with ARM radiosonde data
Behavior and mechanisms of Doppler wind lidar error in varying stability regimes
Inter-comparison of atmospheric boundary layer (ABL) height estimates from different profiling sensors and models in the framework of HyMeX-SOP1
Evaluation of Aeolus L2B wind product with wind profiling radar measurements and numerical weather prediction model equivalents over Australia
Comparison of global UV spectral irradiance measurements between a BTS CCD-array and a Brewer spectroradiometer
Scan strategies for wind profiling with Doppler lidar – an large-eddy simulation (LES)-based evaluation
Exploiting Aeolus level-2b winds to better characterize atmospheric motion vector bias and uncertainty
Modelling the spectral shape of continuous-wave lidar measurements in a turbulent wind tunnel
Three-way calibration checks using ground-based, ship-based, and spaceborne radars
Rainfall retrieval algorithm for commercial microwave links: stochastic calibration
Inter-comparison of wind measurements in the atmospheric boundary layer and the lower troposphere with Aeolus and a ground-based coherent Doppler lidar network over China
Towards operational multi-GNSS tropospheric products at GFZ Potsdam
Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan
Monitoring the Tropospheric Monitoring Instrument (TROPOMI) short-wave infrared (SWIR) module instrument stability using desert sites
Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome
Interpreting estimated observation error statistics of weather radar measurements using the ICON-LAM-KENDA system
Validation of Aeolus winds using ground-based radars in Antarctica and in northern Sweden
Intercomparison review of IPWV retrieved from INSAT-3DR sounder, GNSS and CAMS reanalysis data
Sensitivity of Aeolus HLOS winds to temperature and pressure specification in the L2B processor
Airborne lidar observations of wind, water vapor, and aerosol profiles during the NASA Aeolus calibration and validation (Cal/Val) test flight campaign
Improved method of estimating temperatures at meteor peak heights
Error analyses of a multistatic meteor radar system to obtain a three-dimensional spatial-resolution distribution
Validation of wind measurements of two mesosphere–stratosphere–troposphere radars in northern Sweden and in Antarctica
Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia
A 2-year intercomparison of continuous-wave focusing wind lidar and tall mast wind measurements at Cabauw
Using machine learning to model uncertainty for water vapor atmospheric motion vectors
Validation of pure rotational Raman temperature data from the Raman Lidar for Meteorological Observations (RALMO) at Payerne
Flywheel calibration of a continuous-wave coherent Doppler wind lidar
Validation of the TROPOspheric Monitoring Instrument (TROPOMI) surface UV radiation product
Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study
Validation of Aeolus wind products above the Atlantic Ocean
Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy
Inter-calibration of nine UV sensing instruments over Antarctica and Greenland since 1980
Inter-calibrating SMMR brightness temperatures over continental surfaces
Validating HY-2A CMR precipitable water vapor using ground-based and shipborne GNSS observations
Retrieval of lower-order moments of the drop size distribution using CSU-CHILL X-band polarimetric radar: a case study
Gradient boosting machine learning to improve satellite-derived column water vapor measurement error
Sheila Kirkwood, Evgenia Belova, Peter Voelger, Sourav Chatterjee, and Karathazhiyath Satheesan
Atmos. Meas. Tech., 16, 4215–4227, https://doi.org/10.5194/amt-16-4215-2023, https://doi.org/10.5194/amt-16-4215-2023, 2023
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We compared 2 years of wind measurements by the Aeolus satellite with winds from two wind-profiler radars in Arctic Sweden and coastal Antarctica. Biases are similar in magnitude to results from other locations. They are smaller than in earlier studies due to more comparison points and improved criteria for data rejection. On the other hand, the standard deviation is somewhat higher because of degradation of the Aeolus lidar.
Haichen Zuo and Charlotte Bay Hasager
Atmos. Meas. Tech., 16, 3901–3913, https://doi.org/10.5194/amt-16-3901-2023, https://doi.org/10.5194/amt-16-3901-2023, 2023
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Aeolus is a satellite equipped with a Doppler wind lidar to detect global wind profiles. This study evaluates the impact of Aeolus winds on surface wind forecasts over tropical oceans and high-latitude regions based on the ECMWF observing system experiments. We find that Aeolus can slightly improve surface wind forecasts for the region > 60° N, especially from day 5 onwards. For other study regions, the impact of Aeolus is nearly neutral or limited, which requires further investigation.
Holger Baars, Joshua Walchester, Elizaveta Basharova, Henriette Gebauer, Martin Radenz, Johannes Bühl, Boris Barja, Ulla Wandinger, and Patric Seifert
Atmos. Meas. Tech., 16, 3809–3834, https://doi.org/10.5194/amt-16-3809-2023, https://doi.org/10.5194/amt-16-3809-2023, 2023
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In 2018, the Aeolus satellite of the European Space Agency (ESA) was launched to improve weather forecasts through global measurements of wind profiles. Given the novel lidar technique onboard, extensive validation efforts have been needed to verify the observations. For this reason, we performed long-term validation measurements in Germany and Chile. We found significant improvement in the data products due to a new algorithm version and can confirm the general validity of Aeolus observations.
