Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-2065-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-2065-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Mar 2021
Research article |
| 16 Mar 2021
| 16 Mar 2021
LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework
Stefano Letizia
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA
Lu Zhan
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA
Giacomo Valerio Iungo
CORRESPONDING AUTHOR
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA
Related authors
Raghavendra Krishnamurthy, Rob Newsom, Colleen Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna M. Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-29, https://doi.org/10.5194/wes-2024-29, 2024
Preprint under review for WES
Short summary
Short summary
The growth of wind farms in the central United States in the last decade has been staggering. This study looked at how wind farms affect the recovery of wind wakes – the disturbed air behind wind turbines. In places like the US Great Plains, phenomena such as low-level jets can form, changing how wind farms work. We studied how wind wakes recover under different weather conditions using real-world data, which is important for making wind energy more efficient and reliable.
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022, https://doi.org/10.5194/wes-7-1263-2022, 2022
Short summary
Short summary
The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2095–2113, https://doi.org/10.5194/amt-14-2095-2021, https://doi.org/10.5194/amt-14-2095-2021, 2021
Short summary
Short summary
The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in the wake of wind turbines to reconstruct mean wind speed and turbulence intensity. Various lidar scans performed during a field campaign for a wind farm in complex terrain are analyzed. The results endorse the application of the LiSBOA for lidar-based wind resource assessment and farm diagnosis.
Lu Zhan, Stefano Letizia, and Giacomo Valerio Iungo
Wind Energ. Sci., 5, 1601–1622, https://doi.org/10.5194/wes-5-1601-2020, https://doi.org/10.5194/wes-5-1601-2020, 2020
Short summary
Short summary
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.
Raghavendra Krishnamurthy, Rob Newsom, Colleen Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna M. Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-29, https://doi.org/10.5194/wes-2024-29, 2024
Preprint under review for WES
Short summary
Short summary
The growth of wind farms in the central United States in the last decade has been staggering. This study looked at how wind farms affect the recovery of wind wakes – the disturbed air behind wind turbines. In places like the US Great Plains, phenomena such as low-level jets can form, changing how wind farms work. We studied how wind wakes recover under different weather conditions using real-world data, which is important for making wind energy more efficient and reliable.
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022, https://doi.org/10.5194/wes-7-1263-2022, 2022
Short summary
Short summary
The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2095–2113, https://doi.org/10.5194/amt-14-2095-2021, https://doi.org/10.5194/amt-14-2095-2021, 2021
Short summary
Short summary
The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in the wake of wind turbines to reconstruct mean wind speed and turbulence intensity. Various lidar scans performed during a field campaign for a wind farm in complex terrain are analyzed. The results endorse the application of the LiSBOA for lidar-based wind resource assessment and farm diagnosis.
Matteo Puccioni and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 1457–1474, https://doi.org/10.5194/amt-14-1457-2021, https://doi.org/10.5194/amt-14-1457-2021, 2021
Short summary
Short summary
A procedure for correcting the turbulent-energy damping connected with spatial averaging of wind lidars is proposed. This effect of the lidar measuring process is modeled through a low-pass filter, whose order and cut-off frequency are estimated directly from the lidar data. The proposed procedure is first assessed through simultaneous and colocated lidar and sonic-anemometer measurements. Then it is applied to several datasets collected at sites with different terrain roughness.
Lu Zhan, Stefano Letizia, and Giacomo Valerio Iungo
Wind Energ. Sci., 5, 1601–1622, https://doi.org/10.5194/wes-5-1601-2020, https://doi.org/10.5194/wes-5-1601-2020, 2020
Short summary
Short summary
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.
Mithu Debnath, Giacomo Valerio Iungo, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Scott Gunter, Julie K. Lundquist, John L. Schroeder, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 1215–1227, https://doi.org/10.5194/amt-10-1215-2017, https://doi.org/10.5194/amt-10-1215-2017, 2017
Short summary
Short summary
The XPIA experiment was conducted in 2015 at the Boulder Atmospheric Observatory to estimate capabilities of various remote-sensing techniques for the characterization of complex atmospheric flows. Among different tests, XPIA provided the unique opportunity to perform simultaneous virtual towers with Ka-band radars and scanning Doppler wind lidars. Wind speed and wind direction were assessed against lidar profilers and sonic anemometer data, highlighting a good accuracy of the data retrieved.
Mithu Debnath, G. Valerio Iungo, Ryan Ashton, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Julie K. Lundquist, William J. Shaw, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 431–444, https://doi.org/10.5194/amt-10-431-2017, https://doi.org/10.5194/amt-10-431-2017, 2017
Short summary
Short summary
Triple RHI scans were performed with three simultaneous scanning Doppler wind lidars and assessed with lidar profiler and sonic anemometer data. This test is part of the XPIA experiment. The scan strategy consists in two lidars performing co-planar RHI scans, while a third lidar measures the transversal velocity component. The results show that horizontal velocity and wind direction are measured with good accuracy, while the vertical velocity is typically measured with a significant error.
Katherine McCaffrey, Paul T. Quelet, Aditya Choukulkar, James M. Wilczak, Daniel E. Wolfe, Steven P. Oncley, W. Alan Brewer, Mithu Debnath, Ryan Ashton, G. Valerio Iungo, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 393–407, https://doi.org/10.5194/amt-10-393-2017, https://doi.org/10.5194/amt-10-393-2017, 2017
Short summary
Short summary
During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) field campaign, the wake and flow distortion from a 300-meter meteorological tower was identified using pairs of sonic anemometers mounted on opposite sides of the tower, as well as profiling and scanning lidars. Wind speed deficits up to 50% and TKE increases of 2 orders of magnitude were observed at wind directions in the wake, along with wind direction differences (flow deflection) outside of the wake.
Aditya Choukulkar, W. Alan Brewer, Scott P. Sandberg, Ann Weickmann, Timothy A. Bonin, R. Michael Hardesty, Julie K. Lundquist, Ruben Delgado, G. Valerio Iungo, Ryan Ashton, Mithu Debnath, Laura Bianco, James M. Wilczak, Steven Oncley, and Daniel Wolfe
Atmos. Meas. Tech., 10, 247–264, https://doi.org/10.5194/amt-10-247-2017, https://doi.org/10.5194/amt-10-247-2017, 2017
Short summary
Short summary
This paper discusses trade-offs among various wind measurement strategies using scanning Doppler lidars. It is found that the trade-off exists between being able to make highly precise point measurements versus covering large spatial extents. The highest measurement precision is achieved when multiple lidar systems make wind measurements at one point in space, while highest spatial coverage is achieved through using single lidar scanning measurements and using complex retrieval techniques.