Maria Lívia Lins Mattos Gava, Simone Marilene Sievert da Costa Coelho, and Anthony Carlos Silva Porfírio
EGUsphere, https://doi.org/10.5194/egusphere-2023-1195, https://doi.org/10.5194/egusphere-2023-1195, 2023
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This study assesses the effectiveness of two geostationary satellite-based sunshine duration datasets over Brazil. Statistical parameters were used to evaluate the products' performance. The results showed generally good agreement between satellite and ground observations with some regional discrepancies. Overall, both satellite products offer reliable data for various applications, benefiting from their high spatial resolution and low time latency.
Hubert Luce, Lakshmi Kantha, Hiroyuki Hashiguchi, Dale Lawrence, Abhiram Doddi, Tyler Mixa, and Masanori Yabuki
Atmos. Meas. Tech., 16, 3561–3580, https://doi.org/10.5194/amt-16-3561-2023, https://doi.org/10.5194/amt-16-3561-2023, 2023
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Doppler radars can be used to estimate turbulence kinetic energy dissipation rates in the atmosphere. The performance of various models is evaluated from comparisons between UHF wind profiler and in situ measurements with UAVs. For the first time, we assess a model supposed to be valid for weak stratification or strong shear conditions. This model provides better agreements with in situ measurements than the classical model based on the hypothesis of a stable stratification.
Hubert Luce, Lakshmi Kantha, and Hiroyuki Hashiguchi
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-141, https://doi.org/10.5194/amt-2023-141, 2023
Revised manuscript accepted for AMT
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The potential ability of clear air radars to measure Turbulence Kinetic Energy (TKE) dissipation rate ε in the atmosphere is a major asset of these instruments, because of their continuous measurements. In the present work, we successfully tested the relevance of a model relating ε to the width of the Doppler spectrum peak and wind shear for shear-generated turbulence and we provide a physical interpretation of an empirical model in this context.
Timothy J. Wagner, Thomas August, Tim Hultberg, and Ralph A. Petersen
EGUsphere, https://doi.org/10.5194/egusphere-2023-794, https://doi.org/10.5194/egusphere-2023-794, 2023
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Commercial passenger and freight aircraft need to know the temperature and pressure of the environments they fly through in order to safely operate. In this paper, we investigate how these observations can be used to evaluate and monitor the performance of satellite observations. Normally weather balloons are used for this, but in places like the United States there are many more airplane flights than weather balloon launches. This makes it much easier to compare to satellites.
Chengfeng Feng and Zhaoxia Pu
Atmos. Meas. Tech., 16, 2691–2708, https://doi.org/10.5194/amt-16-2691-2023, https://doi.org/10.5194/amt-16-2691-2023, 2023
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This study demonstrates the positive impacts of assimilating Aeolus Mie-cloudy and Rayleigh-clear near-real-time horizontal line-of-sight winds on the analysis and forecasts of Hurricane Ida (2021) and a mesoscale convective system associated with an African easterly wave using the mesoscale community Weather Research and Forecasting model and the NCEP Gridpoint Statistical Interpolation-based three-dimensional ensemble-variational hybrid data assimilation system.
Tim Trent, Richard Siddans, Brian Kerridge, Marc Schröder, Noëlle A. Scott, and John Remedios
Atmos. Meas. Tech., 16, 1503–1526, https://doi.org/10.5194/amt-16-1503-2023, https://doi.org/10.5194/amt-16-1503-2023, 2023
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Modern weather satellites provide essential information on our lower atmosphere's moisture content and temperature structure. This measurement record will span over 40 years, making it a valuable resource for climate studies. This study characterizes atmospheric temperature and humidity profiles from a European Space Agency climate project. Using weather balloon measurements, we demonstrated the performance of this dataset was within the tolerances required for future climate studies.
Benjamin Witschas, Christian Lemmerz, Alexander Geiß, Oliver Lux, Uwe Marksteiner, Stephan Rahm, Oliver Reitebuch, Andreas Schäfler, and Fabian Weiler
Atmos. Meas. Tech., 15, 7049–7070, https://doi.org/10.5194/amt-15-7049-2022, https://doi.org/10.5194/amt-15-7049-2022, 2022
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In August 2018, the first wind lidar Aeolus was launched into space and has since then been providing data of the global wind field. The primary goal of Aeolus was the improvement of numerical weather prediction. To verify the quality of Aeolus wind data, DLR performed four airborne validation campaigns with two wind lidar systems. In this paper, we report on results from the two later campaigns, performed in Iceland and the tropics.
Olivier Bock, Pierre Bosser, and Carl Mears
Atmos. Meas. Tech., 15, 5643–5665, https://doi.org/10.5194/amt-15-5643-2022, https://doi.org/10.5194/amt-15-5643-2022, 2022
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Integrated water vapour measurements are often compared for the calibration and validation of instruments or techniques. Measurements made at different altitudes must be corrected to account for the vertical variation of water vapour. This paper shows that the widely used empirical correction model has severe limitations that are overcome using the proposed model. The method is applied to the inter-comparison of GPS and satellite microwave radiometer data in a tropical mountainous area.
Anthony J. Mannucci, Chi O. Ao, Byron A. Iijima, Thomas K. Meehan, Panagiotis Vergados, E. Robert Kursinski, and William S. Schreiner
Atmos. Meas. Tech., 15, 4971–4987, https://doi.org/10.5194/amt-15-4971-2022, https://doi.org/10.5194/amt-15-4971-2022, 2022
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The Global Positioning System (GPS) radio occultation (RO) technique is a satellite-based method for producing highly accurate vertical profiles of atmospheric temperature and pressure. RO profiles are used to monitor global climate trends, particularly in that region of the atmosphere that includes the lower stratosphere. Two data sets spanning 1995–1997 that were produced from the first RO satellite are highly accurate and can be used to assess global atmospheric models.
Ze Chen, Yufang Tian, Yinan Wang, Yongheng Bi, Xue Wu, Juan Huo, Linjun Pan, Yong Wang, and Daren Lü
Atmos. Meas. Tech., 15, 4785–4800, https://doi.org/10.5194/amt-15-4785-2022, https://doi.org/10.5194/amt-15-4785-2022, 2022
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Small-scale turbulence plays a vital role in the vertical exchange of heat, momentum and mass in the atmosphere. There are currently three models that can use spectrum width data of MST radar to calculate turbulence parameters. However, few studies have explored the applicability of the three calculation models. We compared and analysed the turbulence parameters calculated by three models. These results can provide a reference for the selection of models for calculating turbulence parameters.
Damao Zhang, Jennifer Comstock, and Victor Morris
Atmos. Meas. Tech., 15, 4735–4749, https://doi.org/10.5194/amt-15-4735-2022, https://doi.org/10.5194/amt-15-4735-2022, 2022
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The planetary boundary layer is the lowest part of the atmosphere. Its structure and depth (PBLHT) significantly impact air quality, global climate, land–atmosphere interactions, and a wide range of atmospheric processes. To test the robustness of the ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. DOE ARM measurements at various ARM observatories located around the world.
Rachel Robey and Julie K. Lundquist
Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, https://doi.org/10.5194/amt-15-4585-2022, 2022
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Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.
Donato Summa, Fabio Madonna, Noemi Franco, Benedetto De Rosa, and Paolo Di Girolamo
Atmos. Meas. Tech., 15, 4153–4170, https://doi.org/10.5194/amt-15-4153-2022, https://doi.org/10.5194/amt-15-4153-2022, 2022
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The evolution of the atmospheric boundary layer height (ABLH) has an important impact on meteorology. However, the complexity of the phenomena occurring within the ABL and the influence of advection and local accumulation processes often prevent an unambiguous determination of the ABLH. The paper reports results from an inter-comparison effort involving different sensors and techniques to measure the ABLH. Correlations between the ABLH and other atmospheric variables are also assessed.
Haichen Zuo, Charlotte Bay Hasager, Ioanna Karagali, Ad Stoffelen, Gert-Jan Marseille, and Jos de Kloe
Atmos. Meas. Tech., 15, 4107–4124, https://doi.org/10.5194/amt-15-4107-2022, https://doi.org/10.5194/amt-15-4107-2022, 2022
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The Aeolus satellite was launched in 2018 for global wind profile measurement. After successful operation, the error characteristics of Aeolus wind products have not yet been studied over Australia. To complement earlier validation studies, we evaluated the Aeolus Level-2B11 wind product over Australia with ground-based wind profiling radar measurements and numerical weather prediction model equivalents. The results show that the Aeolus can detect winds with sufficient accuracy over Australia.
Carmen González, José M. Vilaplana, José A. Bogeat, and Antonio Serrano
Atmos. Meas. Tech., 15, 4125–4133, https://doi.org/10.5194/amt-15-4125-2022, https://doi.org/10.5194/amt-15-4125-2022, 2022
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Monitoring ultraviolet (UV) radiation is important since it can have harmful effects on the biosphere. Array spectroradiometers are increasingly used to measure UV as they are more versatile than scanning spectroradiometers. In this study, the long-term performance of the BTS-2048-UV-S-WP array spectroradiometer was assessed. The results show that the BTS can reliably measure both the UV index and UV radiation in the 300–360 nm range. Moreover, the BTS was stable and showed no seasonal behavior.