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
CALOTRITON: a convective boundary layer height estimation algorithm from ultra-high-frequency (UHF) wind profiler data
Enhancing consistency of microphysical properties of precipitation across the melting layer in dual-frequency precipitation radar data
Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer
Forward operator for polarimetric radio occultation measurements
Assessing atmospheric gravity wave spectra in the presence of observational gaps
Joint 1DVar retrievals of tropospheric temperature and water vapor from Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometer observations
Mispointing characterization and Doppler velocity correction for the conically scanning WIVERN Doppler radar
Radar and environment-based hail damage estimates using machine learning
A new power-law model for μ–Λ relationships in convective and stratiform rainfall
Suppression of precipitation bias in wind velocities from continuous-wave Doppler lidars
Difference spectrum fitting of the ion–neutral collision frequency from dual-frequency EISCAT measurements
Performance evaluation of three bio-optical models in aerosol and ocean color joint retrievals
Observation of horizontal temperature variations by a spatial heterodyne interferometer using single-sided interferograms
Version 8 IMK–IAA MIPAS temperatures from 12–15 µm spectra: Middle and Upper Atmosphere modes
GNSS radio occultation excess-phase processing for climate applications including uncertainty estimation
Impact analysis of processing strategies for long-term GPS zenith tropospheric delay (ZTD)
A directional surface reflectance climatology determined from TROPOMI observations
A multi-decadal time series of upper stratospheric temperature profiles from Odin-OSIRIS limb scattered spectra
Irradiance and cloud optical properties from solar photovoltaic systems
Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals
Higher-order calibration on WindRAD (Wind Radar) scatterometer winds
On the polarimetric backscatter by a still or quasi-still wind turbine
OH airglow observations with two identical spectrometers: benefits of increased data homogeneity in the identification of variations induced by the 11-year solar cycle, the QBO, and other factors
Improved rain event detection in Commercial Microwave Link time series via combination with MSG SEVIRI data
Broadband radiative quantities for the EarthCARE mission: the ACM-COM and ACM-RT products
Measuring rainfall using microwave links: the influence of temporal sampling
Long-term multi-source precipitation estimation with high resolution (RainGRS Clim)
An Improved BRDF Hotspot Model and its Use in VLIDORT to Study the Impact of Atmospheric Scattering on Hotspot Directional Signatures in the Atmosphere
Retrieval of snow layer and melt pond properties on Arctic sea ice from airborne imaging spectrometer observations
Using optimal estimation to retrieve winds from velocity-azimuth display (VAD) scans by a Doppler lidar
Angular sampling of a monochromatic, wide-field-of-view camera to augment next-generation Earth radiation budget satellite observations
Noise filtering options for conically scanning Doppler LiDAR measurements with low pulse accumulation
Next-generation radiance unfiltering process for the Clouds and Earth’s Radiant Energy System instrument
Drone-based photogrammetry combined with deep-learning to estimate hail size distributions and melting of hail on the ground
Investigation of Gravity Waves using Measurements from a Sodium Temperature/Wind Lidar Operated in Multi-Direction Mode
Estimating the refractivity bias of Formosat-7/COSMIC-II GNSS Radio Occultation in the planetary boundary layer
Efficient collocation of global navigation satellite system radio occultation soundings with passive nadir microwave soundings
Analysis of 2D airglow imager data with respect to dynamics using machine learning
Estimation of extreme precipitation events in Estonia and Italy using dual-polarization weather radar quantitative precipitation estimations
The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for the snowfall retrieval at high latitudes
Detection and localization of F-layer ionospheric irregularities with the back-propagation method along the radio occultation ray path
Observations of anomalous propagation over waters near Sweden
Validation of Aeolus wind profiles using ground-based lidar and radiosonde observations at Réunion island and the Observatoire de Haute-Provence
Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach
High-resolution 3D winds derived from a modified WISSDOM synthesis scheme using multiple Doppler lidars and observations
Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations
Assessing and mitigating the radar–radar interference in the German C-band weather radar network
Spectral replacement using machine learning methods for continuous mapping of the Geostationary Environment Monitoring Spectrometer (GEMS)
Doppler spectra from DWD's operational C-band radar birdbath scan: sampling strategy, spectral postprocessing, and multimodal analysis for the retrieval of precipitation processes
High-fidelity retrieval from instantaneous line-of-sight returns of nacelle-mounted lidar including supervised machine learning
Alban Philibert, Marie Lothon, Julien Amestoy, Pierre-Yves Meslin, Solène Derrien, Yannick Bezombes, Bernard Campistron, Fabienne Lohou, Antoine Vial, Guylaine Canut-Rocafort, Joachim Reuder, and Jennifer K. Brooke
Atmos. Meas. Tech., 17, 1679–1701, https://doi.org/10.5194/amt-17-1679-2024, https://doi.org/10.5194/amt-17-1679-2024, 2024
Short summary
Short summary
We present a new algorithm, CALOTRITON, for the retrieval of the convective boundary layer depth with ultra-high-frequency radar measurements. CALOTRITON is partly based on the principle that the top of the convective boundary layer is associated with an inversion and a decrease in turbulence. It is evaluated using ceilometer and radiosonde data. It is able to qualify the complexity of the vertical structure of the low troposphere and detect internal or residual layers.
Kamil Mroz, Alessandro Battaglia, and Ann M. Fridlind
Atmos. Meas. Tech., 17, 1577–1597, https://doi.org/10.5194/amt-17-1577-2024, https://doi.org/10.5194/amt-17-1577-2024, 2024
Short summary
Short summary
In this study, we examine the extent to which radar measurements from space can inform us about the properties of clouds and precipitation. Surprisingly, our analysis showed that the amount of ice turning into rain was lower than expected in the current product. To improve on this, we came up with a new way to extract information about the size and concentration of particles from radar data. As long as we use this method in the right conditions, we can even estimate how dense the ice is.
Volker Wulfmeyer, Christoph Senff, Florian Späth, Andreas Behrendt, Diego Lange, Robert M. Banta, W. Alan Brewer, Andreas Wieser, and David D. Turner
Atmos. Meas. Tech., 17, 1175–1196, https://doi.org/10.5194/amt-17-1175-2024, https://doi.org/10.5194/amt-17-1175-2024, 2024
Short summary
Short summary
A simultaneous deployment of Doppler, temperature, and water-vapor lidar systems is used to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of weather forecast and climate models.
Daisuke Hotta, Katrin Lonitz, and Sean Healy
Atmos. Meas. Tech., 17, 1075–1089, https://doi.org/10.5194/amt-17-1075-2024, https://doi.org/10.5194/amt-17-1075-2024, 2024
Short summary
Short summary
Global Navigation Satellite System (GNSS) polarimetric radio occultation (PRO) is a new type of GNSS observations that can detect heavy precipitation along the ray path between the emitter and receiver satellites. As a first step towards using these observations in numerical weather prediction (NWP), we developed a computer code that simulates GNSS-PRO observations from forecast fields produced by an NWP model. The quality of the developed simulator is evaluated with a number of case studies.
Mohamed Mossad, Irina Strelnikova, Robin Wing, and Gerd Baumgarten
Atmos. Meas. Tech., 17, 783–799, https://doi.org/10.5194/amt-17-783-2024, https://doi.org/10.5194/amt-17-783-2024, 2024
Short summary
Short summary
This numerical study addresses observational gaps' impact on atmospheric gravity wave spectra. Three methods, fast Fourier transform (FFT), generalized Lomb–Scargle periodogram (GLS), and Haar structure function (HSF), were tested on synthetic data. HSF is best for spectra with negative slopes. GLS excels for flat and positive slopes and identifying dominant frequencies. Accurately estimating these aspects is crucial for understanding gravity wave dynamics and energy transfer in the atmosphere.
Kuo-Nung Wang, Chi O. Ao, Mary G. Morris, George A. Hajj, Marcin J. Kurowski, Francis J. Turk, and Angelyn W. Moore
Atmos. Meas. Tech., 17, 583–599, https://doi.org/10.5194/amt-17-583-2024, https://doi.org/10.5194/amt-17-583-2024, 2024
Short summary
Short summary
In this article, we described a joint retrieval approach combining two techniques, RO and MWR, to obtain high vertical resolution and solve for temperature and moisture independently. The results show that the complicated structure in the lower troposphere can be better resolved with much smaller biases, and the RO+MWR combination is the most stable scenario in our sensitivity analysis. This approach is also applied to real data (COSMIC-2/Suomi-NPP) to show the promise of joint RO+MWR retrieval.
Filippo Emilio Scarsi, Alessandro Battaglia, Frederic Tridon, Paolo Martire, Ranvir Dhillon, and Anthony Illingworth
Atmos. Meas. Tech., 17, 499–514, https://doi.org/10.5194/amt-17-499-2024, https://doi.org/10.5194/amt-17-499-2024, 2024
Short summary
Short summary
The WIVERN mission, one of the two candidates to be the ESA's Earth Explorer 11 mission, aims at providing measurements of horizontal winds in cloud and precipitation systems through a conically scanning W-band Doppler radar. This work discusses four methods that can be used to characterize and correct the Doppler velocity error induced by the antenna mispointing. The proposed methodologies can be extended to other Doppler concepts featuring conically scanning or slant viewing Doppler systems.
Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder
Atmos. Meas. Tech., 17, 407–422, https://doi.org/10.5194/amt-17-407-2024, https://doi.org/10.5194/amt-17-407-2024, 2024
Short summary
Short summary
The paper addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilizes a large dataset of insurance hail damage claims and radar observations. A deep neural network was employed, trained with local meteorological variables and the radar observations, to better quantify hail damage. Key meteorological variables were identified to have the most predictive capability in this regard.
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 17, 235–245, https://doi.org/10.5194/amt-17-235-2024, https://doi.org/10.5194/amt-17-235-2024, 2024
Short summary
Short summary
A common method to retrieve important information about the microphysical structure of rain (DSD retrievals) requires a constrained relationship between the drop size distribution parameters. The most widely accepted empirical relationship is between μ and Λ. The relationship shows variability across the different types of rainfall (convective or stratiform). The new proposed power-law model to represent the μ–Λ relation provides a better physical interpretation of the relationship coefficients.
Liqin Jin, Jakob Mann, Nikolas Angelou, and Mikael Sjöholm
Atmos. Meas. Tech., 16, 6007–6023, https://doi.org/10.5194/amt-16-6007-2023, https://doi.org/10.5194/amt-16-6007-2023, 2023
Short summary
Short summary
By sampling the spectra from continuous-wave Doppler lidars very fast, the rain-induced Doppler signal can be suppressed and the bias in the wind velocity estimation can be reduced. The method normalizes 3 kHz spectra by their peak values before averaging them down to 50 Hz. Over 3 h, we observe a significant reduction in the bias of the lidar data relative to the reference sonic data when the largest lidar focus distance is used. The more it rains, the more the bias is reduced.
Florian Günzkofer, Gunter Stober, Dimitry Pokhotelov, Yasunobu Miyoshi, and Claudia Borries
Atmos. Meas. Tech., 16, 5897–5907, https://doi.org/10.5194/amt-16-5897-2023, https://doi.org/10.5194/amt-16-5897-2023, 2023
Short summary
Short summary
Electric currents in the ionosphere can impact both satellite and ground-based infrastructure. These currents depend strongly on the collisions of ions and neutral particles. Measuring ion–neutral collisions is often only possible via certain assumptions. The direct measurement of ion–neutral collision frequencies is possible with multifrequency incoherent scatter radar measurements. This paper presents one analysis method of such measurements and discusses its advantages and disadvantages.