Charlotte Rahlves, Frank Beyrich, and Siegfried Raasch
Atmos. Meas. Tech., 15, 2839–2856, https://doi.org/10.5194/amt-15-2839-2022, https://doi.org/10.5194/amt-15-2839-2022, 2022
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Lidars can measure the wind profile in the lower part of the atmosphere, provided that the wind field is horizontally uniform and does not change during the time of the measurement. These requirements are mostly not fulfilled in reality, and the lidar wind measurement will thus hold a certain error. We investigate different strategies for lidar wind profiling using a lidar simulator implemented in a numerical simulation of the wind field. Our findings can help to improve wind measurements.
Katherine E. Lukens, Kayo Ide, Kevin Garrett, Hui Liu, David Santek, Brett Hoover, and Ross N. Hoffman
Atmos. Meas. Tech., 15, 2719–2743, https://doi.org/10.5194/amt-15-2719-2022, https://doi.org/10.5194/amt-15-2719-2022, 2022
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Winds that are crucial to weather forecasting derived from two different techniques – tracking satellite images (AMVs) and direct measurement of molecular and aerosol motions by Doppler lidar (Aeolus satellite winds) – are compared. We find that AMVs and Aeolus winds are highly correlated. Aeolus Mie-cloudy winds have great potential value as a comparison standard for AMVs. Larger differences are found in the Southern Hemisphere related to higher wind speed and higher vertical variation in wind.
Marijn Floris van Dooren, Anantha Padmanabhan Kidambi Sekar, Lars Neuhaus, Torben Mikkelsen, Michael Hölling, and Martin Kühn
Atmos. Meas. Tech., 15, 1355–1372, https://doi.org/10.5194/amt-15-1355-2022, https://doi.org/10.5194/amt-15-1355-2022, 2022
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The remote sensing technique lidar is widely used for wind speed measurements for both industrial and academic applications. Lidars can measure wind statistics accurately but cannot fully capture turbulent fluctuations in the high-frequency range, since they are partly filtered out. This paper therefore investigates the turbulence spectrum measured by a continuous-wave lidar and analytically models the lidar's measured spectrum with a Lorentzian filter function and a white noise term.
Alain Protat, Valentin Louf, Joshua Soderholm, Jordan Brook, and William Ponsonby
Atmos. Meas. Tech., 15, 915–926, https://doi.org/10.5194/amt-15-915-2022, https://doi.org/10.5194/amt-15-915-2022, 2022
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This study uses collocated ship-based, ground-based, and spaceborne radar observations to validate the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks to the accuracy required for operational severe weather applications such as rainfall and hail nowcasting.
Wagner Wolff, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet
Atmos. Meas. Tech., 15, 485–502, https://doi.org/10.5194/amt-15-485-2022, https://doi.org/10.5194/amt-15-485-2022, 2022
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The existing infrastructure for cellular communication is promising for ground-based rainfall remote sensing. Rain-induced signal attenuation is used in dedicated algorithms for retrieving rainfall depth along commercial microwave links (CMLs) between cell phone towers. This processing is a source of many uncertainties about input data, algorithm structures, parameters, CML network, and local climate. Application of a stochastic optimization method leads to improved CML rainfall estimates.
Songhua Wu, Kangwen Sun, Guangyao Dai, Xiaoye Wang, Xiaoying Liu, Bingyi Liu, Xiaoquan Song, Oliver Reitebuch, Rongzhong Li, Jiaping Yin, and Xitao Wang
Atmos. Meas. Tech., 15, 131–148, https://doi.org/10.5194/amt-15-131-2022, https://doi.org/10.5194/amt-15-131-2022, 2022
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During the VAL-OUC campaign, we established a coherent Doppler lidar (CDL) network over China to verify the Level 2B (L2B) products from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations, the L2B products from Aeolus are compared with those from CDLs. To our knowledge, the VAL-OUC campaign is the most extensive so far between CDLs and Aeolus in the lower troposphere for different atmospheric scenes. The vertical velocity impact on the HLOS retrieval from Aeolus is evaluated.
Karina Wilgan, Galina Dick, Florian Zus, and Jens Wickert
Atmos. Meas. Tech., 15, 21–39, https://doi.org/10.5194/amt-15-21-2022, https://doi.org/10.5194/amt-15-21-2022, 2022
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The assimilation of GNSS data in weather models has a positive impact on the forecasts. The impact is still limited due to using only the GPS zenith direction parameters. We calculate and validate more advanced tropospheric products from three satellite systems: the US American GPS, Russian GLONASS and European Galileo. The quality of all the solutions is comparable; however, combining more GNSS systems enhances the observations' geometry and improves the quality of the weather forecasts.
Hironori Iwai, Makoto Aoki, Mitsuru Oshiro, and Shoken Ishii
Atmos. Meas. Tech., 14, 7255–7275, https://doi.org/10.5194/amt-14-7255-2021, https://doi.org/10.5194/amt-14-7255-2021, 2021
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The first space-based Doppler wind lidar on board the Aeolus satellite was launched on 22 August 2018 to obtain global horizontal wind profiles. In this study, wind profilers, ground-based coherent Doppler wind lidars, and GPS radiosondes were used to validate the quality of Aeolus Level 2B wind products over Japan during three different periods. The results show that Aeolus can measure the horizontal winds over Japan accurately.
Tim A. van Kempen, Filippo Oggionni, and Richard M. van Hees
Atmos. Meas. Tech., 14, 6711–6722, https://doi.org/10.5194/amt-14-6711-2021, https://doi.org/10.5194/amt-14-6711-2021, 2021
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Validation of the instrument stability of the TROPOMI-SWIR module is done by monitoring a group of very stable and remote locations in the Saharan and Arabian deserts. These results confirm the excellent stability and lack of degradation of the TROPOMI-SWIR module derived from the internal calibration sources. The method was done for the first time on a spectrometer in the short-wave infrared and ensures TROPOMI-SWIR can be used for atmospheric research for years to come.
Susanna Hagelin, Roohollah Azad, Magnus Lindskog, Harald Schyberg, and Heiner Körnich
Atmos. Meas. Tech., 14, 5925–5938, https://doi.org/10.5194/amt-14-5925-2021, https://doi.org/10.5194/amt-14-5925-2021, 2021
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In this paper we study the impact of using wind observations from the Aeolus satellite, which provides wind speed profiles globally, in our numerical weather prediction system using a regional model covering the Nordic countries. The wind speed profiles from Aeolus are assimilated by the model, and we see that they have an impact on both the model analysis and forecast, though given the relatively few observations available the impact is often small.
Yuefei Zeng, Tijana Janjic, Yuxuan Feng, Ulrich Blahak, Alberto de Lozar, Elisabeth Bauernschubert, Klaus Stephan, and Jinzhong Min
Atmos. Meas. Tech., 14, 5735–5756, https://doi.org/10.5194/amt-14-5735-2021, https://doi.org/10.5194/amt-14-5735-2021, 2021
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Observation errors (OEs) of radar measurements are correlated. The Desroziers method has been often used to estimate statistics of OE in data assimilation. However, the resulting statistics consist of contributions from different sources and are difficult to interpret. Here, we use an approach based on samples for truncation error to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with OE statistics estimated by the Desroziers method.
Evgenia Belova, Sheila Kirkwood, Peter Voelger, Sourav Chatterjee, Karathazhiyath Satheesan, Susanna Hagelin, Magnus Lindskog, and Heiner Körnich
Atmos. Meas. Tech., 14, 5415–5428, https://doi.org/10.5194/amt-14-5415-2021, https://doi.org/10.5194/amt-14-5415-2021, 2021
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Wind measurements from two radars (ESRAD in Arctic Sweden and MARA at the Indian Antarctic station Maitri) are compared with lidar winds from the ESA satellite Aeolus, for July–December 2019. The aim is to check if Aeolus data processing is adequate for the sunlit conditions of polar summer. Agreement is generally good with bias in Aeolus winds < 1 m/s in most circumstances. The exception is a large bias (7 m/s) when the satellite has crossed a sunlit Antarctic ice cap before passing MARA.
Ramashray Yadav, Ram Kumar Giri, and Virendra Singh
Atmos. Meas. Tech., 14, 4857–4877, https://doi.org/10.5194/amt-14-4857-2021, https://doi.org/10.5194/amt-14-4857-2021, 2021
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We performed an intercomparison of seasonal and annual studies of retrievals of integrated precipitable water vapor (IPWV) carried out by INSAT-3DR satellite-borne infrared radiometer sounding and CAMS reanalysis data with ground-based Indian GNSS data. The magnitude and sign of the bias of INSAT-3DR and CAMS with respect to GNSS IPWV differs from station to station and season to season. A statistical evaluation of the collocated data sets was done to improve day-to-day weather forecasting.
Matic Šavli, Vivien Pourret, Christophe Payan, and Jean-François Mahfouf
Atmos. Meas. Tech., 14, 4721–4736, https://doi.org/10.5194/amt-14-4721-2021, https://doi.org/10.5194/amt-14-4721-2021, 2021
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The ESA's Aeolus satellite wind retrieval is provided through a series of processors. It depends on the temperature and pressure specification, which, however, are not measured by the satellite. The numerical weather predicted values are used instead, but these are erroneous. This article studies the sensitivity of the wind retrieval by introducing errors in temperature and pressure. This has been found to be small for Aeolus but is expected to be more crucial for future missions.