Neranga K. Hannadige, Peng-Wang Zhai, Meng Gao, Yongxiang Hu, P. Jeremy Werdell, Kirk Knobelspiesse, and Brian Cairns
Atmos. Meas. Tech., 16, 5749–5770, https://doi.org/10.5194/amt-16-5749-2023, https://doi.org/10.5194/amt-16-5749-2023, 2023
Short summary
Short summary
We evaluated the impact of three ocean optical models with different numbers of free parameters on the performance of an aerosol and ocean color remote sensing algorithm using the multi-angle polarimeter (MAP) measurements. It was demonstrated that the three- and seven-parameter bio-optical models can be used to accurately represent both open and coastal waters, whereas the one-parameter model has smaller retrieval uncertainty over open water.
Konstantin Ntokas, Jörn Ungermann, Martin Kaufmann, Tom Neubert, and Martin Riese
Atmos. Meas. Tech., 16, 5681–5696, https://doi.org/10.5194/amt-16-5681-2023, https://doi.org/10.5194/amt-16-5681-2023, 2023
Short summary
Short summary
A nanosatellite was developed to obtain 1-D vertical temperature profiles in the mesosphere and lower thermosphere, which can be used to derive wave parameters needed for atmospheric models. A new processing method is shown, which allows one to extract two 1-D temperature profiles. The location of the two profiles is analyzed, as it is needed for deriving wave parameters. We show that this method is feasible, which however will increase the requirements of an accurate calibration and processing.
Maya García-Comas, Bernd Funke, Manuel López-Puertas, Norbert Glatthor, Udo Grabowski, Sylvia Kellmann, Michael Kiefer, Andrea Linden, Belén Martínez-Mondéjar, Gabriele P. Stiller, and Thomas von Clarmann
Atmos. Meas. Tech., 16, 5357–5386, https://doi.org/10.5194/amt-16-5357-2023, https://doi.org/10.5194/amt-16-5357-2023, 2023
Short summary
Short summary
We have released version 8 of MIPAS IMK–IAA temperatures and pointing information retrieved from MIPAS Middle and Upper Atmosphere mode version 8.03 calibrated spectra, covering 20–115 km altitude. We considered non-local thermodynamic equilibrium emission explicitly for each limb scan, essential to retrieve accurate temperatures above the mid-mesosphere. Comparisons of this temperature dataset with SABER measurements show excellent agreement, improving those of previous MIPAS versions.
Josef Innerkofler, Gottfried Kirchengast, Marc Schwärz, Christian Marquardt, and Yago Andres
Atmos. Meas. Tech., 16, 5217–5247, https://doi.org/10.5194/amt-16-5217-2023, https://doi.org/10.5194/amt-16-5217-2023, 2023
Short summary
Short summary
Atmosphere remote sensing using GNSS radio occultation provides a highly valuable basis for atmospheric and climate science. For the highest-quality demands, the Wegener Center set up a rigorous system for processing low-level measurement data. This excess-phase processing setup includes integrated quality control and uncertainty estimation. It was successfully evaluated and inter-compared, ensuring the capability of producing reliable long-term data records for climate applications.
Jingna Bai, Yidong Lou, Weixing Zhang, Yaozong Zhou, Zhenyi Zhang, Chuang Shi, and Jingnan Liu
Atmos. Meas. Tech., 16, 5249–5259, https://doi.org/10.5194/amt-16-5249-2023, https://doi.org/10.5194/amt-16-5249-2023, 2023
Short summary
Short summary
Homogenized atmospheric water vapor data are an important prerequisite for climate analysis. Compared to other techniques, GPS has an inherent homogeneity advantage but requires reprocessing and homogenization to eliminate impacts of applied strategy and observation environmental changes. The low-elevation cut-off angles are suggested for the best estimates of zenith tropospheric delay (ZTD) reprocessing time series when compared to homogenized radiosonde data or ERA5 reference time series.
Lieuwe G. Tilstra, Martin de Graaf, Victor Trees, Pavel Litvinov, Oleg Dubovik, and Piet Stammes
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-222, https://doi.org/10.5194/amt-2023-222, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
This paper introduced a new surface albedo climatology of directionally dependent Lambertian-equivalent reflectivity (DLER) observed by the TROPOMI instrument on the Sentinel-5 Precursor satellite. The database contains monthly fields of DLER for 21 wavelength bands at a relatively high spatial resolution of 0.125 by 0.125 degrees. The anisotropy of the surface reflection is handled by parameterisation of the viewing angle dependence.
Daniel J. Zawada, Kimberlee Robyn Dubé, Taran W. Warnock, Adam Edward Bourassa, Susann Tegtmeier, and Douglas A. Degenstein
EGUsphere, https://doi.org/10.5194/egusphere-2023-2264, https://doi.org/10.5194/egusphere-2023-2264, 2023
Short summary
Short summary
There remain large uncertainties in long term changes of stratospheric atmospheric temperatures. We have produced a more than 20 year time series of satellite-based temperature measurements from the OSIRIS instrument in the upper-middle stratosphere. The dataset is publicly available, and intended to be used to better understand changes in stratospheric temperatures.
James Barry, Stefanie Meilinger, Klaus Pfeilsticker, Anna Herman-Czezuch, Nicola Kimiaie, Christopher Schirrmeister, Rone Yousif, Tina Buchmann, Johannes Grabenstein, Hartwig Deneke, Jonas Witthuhn, Claudia Emde, Felix Gödde, Bernhard Mayer, Leonhard Scheck, Marion Schroedter-Homscheidt, Philipp Hofbauer, and Matthias Struck
Atmos. Meas. Tech., 16, 4975–5007, https://doi.org/10.5194/amt-16-4975-2023, https://doi.org/10.5194/amt-16-4975-2023, 2023
Short summary
Short summary
Measured power data from solar photovoltaic (PV) systems contain information about the state of the atmosphere. In this work, power data from PV systems in the Allgäu region in Germany were used to determine the solar irradiance at each location, using state-of-the-art simulation and modelling. The results were validated using concurrent measurements of the incoming solar radiation in each case. If applied on a wider scale, this algorithm could help improve weather and climate models.
Wan Wu, Xu Liu, Liqiao Lei, Xiaozhen Xiong, Qiguang Yang, Qing Yue, Daniel K. Zhou, and Allen M. Larar
Atmos. Meas. Tech., 16, 4807–4832, https://doi.org/10.5194/amt-16-4807-2023, https://doi.org/10.5194/amt-16-4807-2023, 2023
Short summary
Short summary
We present a new operational physical retrieval algorithm that is used to retrieve atmospheric properties for each single field-of-view measurement of hyper-spectral IR sounders. The physical scheme includes a cloud-scattering calculation in its forward-simulation part. The data product generated using this algorithm has an advantage over traditional IR sounder data production algorithms in terms of improved spatial resolution and minimized error due to cloud contamination.
Zhen Li, Ad Stoffelen, Anton Verhoef, Zhixiong Wang, Jian Shang, and Honggang Yin
Atmos. Meas. Tech., 16, 4769–4783, https://doi.org/10.5194/amt-16-4769-2023, https://doi.org/10.5194/amt-16-4769-2023, 2023
Short summary
Short summary
WindRAD (Wind Radar) is the first dual-frequency rotating fan-beam scatterometer in orbit. We observe non-linearity in the backscatter distribution. Therefore, higher-order calibration (HOC) is proposed, which removes the non-linearities per incidence angle. The combination of HOC and NOCant is discussed. It can remove not only the non-linearity but also the anomalous harmonic azimuth dependencies caused by the antenna rotation; hence the optimal winds can be achieved with this combination.
Marco Gabella, Martin Lainer, Daniel Wolfensberger, and Jacopo Grazioli
Atmos. Meas. Tech., 16, 4409–4422, https://doi.org/10.5194/amt-16-4409-2023, https://doi.org/10.5194/amt-16-4409-2023, 2023
Short summary
Short summary
A still wind turbine observed with a fixed-pointing radar antenna has shown distinctive polarimetric signatures: the correlation coefficient between the two orthogonal polarization states was persistently equal to 1. The differential reflectivity and the radar reflectivity factors were also stable in time. Over 2 min (2000 Hz, 128 pulses were used; consequently, the sampling time was 64 ms), the standard deviation of the differential backscattering phase shift was only a few degrees.
Carsten Schmidt, Lisa Küchelbacher, Sabine Wüst, and Michael Bittner
Atmos. Meas. Tech., 16, 4331–4356, https://doi.org/10.5194/amt-16-4331-2023, https://doi.org/10.5194/amt-16-4331-2023, 2023
Short summary
Short summary
Two identical instruments in a parallel setup were used to observe the mesospheric OH airglow for more than 10 years (2009–2020) at 47.42°N, 10.98°E. This allows unique analyses of data quality aspects and their impact on the obtained results. During solar cycle 24 the influence of the sun was strong (∼6 K per 100 sfu). A quasi-2-year oscillation (QBO) of ±1 K is observed mainly during the maximum of the solar cycle. Unlike the stratospheric QBO the variation has a period of or below 24 months.