Kristopher M. Bedka, Amin R. Nehrir, Michael Kavaya, Rory Barton-Grimley, Mark Beaubien, Brian Carroll, James Collins, John Cooney, G. David Emmitt, Steven Greco, Susan Kooi, Tsengdar Lee, Zhaoyan Liu, Sharon Rodier, and Gail Skofronick-Jackson
Atmos. Meas. Tech., 14, 4305–4334, https://doi.org/10.5194/amt-14-4305-2021, https://doi.org/10.5194/amt-14-4305-2021, 2021
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This paper demonstrates the Doppler Aerosol WiNd (DAWN) lidar and High Altitude Lidar Observatory (HALO) measurement capabilities across a range of atmospheric conditions, compares DAWN and HALO measurements with Aeolus satellite Doppler wind lidar to gain an initial perspective of Aeolus performance, and discusses how atmospheric dynamic processes can be resolved and better understood through simultaneous observations of wind, water vapour, and aerosol profile observations.
Emranul Sarkar, Alexander Kozlovsky, Thomas Ulich, Ilkka Virtanen, Mark Lester, and Bernd Kaifler
Atmos. Meas. Tech., 14, 4157–4169, https://doi.org/10.5194/amt-14-4157-2021, https://doi.org/10.5194/amt-14-4157-2021, 2021
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The biasing effect in meteor radar temperature has been a pressing issue for the last 2 decades. This paper has addressed the underlying reasons for such a biasing effect on both theoretical and experimental grounds. An improved statistical method has been developed which allows atmospheric temperatures at around 90 km to be measured with meteor radar in an independent way such that any subsequent bias correction or calibration is no longer required.
Wei Zhong, Xianghui Xue, Wen Yi, Iain M. Reid, Tingdi Chen, and Xiankang Dou
Atmos. Meas. Tech., 14, 3973–3988, https://doi.org/10.5194/amt-14-3973-2021, https://doi.org/10.5194/amt-14-3973-2021, 2021
Evgenia Belova, Peter Voelger, Sheila Kirkwood, Susanna Hagelin, Magnus Lindskog, Heiner Körnich, Sourav Chatterjee, and Karathazhiyath Satheesan
Atmos. Meas. Tech., 14, 2813–2825, https://doi.org/10.5194/amt-14-2813-2021, https://doi.org/10.5194/amt-14-2813-2021, 2021
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We validate horizontal wind measurements at altitudes of 0.5–14 km made with atmospheric radars: ESRAD located near Kiruna in the Swedish Arctic and MARA at the Indian research station Maitri in Antarctica, by comparison with radiosondes, the regional model HARMONIE-AROME and the ECMWF ERA5 reanalysis. Good agreement was found in general, and radar bias and uncertainty were estimated. These radars are planned to be used for validation of winds measured by lidar by the ESA satellite Aeolus.
Gizachew Kabite Wedajo, Misgana Kebede Muleta, and Berhan Gessesse Awoke
Atmos. Meas. Tech., 14, 2299–2316, https://doi.org/10.5194/amt-14-2299-2021, https://doi.org/10.5194/amt-14-2299-2021, 2021
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Satellite rainfall estimates (SREs) are alternative data sources for data-scarce basins. However, the accuracy of the products is plagued by multiple sources of errors. Therefore, SREs should be evaluated for particular basins before being used for other applications. The results of the study showed that CHIRPS2 and IMERG6 estimated rainfall and predicted hydrologic simulations well for Dhidhessa River Basin, which shows remote sensing technology could improve hydrologic studies.
Steven Knoop, Fred C. Bosveld, Marijn J. de Haij, and Arnoud Apituley
Atmos. Meas. Tech., 14, 2219–2235, https://doi.org/10.5194/amt-14-2219-2021, https://doi.org/10.5194/amt-14-2219-2021, 2021
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Doppler wind lidars are laser-based remote sensing instruments that measure the wind up to a few hundred metres or even a few kilometres. Their data can improve weather models and help forecasters. To investigate their accuracy and required meteorological conditions, we have carried out a 2-year measurement campaign of a wind lidar at our Cabauw test site and made a comparison with cup anemometers and wind vanes at several levels in a 213 m tall meteorological mast.
Joaquim V. Teixeira, Hai Nguyen, Derek J. Posselt, Hui Su, and Longtao Wu
Atmos. Meas. Tech., 14, 1941–1957, https://doi.org/10.5194/amt-14-1941-2021, https://doi.org/10.5194/amt-14-1941-2021, 2021
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Wind-tracking algorithms produce atmospheric motion vectors (AMVs) by tracking satellite observations. Accurately characterizing the uncertainties in AMVs is essential in assimilating them into data assimilation models. We develop a machine-learning-based approach for error characterization which involves Gaussian mixture model clustering and random forest using a simulation dataset of water vapor, AMVs, and true winds. We show that our method improves on existing AMV error characterizations.
Giovanni Martucci, Francisco Navas-Guzmán, Ludovic Renaud, Gonzague Romanens, S. Mahagammulla Gamage, Maxime Hervo, Pierre Jeannet, and Alexander Haefele
Atmos. Meas. Tech., 14, 1333–1353, https://doi.org/10.5194/amt-14-1333-2021, https://doi.org/10.5194/amt-14-1333-2021, 2021
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This article presents a validation of 1.5 years of pure rotational temperature data measured by the Raman lidar RALMO installed at the MeteoSwiss station of Payerne. The statistical results are in terms of bias and standard deviation with respect to two well-established radiosounding systems. The statistics are divided into daytime (bias = 0.28 K, SD = 0.62±0.03 K) and nighttime (bias = 0.29 K, SD = 0.66±0.06 K). The lidar temperature profiles are applied to cloud supersaturation studies.
Anders Tegtmeier Pedersen and Michael Courtney
Atmos. Meas. Tech., 14, 889–903, https://doi.org/10.5194/amt-14-889-2021, https://doi.org/10.5194/amt-14-889-2021, 2021
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This paper suggests and describes a method for calibrating wind lidars using a rotating flywheel. An uncertainty analysis shows that a standard uncertainty of 0.1 % can be achieved, with the main contributor being the width of the laser beam which is in agreement with experimental results. The method can potentially lower the calibration uncertainty of wind lidars, which today is often based on cup anemometers, and thus lead to better wind assessments and perhaps more widespread use.
Kaisa Lakkala, Jukka Kujanpää, Colette Brogniez, Nicolas Henriot, Antti Arola, Margit Aun, Frédérique Auriol, Alkiviadis F. Bais, Germar Bernhard, Veerle De Bock, Maxime Catalfamo, Christine Deroo, Henri Diémoz, Luca Egli, Jean-Baptiste Forestier, Ilias Fountoulakis, Katerina Garane, Rosa Delia Garcia, Julian Gröbner, Seppo Hassinen, Anu Heikkilä, Stuart Henderson, Gregor Hülsen, Bjørn Johnsen, Niilo Kalakoski, Angelos Karanikolas, Tomi Karppinen, Kevin Lamy, Sergio F. León-Luis, Anders V. Lindfors, Jean-Marc Metzger, Fanny Minvielle, Harel B. Muskatel, Thierry Portafaix, Alberto Redondas, Ricardo Sanchez, Anna Maria Siani, Tove Svendby, and Johanna Tamminen
Atmos. Meas. Tech., 13, 6999–7024, https://doi.org/10.5194/amt-13-6999-2020, https://doi.org/10.5194/amt-13-6999-2020, 2020
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The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite was launched on 13 October 2017 to provide the atmospheric composition for atmosphere and climate research. Ground-based data from 25 sites located in Arctic, subarctic, temperate, equatorial and Antarctic
areas were used for the validation of the TROPOMI surface ultraviolet (UV) radiation product. For most sites 60 %–80 % of TROPOMI data was within ± 20 % of ground-based data.
Pauline Martinet, Domenico Cimini, Frédéric Burnet, Benjamin Ménétrier, Yann Michel, and Vinciane Unger
Atmos. Meas. Tech., 13, 6593–6611, https://doi.org/10.5194/amt-13-6593-2020, https://doi.org/10.5194/amt-13-6593-2020, 2020
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Each year large human and economical losses are due to fog episodes. However, fog forecasts remain quite inaccurate, partly due to a lack of observations in the atmospheric boundary layer. The benefit of ground-based microwave radiometers has been investigated and has demonstrated their capability of significantly improving the initial state of temperature and liquid water content profiles in current numerical weather prediction models, paving the way for improved fog forecasts in the future.
Holger Baars, Alina Herzog, Birgit Heese, Kevin Ohneiser, Karsten Hanbuch, Julian Hofer, Zhenping Yin, Ronny Engelmann, and Ulla Wandinger
Atmos. Meas. Tech., 13, 6007–6024, https://doi.org/10.5194/amt-13-6007-2020, https://doi.org/10.5194/amt-13-6007-2020, 2020
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A first validation for the European satellite Aeolus is presented. Aeolus is the first satellite that can actively measure horizontal wind profiles from space.
Radiosonde launches on board the German research vessel Polarstern have been utilized to validate Aeolus observations over the Atlantic Ocean, a region where almost no other reference measurements are available. It is shown that Aeolus is able to measure accurately atmospheric winds and thus may significantly improve weather forecasts.
Giacomo Roversi, Pier Paolo Alberoni, Anna Fornasiero, and Federico Porcù
Atmos. Meas. Tech., 13, 5779–5797, https://doi.org/10.5194/amt-13-5779-2020, https://doi.org/10.5194/amt-13-5779-2020, 2020
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The microwave signal travelling between two antennas of the commercial mobile backhaul network is strongly attenuated by rainfall. The open-source RAINLINK algorithm extracts rainfall rate maps, processing the attenuation data recorded by the transmission system. In this work, we applied RAINLINK to 357 Vodafone links in northern Italy and compared the outputs with the operational rain products of the local weather service (Arpae), outlining pros and cons and discussing error structure.