Andreas Wagner, Christian Chwala, Maximilian Graf, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, and Harald Kunstmann
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-175, https://doi.org/10.5194/amt-2023-175, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
Commercial Microwave Links (CMLs) can be used for rainfall retrieval. The detection of rainy periods in their attenuation time series is the most important processing step. We investigate the usage of rainfall information from MSG SEVIRI for this task, compare these methods to existing methods, and finally combined both approaches. The results show advantages for SEVIRI based methods for light rain and poor performing CMLs. Our newly developed combination reveals the best overall performance.
Jason N. S. Cole, Howard W. Barker, Zhipeng Qu, Najda Villefranque, and Mark W. Shephard
Atmos. Meas. Tech., 16, 4271–4288, https://doi.org/10.5194/amt-16-4271-2023, https://doi.org/10.5194/amt-16-4271-2023, 2023
Short summary
Short summary
Measurements from the EarthCARE satellite mission will be used to retrieve profiles of cloud and aerosol properties. These retrievals are combined with auxiliary information about surface properties and atmospheric state, e.g., temperature and water vapor. This information allows computation of 1D and 3D solar and thermal radiative transfer for small domains, which are compared with coincident radiometer observations to continually assess EarthCARE retrievals.
Luuk D. van der Valk, Miriam Coenders-Gerrits, Rolf W. Hut, Aart Overeem, Bas Walraven, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1971, https://doi.org/10.5194/egusphere-2023-1971, 2023
Short summary
Short summary
Microwave links, often part of mobile phone networks, can be used to measure rainfall along the link path by determining the signal loss caused by rainfall. We use high-frequency data of multiple microwave links to recreate commonly used sampling strategies. For time intervals up to 1 min, the influence of sampling strategies on estimated rainfall intensities is relatively little, while for intervals longer than 5–15 min, the sampling strategy can have significant influences on the estimates.
Anna Jurczyk, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz
Atmos. Meas. Tech., 16, 4067–4079, https://doi.org/10.5194/amt-16-4067-2023, https://doi.org/10.5194/amt-16-4067-2023, 2023
Short summary
Short summary
A data-processing algorithm, RainGRS Clim, has been developed to work on precipitation accumulations such as daily or monthly totals. The algorithm makes the most of additional opportunities: access to high-quality data that are not operationally available and greater efficiency of the algorithms for data quality control and merging for longer accumulations. Monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally.
Xiaozhen Xiong, Xu Liu, Robert Spurr, Ming Zhao, Qiguang Yang, Wan Wu, and Liqiao Lei
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-159, https://doi.org/10.5194/amt-2023-159, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
The term “hotspot” refers to the sharp increase of reflectance occurring when incident (solar) and reflected (viewing) directions coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications, but current models typically require large values of computations to represent the hotspot accurately. This paper developed an improved hotspot BRDF model that converges much faster and was tested in a model.
Sophie Rosenburg, Charlotte Lange, Evelyn Jäkel, Michael Schäfer, André Ehrlich, and Manfred Wendisch
Atmos. Meas. Tech., 16, 3915–3930, https://doi.org/10.5194/amt-16-3915-2023, https://doi.org/10.5194/amt-16-3915-2023, 2023
Short summary
Short summary
Snow layer melting and melt pond formation on Arctic sea ice are important seasonal processes affecting the surface reflection and energy budget. Sea ice reflectivity was surveyed by airborne imaging spectrometers in May–June 2017. Adapted retrieval approaches were applied to find snow layer liquid water fraction, snow grain effective radius, and melt pond depth. The retrievals show the potential and limitations of spectral airborne imaging to map melting snow layer and melt pond properties.
Sunil Baidar, Timothy J. Wagner, David D. Turner, and W. Alan Brewer
Atmos. Meas. Tech., 16, 3715–3726, https://doi.org/10.5194/amt-16-3715-2023, https://doi.org/10.5194/amt-16-3715-2023, 2023
Short summary
Short summary
This paper provides a new method to retrieve wind profiles from coherent Doppler lidar (CDL) measurements. It takes advantage of layer-to-layer correlation in wind profiles to provide continuous profiles of up to 3 km by filling in the gaps where the CDL signal is too small to retrieve reliable results by itself. Comparison with the current method and collocated radiosonde wind measurements showed excellent agreement with no degradation in results where the current method gives valid results.
Jake J. Gristey, K. Sebastian Schmidt, Hong Chen, Daniel R. Feldman, Bruce C. Kindel, Joshua Mauss, Mathew van den Heever, Maria Z. Hakuba, and Peter Pilewskie
Atmos. Meas. Tech., 16, 3609–3630, https://doi.org/10.5194/amt-16-3609-2023, https://doi.org/10.5194/amt-16-3609-2023, 2023
Short summary
Short summary
The concept of a satellite-based camera is demonstrated for sampling the angular distribution of outgoing radiance from Earth needed to generate data products for new radiation budget spectral channels.
Eileen Päschke and Carola Detring
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-153, https://doi.org/10.5194/amt-2023-153, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
Little noise in radial velocity Doppler lidar measurements can contribute to large errors in retrieved turbulence variables. In order to distinguish between plausible and erroneous measurements we developed new filter techniques that work independently of the choice of a specific threshold for the signal-to-noise ratio. The performance of these techniques is discussed, both, by means of assessing the filter results, and by comparing retrieved turbulence variables versus independent measurements.
Lusheng Liang, Wenying Su, Sergio Sejas, Zachary A. Eitzen, and Norman G. Loeb
EGUsphere, https://doi.org/10.5194/egusphere-2023-1670, https://doi.org/10.5194/egusphere-2023-1670, 2023
Short summary
Short summary
This paper describes an updated process to obtain unfiltered radiation from CERES satellite instruments by incorporating the most recent developments in radiative transfer modeling and ancillary input datasets (e.g., realistic representation of land surface radiation and climatology of surface temperatures and aerosols) during the past 20 years. The resulted global mean of instantaneous SW and LW fluxes are changed by less than 0.5 Wm-2 with regional differences can be as large as 2.0 Wm-2.
Martin Lainer, Killian P. Brennan, Alessandro Hering, Jérôme Kopp, Samuel Monhart, Daniel Wolfensberger, and Urs Germann
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-89, https://doi.org/10.5194/amt-2023-89, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
We present an approach for hail size estimation combining drone-based photogrammetry with a deep-learning object detection model. The method is applied to a hail event of a supercell that crossed Switzerland on June 20, 2021, allowing an accurate estimation of the hail size distribution (>18000 samples). Results are then compared with data from nearby automatic hail sensors and radar-based hail products. The opportunity to monitor the hail melting on the ground is also investigated.
Bing Cao and Alan Z. Liu
EGUsphere, https://doi.org/10.5194/egusphere-2023-1563, https://doi.org/10.5194/egusphere-2023-1563, 2023
Short summary
Short summary
A narrow-band sodium lidar measures atmospheric waves but focuses on vertical variations. By analyzing phase shifts among laser beams in different directions, horizontal wave information can be derived. With this method, two wave packets were identified, propagating in different directions. These waves interacted with thin evanescent layers, reflecting partially but transmitting energy to higher altitudes. The method can be used to detect more gravity waves from similar lidar systems worldwide.
Gia Huan Pham, Shu-Chih Yang, Chih-Chien Chang, Shu-Ya Chen, and Cheng-Yung Huang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1246, https://doi.org/10.5194/egusphere-2023-1246, 2023
Short summary
Short summary
This research examines the characteristics of low-level GNSS radio occultation (RO) refractivity bias over ocean and land and their dependency on the RO retrieval uncertainty, atmospheric temperature, and moisture. We proposed methods for estimating the region-dependent refractivity bias. Our methods can be applied to calibrate the bias under different atmospheric conditions and thus improves the applications of the GNSS RO data in the planetary boundary layer.
Alex Meredith, Stephen Leroy, Lucy Halperin, and Kerri Cahoy
Atmos. Meas. Tech., 16, 3345–3361, https://doi.org/10.5194/amt-16-3345-2023, https://doi.org/10.5194/amt-16-3345-2023, 2023
Short summary
Short summary
We developed a new efficient algorithm leveraging orbital dynamics to collocate radio occultation soundings with microwave radiance soundings. This new algorithm is 99 % accurate and is much faster than traditional collocation-finding approaches. Speeding up collocation finding is useful for calibrating and validating microwave radiometers and for data assimilation into numerical weather prediction models. Our algorithm can also be used to predict collocation yield for new satellite missions.
René Sedlak, Andreas Welscher, Patrick Hannawald, Sabine Wüst, Rainer Lienhart, and Michael Bittner
Atmos. Meas. Tech., 16, 3141–3153, https://doi.org/10.5194/amt-16-3141-2023, https://doi.org/10.5194/amt-16-3141-2023, 2023
Short summary
Short summary
We show that machine learning can help in classifying images of the OH* airglow, a thin layer in the middle atmosphere (ca. 86 km height) emitting infrared radiation, in an efficient way. By doing this,
dynamicepisodes of strong movement in the OH* airglow caused predominantly by waves can be extracted automatically from large data sets. Within these dynamic episodes, turbulent wave breaking can also be found. We use these observations of turbulence to derive the energy released by waves.
Roberto Cremonini, Tanel Voormansik, Piia Post, and Dmitri Moisseev
Atmos. Meas. Tech., 16, 2943–2956, https://doi.org/10.5194/amt-16-2943-2023, https://doi.org/10.5194/amt-16-2943-2023, 2023
Short summary
Short summary
Extreme rainfall for a specific location is commonly evaluated when designing stormwater management systems. This study investigates the use of quantitative precipitation estimations (QPEs) based on polarimetric weather radar data, without rain gauge corrections, to estimate 1 h rainfall total maxima in Italy and Estonia. We show that dual-polarization weather radar provides reliable QPEs and effective estimations of return periods for extreme rainfall in climatologically homogeneous regions.
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-94, https://doi.org/10.5194/amt-2023-94, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
A new snowfall retrieval algorithm developed especially for high latitude environmental conditions and based on Advanced Technology Microwave Sounder (ATMS) observations is described. The algorithm exploits ATMS low-frequency channels to retrieve the surface emissivity and compute the clear-sky brightness temperature, to highlight the snowfall signature. This information is used in a neural network based snowfall retrieval, trained against Cloud Profiling Radar snowfall products.
Vinícius Ludwig-Barbosa, Joel Rasch, Thomas Sievert, Anders Carlström, Mats I. Pettersson, Viet Thuy Vu, and Jacob Christensen
Atmos. Meas. Tech., 16, 1849–1864, https://doi.org/10.5194/amt-16-1849-2023, https://doi.org/10.5194/amt-16-1849-2023, 2023
Short summary
Short summary
In this paper, the back-propagation method's capabilities and limitations regarding the location of irregularity regions in the ionosphere, e.g. equatorial plasma bubbles, are evaluated. The assessment was performed with simulations in which different scenarios were assumed. The results showed that the location estimate is possible if the amplitude of the ionospheric disturbance is stronger than the instrument noise level. Further, multiple patches can be located if regions are well separated.
Lars Norin
Atmos. Meas. Tech., 16, 1789–1801, https://doi.org/10.5194/amt-16-1789-2023, https://doi.org/10.5194/amt-16-1789-2023, 2023
Short summary
Short summary
The atmosphere can cause radar beams to bend more or less towards the ground. When the atmosphere differs from standard atmospheric conditions, the propagation is considered anomalous. Radars affected by anomalous propagation can observe ground clutter far beyond the radar horizon. Here, 4.5 years' worth of data from five operational Swedish weather radars are presented. Analyses of the data reveal a strong seasonal cycle and weaker diurnal cycle in ground clutter from across nearby waters.
Mathieu Ratynski, Sergey Khaykin, Alain Hauchecorne, Robin Wing, Jean-Pierre Cammas, Yann Hello, and Philippe Keckhut
Atmos. Meas. Tech., 16, 997–1016, https://doi.org/10.5194/amt-16-997-2023, https://doi.org/10.5194/amt-16-997-2023, 2023
Short summary
Short summary
Aeolus is the first spaceborne wind lidar providing global wind measurements since 2018. This study offers a comprehensive analysis of Aeolus instrument performance, using ground-based wind lidars and meteorological radiosondes, at tropical and mid-latitudes sites. The analysis allows assessing the long-term evolution of the satellite's performance for more than 3 years. The results will help further elaborate the understanding of the error sources and the behavior of the Doppler wind lidar.
Anne-Claire Billault-Roux, Gionata Ghiggi, Louis Jaffeux, Audrey Martini, Nicolas Viltard, and Alexis Berne
Atmos. Meas. Tech., 16, 911–940, https://doi.org/10.5194/amt-16-911-2023, https://doi.org/10.5194/amt-16-911-2023, 2023
Short summary
Short summary
Better understanding and modeling snowfall properties and processes is relevant to many fields, ranging from weather forecasting to aircraft safety. Meteorological radars can be used to gain insights into the microphysics of snowfall. In this work, we propose a new method to retrieve snowfall properties from measurements of radars with different frequencies. It relies on an original deep-learning framework, which incorporates knowledge of the underlying physics, i.e., electromagnetic scattering.
Chia-Lun Tsai, Kwonil Kim, Yu-Chieng Liou, and GyuWon Lee
Atmos. Meas. Tech., 16, 845–869, https://doi.org/10.5194/amt-16-845-2023, https://doi.org/10.5194/amt-16-845-2023, 2023
Short summary
Short summary
Since the winds in clear-air conditions usually play an important role in the initiation of various weather systems and phenomena, the modified Wind Synthesis System using Doppler Measurements (WISSDOM) synthesis scheme was developed to derive high-quality and high-spatial-resolution 3D winds under clear-air conditions. The performance and accuracy of derived 3D winds from this modified scheme were evaluated with an extreme strong wind event over complex terrain in Pyeongchang, South Korea.
Simone Kotthaus, Juan Antonio Bravo-Aranda, Martine Collaud Coen, Juan Luis Guerrero-Rascado, Maria João Costa, Domenico Cimini, Ewan J. O'Connor, Maxime Hervo, Lucas Alados-Arboledas, María Jiménez-Portaz, Lucia Mona, Dominique Ruffieux, Anthony Illingworth, and Martial Haeffelin
Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, https://doi.org/10.5194/amt-16-433-2023, 2023
Short summary
Short summary
Profile observations of the atmospheric boundary layer now allow for layer heights and characteristics to be derived at high temporal and vertical resolution. With novel high-density ground-based remote-sensing measurement networks emerging, horizontal information content is also increasing. This review summarises the capabilities and limitations of various sensors and retrieval algorithms which need to be considered during the harmonisation of data products for high-impact applications.
Michael Frech, Cornelius Hald, Maximilian Schaper, Bertram Lange, and Benjamin Rohrdantz
Atmos. Meas. Tech., 16, 295–309, https://doi.org/10.5194/amt-16-295-2023, https://doi.org/10.5194/amt-16-295-2023, 2023
Short summary
Short summary
Weather radar data are the backbone of a lot of meteorological products. In order to obtain a better low-level coverage with radar data, additional systems have to be included. The frequency range in which radars are allowed to operate is limited. A potential radar-to-radar interference has to be avoided. The paper derives guidelines on how additional radars can be included into a C-band weather radar network and how interferences can be avoided.
Yeeun Lee, Myoung-Hwan Ahn, Mina Kang, and Mijin Eo
Atmos. Meas. Tech., 16, 153–168, https://doi.org/10.5194/amt-16-153-2023, https://doi.org/10.5194/amt-16-153-2023, 2023
Short summary
Short summary
This study aims to verify that a partly defective hyperspectral measurement can be successfully reproduced with concise machine learning models coupled with principal component analysis. Evaluation of the approach is performed with radiances and retrieval results of ozone and cloud properties. Considering that GEMS is the first geostationary UV–VIS hyperspectral spectrometer, we expect our findings can be introduced further to similar geostationary environmental instruments to be launched soon.
Mathias Gergely, Maximilian Schaper, Matthias Toussaint, and Michael Frech
Atmos. Meas. Tech., 15, 7315–7335, https://doi.org/10.5194/amt-15-7315-2022, https://doi.org/10.5194/amt-15-7315-2022, 2022
Short summary
Short summary
This study presents the new vertically pointing birdbath scan of the German C-band radar network, which provides high-resolution profiles of precipitating clouds above all DWD weather radars since the spring of 2021. Our AI-based postprocessing method for filtering and analyzing the recorded radar data offers a unique quantitative view into a wide range of precipitation events from snowfall over stratiform rain to intense frontal showers and will be used to complement DWD's operational services.
Kenneth A. Brown and Thomas G. Herges
Atmos. Meas. Tech., 15, 7211–7234, https://doi.org/10.5194/amt-15-7211-2022, https://doi.org/10.5194/amt-15-7211-2022, 2022
Short summary
Short summary
The character of the airflow around and within wind farms has a significant impact on the energy output and longevity of the wind turbines in the farm. For both research and control purposes, accurate measurements of the wind speed are required, and these are often accomplished with remote sensing devices. This article pertains to a field experiment of a lidar mounted to a wind turbine and demonstrates three data post-processing techniques with efficacy at extracting useful airflow information.