Clark J. Weaver, Pawan K. Bhartia, Dong L. Wu, Gordon J. Labow, and David E. Haffner
Atmos. Meas. Tech., 13, 5715–5723, https://doi.org/10.5194/amt-13-5715-2020, https://doi.org/10.5194/amt-13-5715-2020, 2020
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Currently, we do not know whether clouds will accelerate or moderate climate. We look to the past and ask whether cloudiness has changed over the last 4 decades. Using a suite of nine satellite instruments, we need to ensure that the first satellite, which was launched in 1980 and died in 1991, observed the same measurement as the eight other satellite instruments used in the record. If the instruments were measuring length and observing a 1.00 m long stick, they would all see 0.99 to 1.01 m.
Samuel Favrichon, Carlos Jimenez, and Catherine Prigent
Atmos. Meas. Tech., 13, 5481–5490, https://doi.org/10.5194/amt-13-5481-2020, https://doi.org/10.5194/amt-13-5481-2020, 2020
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Long-term monitoring of satellite-derived variables is necessary for a better understanding of the evolution of Earth parameters at global scale. However different instruments' observations used over the years need to be inter-calibrated with each other to provide meaningful information. This paper describes how a linear correction can improve the observations from the Scanning Multichannel Microwave Radiometer over continental surfaces to be more consistent with more recent radiometers.
Zhilu Wu, Yanxiong Liu, Yang Liu, Jungang Wang, Xiufeng He, Wenxue Xu, Maorong Ge, and Harald Schuh
Atmos. Meas. Tech., 13, 4963–4972, https://doi.org/10.5194/amt-13-4963-2020, https://doi.org/10.5194/amt-13-4963-2020, 2020
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The HY-2A calibration microwave radiometer (CMR) water vapor product is validated using ground-based GNSS observations along the coastline and shipborne GNSS observations over the Indian Ocean. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV, with 2.67 mm in rms within 100 km and an RMS of 1.57 mm with shipborne GNSS for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well.
Viswanathan Bringi, Kumar Vijay Mishra, Merhala Thurai, Patrick C. Kennedy, and Timothy H. Raupach
Atmos. Meas. Tech., 13, 4727–4750, https://doi.org/10.5194/amt-13-4727-2020, https://doi.org/10.5194/amt-13-4727-2020, 2020
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The raindrop size distribution and its moments are fundamental in many areas, such as radar measurement of rainfall using polarimetry and numerical modeling of the microphysical processes of rain formation and evolution. We develop a technique which uses advanced radar measurements and complete drop size distributions using two collocated instruments to retrieve the lower-order moments such as total drop concentration and rain water content. We demonstrate a proof-of-concept using a case study.
Allan C. Just, Yang Liu, Meytar Sorek-Hamer, Johnathan Rush, Michael Dorman, Robert Chatfield, Yujie Wang, Alexei Lyapustin, and Itai Kloog
Atmos. Meas. Tech., 13, 4669–4681, https://doi.org/10.5194/amt-13-4669-2020, https://doi.org/10.5194/amt-13-4669-2020, 2020
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A flexible machine-learning model was fit to explain the differences between estimates of water vapor from satellites versus ground stations in Northeastern USA. We use nine variables derived from the satellite acquisition and ground characteristics to explain this measurement error. Our results showed overall good agreement, but data from the Terra satellite were drifting too high in recent summers. Our model reduces measurement error and works well in new locations in the northeast.
Cited articles
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Bingöl, F., Mann, J., and Foussekis, D.: Modeling conically scanning lidar error in complex terrain with WAsP Engineering, Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi, 16 p. (Denmark. Forskningscenter Risoe. Risoe-R; No. 1664(EN)), available at: http://orbit.dtu.dk/services/downloadRegister/3332817/ris-r-1664.pdf, (last access: 14 February 2015), 2008.
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Boquet, M., Ribstein, B. R., Parmentier, R. P., Sauvage, L. S., and Cariou, J.-P. C.: Theoretical and CFD analysis of pulsed Doppler lidar wind profile measurement process in complex terrain, Proceedings of the EWEA European Wind Energy Conference, 20–23 April 2010, Warsaw, Poland.
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Short summary
Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications like wind energy, but their use often relies on assuming homogeneity in the wind. Using numerical simulations of stable flow past a wind turbine, we quantify the error expected because of the inhomogeneity of the flow. Large errors (30%) in winds are found near the wind turbine, but by three rotor diameters downwind, errors in the horizontal components have decreased to 15% of the inflow.
Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications...