Cited articles
Abkar, M. and Porté-Agel, F.: The effect of free-atmosphere stratification on boundary-layer flow and power output from very large wind farms, Energies, 6, 2338–2361, https://doi.org/10.3390/en6052338, 2013. a
Achtemeier, G. L.: The Impact of Data Boundaries upon a Successive Corrections Objective Analysis of Limited-Area Datasets, Mon. Weather Rev., 114, 40–49, https://doi.org/10.1175/1520-0493(1986)114<0040:TIODBU>2.0.CO;2, 1986. a
Achtemeier, G. L.: Modification of a Successive Corrections Objective Analysis for Improved Derivative Calculations, Mon. Weather Rev., 117, 78–86, https://doi.org/10.1175/1520-0493(1989)117<0078:MOASCO>2.0.CO;2, 1989. a
Aitken, M. L. and Lundquist, J. K.: Utility-Scale Wind Turbine Wake Characterization Using Nacelle-Based Long-Range Scanning Lidar, J. Atmos. Ocean. Tech., 31, 1529–1539, https://doi.org/10.1175/JTECH-D-13-00218.1, 2014. a, b, c, d
Arenas, I., García, E., Fu, M. K., Orlandi, P., Hultmark, M., and Leonardi, S.: Comparison between super-hydrophobic, liquid infused and rough surfaces: a direct numerical simulation study, J. Fluid Mech., 869, 500–525, https://doi.org/10.1017/jfm.2019.222, 2019. a
Ashton, R., Iungo, G. V., Viola, F., Gallaire, F., and Camarri, S.: Hub vortex instability within wind turbine wakes : Effects of wind turbulence, loading conditions and blade aerodynamics, Physical Review Fluids, 1, 073 603, https://doi.org/10.1103/PhysRevFluids.1.073603, 2016. a, b
Askelson, M. A., Aubagnac, J. P., and Straka, J. M.: An Adaptation of the Barnes Filter Applied to the Objective Analysis of Radar Data, Mon. Weather Rev., 128, 3050–3082, https://doi.org/10.1175/1520-0493(2000)128<3050:AAOTBF>2.0.CO;2, 2000. a, b
Banakh, V. A., Smalikho, I. N., Köpp, F., and Werner, C.: Measurements of Turbulent Energy Dissipation Rate with a CW Doppler Lidar in the Atmospheric Boundary Layer, J. Atmos. Ocean. Tech., 16, 1044–1061, https://doi.org/10.1175/1520-0426(1999)016<1044:MOTEDR>2.0.CO;2, 1999. a
Banta, R. M., Pichugina, Y. L., and Brewer, W. A.: Turbulent Velocity-Variance Profiles in the Stable Boundary Layer Generated by a Nocturnal Low-Level Jet, J. Atmos. Sci., 63, 2700–2719, https://doi.org/10.1175/JAS3776.1, 2006. a, b
Barnes, S.: Application of Barnes Objective Analysis scheme. Part III: tuning for minimum error., J. Atmos. Ocean. Tech., 11, 1449–1458, https://doi.org/10.1175/1520-0426(1994)011<1459:AOTBOA>2.0.CO;2, 1994c. a
Barnes, S. L.: Mesoscale Objective Map Analysis Using Weighted Time-Series Observations, NOAA Technical Memorandum ERL NSSL-62, National Severe Storms Laboratory Norman, OK, USA, 1973. a
Barnes, S. L.: Applications of the Barnes Objective Analysis Scheme. Part I: Effects of Undersampling, Wave Position and Station Randomness, J. Atmos. Ocean. Tech., 11, 1433–1448, https://doi.org/10.1175/1520-0426(1994)011<1433:AOTBOA>2.0.CO;2, 1994a. a, b, c
Bayley, V. G. and Hammersley, J. M.: The ”Effective” Number of Independent Observations in an Autocorrelated Time Series, Supplement to the Journal of Royal Statistical Society, 8, 184–197, 1946. a
Bell, T. M., Klein, P., Wildmann, N., and Menke, R.: Analysis of flow in complex terrain using multi-Doppler lidar retrievals, Atmos. Meas. Tech., 13, 1357–1371, https://doi.org/10.5194/amt-13-1357-2020, 2020. a
Berg, J., Mann, J., Bechmann, A., Courtney, M. S., and Jørgensen, H. E.: The Bolund Experiment. Part I : Flow Over a Steep, Three-dimensional Hill, Bound.-Lay. Meteorol., 20, 219–243, https://doi.org/10.1007/s10546-011-9636-y, 2011. a
Bingöl, F., Mann, J., and Larsen, G. C.: Light detection and ranging measurements of wake dynamics. Part I: one-dimensional scanning, Wind Energy, 13, 51–61, https://doi.org/10.1002/we.352, 2010. a
Bodini, N., Zardi, D., and Lundquist, J. K.: Three-dimensional structure of wind turbine wakes as measured by scanning lidar, Atmos. Meas. Tech., 10, 2881–2896, https://doi.org/10.5194/amt-10-2881-2017, 2017. a
Bonin, T. A., Choukulkar, A., Brewer, W. A., Sandberg, S. P., Weickmann, A. M., Pichugina, Y. L., Banta, R. M., Oncley, S. P., and Wolfe, D. E.: Evaluation of turbulence measurement techniques from a single Doppler lidar, Atmos. Meas. Tech., 10, 3021–3039, https://doi.org/10.5194/amt-10-3021-2017, 2017. a
Bradley, E. F.: The influence of thermal stability and angle of incidence on the acceleration of wind up a slope, J. Wind Eng. Ind. Aerod., 15, 231–242, 1983. a
Braham, R. R.: Field Experimentation in Weather Modification, J. Am. Stat. Assoc., 74, 57–68, http://www.jstor.org/stable/2286722 (last access: 3 March 2021), 1979. a
Bromm, M., Rott, A., Beck, H., Vollmer, L., Steinfeld, G., and Kühn, M.: Field investigation on the influence of yaw misalignment on the propagation of wind turbine wakes, Wind Energy, 21, 1011–1028, https://doi.org/10.1002/we.2210, 2018. a, b, c, d
Busch, N. E. and Kristensen, L.: Cup Anemometer Overspeeding, J. Appl. Meteorol., 15, 1328–1332, https://doi.org/10.1175/1520-0450(1976)015<1328:cao>2.0.co;2, 1976. a
Buzzi, A., D Gomis, D., A., P. M., and Alonso, S.: A Method to Reduce the Adverse Impact that Inhomogeneous Station Distributions Have on Spatial Interpolation, Mon. Weather Rev., 119, 2465–2491, https://doi.org/10.1175/1520-0493(1991)119<2465:AMTRTA>2.0.CO;2, 1991. a
Caracena, F.: Analytic Approximation of Discrete Field Samples with Weighted Sums and the Gridless Computation of Field Derivatives, J. Atmos. Sci., 44, 3753–3768, https://doi.org/10.1175/1520-0469(1987)044<3753:AAODFS>2.0.CO;2, 1987. a
Caracena, F., Barnes, S. L., and Doswell III, C.: Weighting function parameters for objective interpolation of meteorological data, in: Preprints, 10th Conf. on Weather Forecasting and Analysis, Clearwater Beach, FL, 25–29 June 1984, 109–116, 1984. a
Carbajo Fuertes, F., Markfort, C. D., and Porté-Agel, F.: Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation, Remote Sens.-Basel, 10, 668, https://doi.org/10.3390/rs10050668, 2018. a
Chamorro, L. P. and Porté-Agel, F.: Effects of Thermal Stability and Incoming Boundary-Layer Flow Characteristics on Wind-Turbine Wakes: A Wind-Tunnel Study, Bound.-Lay. Meteor., 136, 515–533, https://doi.org/10.1007/s10546-010-9512-1, 2010. a
Choukulkar, A., Brewer, W. A., Sandberg, S. P., Weickmann, A., Bonin, T. A., Hardesty, R. M., Lundquist, J. K., Delgado, R., Iungo, G. V., Ashton, R., Debnath, M., Bianco, L., Wilczak, J. M., Oncley, S., and Wolfe, D.: Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign, Atmos. Meas. Tech., 10, 247–264, https://doi.org/10.5194/amt-10-247-2017, 2017. a
Ciri, U., Rotea, M., Santoni, C., and Leonardi, S.: Large-eddy simulations with extremum-seeking control for individual wind turbine power optimization, Wind Energy, 20, 1617–1634, https://doi.org/10.1002/we.2112, 2017. a
Cressman, G. P.: An Operational Objective Analysis System, Mon. Weather Rev., 87, 367–374, https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2, 1959. a
Cuerva, A. and Sanz-Andrés, A.: On sonic anemometer measurement theory, J. Wind Eng. Ind. Aerod., 88, 25–55, https://doi.org/10.1016/S0167-6105(00)00023-4, 2000. a
Cushman-Roisin, B. and Beckers, J. M.: Introduction, in: Introduction to Geophysical Fluid Dynamics, Academic Press, 7–11, 1990a. a
Cushman-Roisin, B. and Beckers, J. M.: Stratification effects, in: Introduction to Geophysical Fluid Dynamics, p. 319, Academic Press, 1990b. a
Davies, F., Middleton, D. R., and Bozier, K. E.: Urban air pollution modelling and measurements of boundary layer height, Atmos. Environ., 41, 4040–4049, https://doi.org/10.1016/j.atmosenv.2007.01.015, 2007. a
Debnath, M., Iungo, G. V., Ashton, R., Brewer, W. A., Choukulkar, A., Delgado, R., Lundquist, J. K., Shaw, W. J., Wilczak, J. M., and Wolfe, D.: Vertical profiles of the 3-D wind velocity retrieved from multiple wind lidars performing triple range-height-indicator scans, Atmos. Meas. Tech., 10, 431–444, https://doi.org/10.5194/amt-10-431-2017, 2017a. a
Debnath, M., Iungo, G. V., Brewer, W. A., Choukulkar, A., Delgado, R., Gunter, S., Lundquist, J. K., Schroeder, J. L., Wilczak, J. M., and Wolfe, D.: Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the XPIA experiment, Atmos. Meas. Tech., 10, 1215–1227, https://doi.org/10.5194/amt-10-1215-2017, 2017b. a, b
Debnath, M., Santoni, C., Leonardi, S., and Iungo, G. V.: Towards reduced order modelling for predicting the dynamics of coherent vorticity structures within wind turbine wakes, Philos. T. R. Soc. A., 375, 20160108, https://doi.org/10.1098/rsta.2016.0108, 2017c. a
Doswell, C. A.: Obtaining Meteorologically Significant Surface Divergence Fields Through the Filtering Property of Objective Analysis, Mon. Weather Rev., 105, 885–892, https://doi.org/10.1175/1520-0493(1977)105<0885:OMSSDF>2.0.CO;2, 1977. a
Duncan, Jr., J. B., Hirth, B. D., and Schroeder, J. L.: Enhanced estimation of boundary layer advective properties to improve space-to-time conversion processes for wind energy applications, Wind Energy, 22, 1203–1218, https://doi.org/10.1002/we.2350, https://doi.org/10.1002/we.2350, 2019. a
Eberhard, W. L., Cupp, R. E., and Healy, K. R.: Doppler Lidar Measurement of Profiles of Turbulence and Momentum Flux, J. Atmos. Ocean. Tech., 6, 809–819, https://doi.org/10.1175/1520-0426(1989)006<0809:DLMOPO>2.0.CO;2, 1989. a
El-Asha, S., Zhan, L., and Iungo, G. V.: Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data, Wind Energy, 20, 1823–1839, https://doi.org/10.1002/we.2123, 2017. a, b
Emeis, S.: Surface-Based Remote Sensing of the Atmospheric Boundary Layer, Springer, Berlin/Heidelberg, 2010. a
Emeis, S., Frank, H. R., and Fiedler, F.: Modification of air flow over an escarpment – Results from the Hjardemal experiment, Bound.-Lay. Meteorol., 74, 131–161, 1995. a
Endlich, R. M. and Mancuso, R. L.: Objective analysis of environmental conditions associated with severe thunderstorms and tornadoes, Mon. Weather Rev., 96, 342–350, https://doi.org/10.1126/science.27.693.594, 1968. a
ESDU: Characteristics of atmospheric turbulence near the ground. Part III: Variations in space and time for strong winds (neutral atmosphere), Engineering Sciences Data Unit, Regent Street, London, England, 1975. a
Fernando, H. J. S., Mann, J., Palma, J. M. L. M., Lundquist, J. K., Barthelmie, R. J., Belo-Pereira, M., Brown, W., Chow, F. K., and Gerz, T., e. a.: Peering into microscale details of mountain winds, Bull. Am. Meteorol. Soc., 100, 799–820, https://doi.org/10.1175/BAMS-D-17-0227.1, 2019. a
Foken, T., Göockede, M., Mauder, M., Mahrt, L., B., A., and W., M.: Post-Field Data Quality Control, in: Handbook of Micrometeorology, Atmospheric and Oceanographic Sciences Library, vol. 29, Springer, Dordrecht, 2004. a
Frehlich, R.: Effects of Wind Turbulence on Coherent Doppler Lidar Performance, J. Atmos. Ocean. Tech., 14, 54–75, https://doi.org/10.1175/1520-0426(1997)014<0054:EOWTOC>2.0.CO;2, 1997. a
Frehlich, R. and Cornman, L.: Estimating Spatial Velocity Statistics with Coherent Doppler Lidar, J. Atmos. Ocean. Tech., 19, 355–366, https://doi.org/10.1175/1520-0426-19.3.355, 2002. a
Frisch, A. S.: On the measurement of second moments of turbulent wind velocity with a single Doppler radar over non-homogeneous terrain, Bound.-Lay. Meteorol., 54, 29–39, 1991. a
Fuertes Carbajo, F. and Porté-Agel, F.: Using a Virtual Lidar Approach to Assess the Accuracy of the Volumetric Reconstruction of a Wind Turbine Wake, Remote Sens.-Basel, 10, 721, https://doi.org/10.3390/rs10050721, 2018. a, b, c
Gal-Chen, T., Xu, M., and Eberhard, W. L.: Estimations of atmospheric boundary layer fluxes and other turbulence parameters from Doppler lidar data, J. Geophys. Res.-Atmos., 97, 18409–18423, https://doi.org/10.1029/91JD03174, 1992. a, b
Garcia, E. T., Aubrun, S., Boquet, M., Royer, P., Coupiac, O., and Girard, N.: Wake meandering and its relationship with the incoming wind characteristics: a statistical approach applied to long-term on-field observations, J. Phys. Conf. Ser., 854, 012045, https://doi.org/10.1088/1742-6596/854/1/012045, 2017. a, b, c
George, W. K., Beuther, P. D., and Lumley, J. L.: Processing of random signals, Proceedings of the Dynamic Flow Conference 1978 on Dynamic Measurements in Unsteady Flows, Springer, Dordrecht, 1978. a
Gomis, D. and Alonso, S.: Diagnosis of a Cyclogenetic Event in the Western Mediterranean Using an Objective Technique for Scale Separation, Mon. Weather Rev., 118, 723–736, https://doi.org/10.1175/1520-0493(1990)118<0723:DOACEI>2.0.CO;2, 1990. a
Greenberg, M.: Advanced Engineering Mathematics, 2nd edn., Prentice Hall, Upper Saddle River, NJ, 1998. a
Halios, C. H. and Barlow, J. F.: Observations of the Morning Development of the Urban Boundary Layer Over London, UK, Taken During the ACTUAL Project, Bound.-Lay. Meteorol., 166, 395–422, https://doi.org/10.1007/s10546-017-0300-z, 2018. a
Haugen, D. A., Kaimal, J. C., and Bradley, E. F.: An experimental study of Reynolds stress and heat flux in the atmospheric surface layer, Q. J. Roy. Meteorol. Soc., 97, 168–180, https://doi.org/10.1002/qj.49709741204, 1971. a
Högström, U., Hunt, J. C. R., and Smedman, A. S.: Theory and measurements for turbulence spectra and variances in the atmospheric neutral surface layer, Bound.-Lay. Meteor., 103, 101–124, https://doi.org/10.1023/A:1014579828712, 2002. a
Holzäpfel, F., Stephan, A., Heel, T., and Körner, S.: Enhanced wake vortex decay in ground proximity triggered by plate lines, Aircr. Eng. Aerosp. Technol., 88, 206–214, https://doi.org/10.1108/AEAT-02-2015-0045, 2016. a
Huang, M., Gao, Z., Miao, S., Chen, F., LeMone, M. A., Li, J., Hu, F., and Wang, L.: Estimate of Boundary-Layer Depth Over Beijing, China, Using Doppler Lidar Data During SURF-2015, Bound.-Lay. Meteorol., 162, 503–522, https://doi.org/10.1007/s10546-016-0205-2, 2017. a
Iungo, G. V. and Porté-Agel, F.: Volumetric Lidar Scanning of Wind Turbine Wakes under Convective and Neutral Atmospheric Stability Regimes, J. Atmos. Ocean. Tech., 31, 2035–2048, https://doi.org/10.1175/JTECH-D-13-00252.1, 2014. a, b, c
Iungo, G. V., Viola, F., Camarri, S., Porté-Agel, F., and Gallaire, F.: Linear stability analysis of wind turbine wakes performed on wind tunnel measurements, J. Fluid Mech., 737, 499–526, https://doi.org/10.1017/jfm.2013.569, 2013a. a, b
Iungo, G. V., Wu, Y., and Porté-Agel, F.: Field Measurements of Wind Turbine Wakes with Lidars, J. Atmos. Ocean. Tech., 30, 274–287, https://doi.org/10.1175/JTECH-D-12-00051.1, 2013b. a, b
Iungo, G. V., Letizia, S., and Zhan, L.: Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations, J. Phys. Conf. Ser., 1037, https://doi.org/10.1088/1742-6596/1037/7/072023, 2018. a, b
Ivanell, S., Mikkelsen, R., Sørensen, J. N., and Henningson, D.: Stability analysis of the tip vortices of a wind turbine, Wind Energy, 13, 705–715, https://doi.org/10.1002/we.391, 2010. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, Technical report NREL/TP-500-38060, NREL, 1617 Cole Boulevard, Golden, CO, USA, 2009. a
Käsler, Y., S., R., Simmet, R., and Kühn, M.: Wake Measurements of a Multi-MW Wind Turbine with Coherent Long-Range Pulsed Doppler Wind Lidar, J. Atmos. Ocean. Tech., 27, 1529–1532, https://doi.org/10.1175/2010JTECHA1483.1, 2010. a
Kim, D., Kim, T., Oh, G., Huh, J., and Ko, K.: A comparison of ground-based LiDAR and met mast wind measurements for wind resource assessment over various terrain conditions, J. Wind Eng. Ind. Aerod., 158, 109–121, https://doi.org/10.1016/j.jweia.2016.09.011, 2016. a
Koch, S. E., Des Jardins, M., and Kocin, P. J.: An Interactive Barnes objective Map Analysis Scheme for Use with Satellite and Conventional Data, J. Clim. Appl. Meteorol., 22, 1487–1503, https://doi.org/10.1175/1520-0450(1983)022<1487:AIBOMA>2.0.CO;2, 1983. a, b, c
Kongara, S., Calhoun, R., Choukulkar, A., and Boldi, M. O.: Velocity retrieval for coherent Doppler lidar, Int. J. Remote Sens., 33, 3596–3613, https://doi.org/10.1080/01431161.2011.631948, 2012. a, b
Köpp, F., Rahm, S., Smalikho, I., Dolfi, A., Cariou, J. P., Harris, M., and Young, R. I.: Comparison of wake-vortex parameters measured by pulsed and continuous-wave lidars, J. Aircr., 42, 916–923, https://doi.org/10.2514/1.8177, 2005. a
Krishnamurthy, R., Choukulkar, A., Calhoun, R., Fine, J., Oliver, A., and Barr, K. S.: Coherent Doppler lidar for wind farm characterization, Wind Energy, 16, 189–206, https://doi.org/10.1002/we.539, 2013. a
Kropfli, R. A.: Single Doppler Radar Measurements of Turbulence Profiles in the Convective Boundary Layer, J. Atmos. Ocean. Tech., 3, 305–314, https://doi.org/10.1175/1520-0426(1986)003<0305:SDRMOT>2.0.CO;2, 1986. a
Kumer, V. M., Reudera, J., Svardalc, B., Sætrec, C., and Eecen, P.: Characterisation of Single Wind Turbine Wakes with Static and Scanning WINTWEX-W LiDAR Data, Energy Procedia, 80, 245–254, https://doi.org/10.1016/j.egypro.2015.11.428, 12th Deep Sea Offshore Wind R and D Conference, EERA DeepWind 2015, Trondheim, Norway, 2–5 February 2015. a, b
Kunkel, G. J. and Marusic, I.: Study of the near-wall-turbulent region of the high-Reynolds-number boundary layer using an atmospheric flow, J. Fluid Mech., 548, 375–402, https://doi.org/10.1017/S0022112005007780, 2006. a, b, c
Letizia, S. and Iungo, G. V.: LiSBOA, GitHub, available at:
https://github.com/UTD-WindFluX/LiSBOA, last access: 4 March 2021. a
Letizia, S., Zhan, L., and Iungo, G. V.: LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 2: Applications to lidar measurements of wind turbine wakes, Atmos. Meas. Tech., 14, 2095–2113, https://doi.org/10.5194/amt-14-2095-2021, 2021. a
Lhermitte, R. M.: Note on the Observation of Small-Scale Atmospheric Turbulence by Doppler Radar Techniques, Radio Sci., 4, 1241–1246, https://doi.org/10.1029/RS004i012p01241, 1969. a
Liu, H. Y., , Bo, T. L., and Liang, Y. R.: The variation of large-scale structure inclination angles in high Reynolds number atmospheric surface layers, Phys. Fluids, 29, 035104, https://doi.org/10.1063/1.4978803, 2017. a
Liu, Z., Barlow, J. F., Chan, P. W., Fung, J. C. H., Li, Y., Ren, C., Mak, H. W. L., and Ng, E.: A review of progress and applications of pulsed Doppler Wind LiDARs, Remote Sens.-Basel, 11, 1–47, https://doi.org/10.3390/rs11212522, 2019. a, b
Lundquist, J. K., Churchfield, M. J., Lee, S., and Clifton, A.: Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics, Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, 2015. a
Lundquist, J. K., Wilczak, J. M., Ashton, R., Bianco, L., Brewer, W. A., Choukulkar, A., Clifton, A., Debnath, M., Delgado, R., Friedrich, K., Gunter, S., Hamidi, A., Iungo, G. V., Kaushik, A., Kosovic, B., Langan, P., Lass, A., Lavin, E., Lee, J. C. Y., McCaffrey, K. L., Newsom, R. K., Noone, D. C., Oncley, S. P., Quelet, P. T., Sandberg, S. P., Schroeder, J. L., Shaw, W. J., Sparling, L., Martin, C. S., Pe, A. S., Strobach, E., Tay, K., Vanderwende, B. J., Weickmann, A., Wolfe, D., and Worsnop, R.: Assessing state-of-The-Art capabilities for probing the atmospheric boundary layer the XPIA field campaign, Bull. Am. Meteorol. Soc., 98, 289–314, https://doi.org/10.1175/BAMS-D-15-00151.1, 2017. a
Machefaux, E., Larsen, G. C., Troldborg, N., Hansen, K. S., Angelou, N., Mikkelsen, T., and Mann, J.: Investigation of wake interaction using full-scale lidar measurements and large eddy simulation, Wind Energy, 19, 1535–1551, https://doi.org/10.1002/we.1936, 2015. a, b, c
Machefaux, E., Larsen, G. C., Koblitz, T., Troldborg, N., Kelly, M. C., Chougule, A., Hansen, K. S., and Rodrigo, J. S.: An experimental and numerical study of the atmospheric stability impact on wind turbine wakes, Wind Energy, 19, 1785–1805, https://doi.org/10.1002/we.1950, 2016. a, b, c, d
Maddox, R. A.: An Objective Technique for Separating Macroscale and Mesoscale Features in Meteorological Data, Mon. Weather Rev., 108, 1108–1121, https://doi.org/10.1175/1520-0493(1980)108<1108:AOTFSM>2.0.CO;2, 1980. a
Mann, J., Cariou, J. P., Courtney, M., Parmentier, R., Mikkelsen, T., Wagner, R., Lindelöw, P., Sjöholm, M., and Enevoldsen, K.: Comparison of 3D turbulence measurements using three staring wind lidars and a sonic anemometer, Meteorol. Z., https://doi.org/10.1127/0941-2948/2009/0370, 2009. a
Mann, J., Peña, A., Bingöl, F., Wagner, R., and Courtney, M. S.: Lidar Scanning of Momentum Flux in and above the Atmospheric Surface Layer, J. Atmos. Ocean. Tech., 27, 959–976, https://doi.org/10.1175/2010JTECHA1389.1, 2010. a, b, c, d
Mann, J., Menke, R., Vasiljević, N., Berg, J., and Troldborg, N.: Challenges in using scanning lidars to estimate wind resources in complex terrain, J. Phys. Conf. Ser., 1037, 0–8, https://doi.org/10.1088/1742-6596/1037/7/072017, 2018. a
Manninen, A. J., O'Connor, E. J., Vakkari, V., and Petäjä, T.: A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland, Atmos. Meas. Tech., 9, 817–827, https://doi.org/10.5194/amt-9-817-2016, 2016. a
Mayor, S. D., Lenschow, D. H., Schwiesow, R. L., Mann, J., Frush, C. L., and Simon, M. K.: Validation of NCAR 10.6-µm CO2 Doppler Lidar Radial Velocity Measurements and Comparison with a 915-MHz Profiler, J. Atmos. Ocean. Tech., 14, 1110–1126, https://doi.org/10.1175/1520-0426(1997)014<1110:VONMCD>2.0.CO;2, 1997. a
Metzger, M., McKeon, B. J., and Holmes, H.: The near-neutral atmospheric surface layer: turbulence and non-stationarity, Phil. Trans. R. Soc. A, 365, 859–876, https://doi.org/10.1098/rsta.2006.1946, 2007. a
Mirocha, J. D., Rajewski, D. A., Marjanovic, N., Lundquist, J. K., Kosović, B., Draxl, C., and Churchfield, M. J.: Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model, J. Renew. Sustain. Ener., 7, 1–21, https://doi.org/10.1063/1.4928873, 2015. a
Mohr, C. and Vaughan, R.: An Economical Procedure for Cartesian Interpolation and Display of Reflectivity Factor Data in Three-Dimensional Space, J. Appl. Meteorol., 18, 661–670, 1979. a
Muñoz-Esparza, D., Cañadillas, B., Neumann, T., and Van Beeck, J.: Turbulent fluxes, stability and shear in the offshore environment: Mesoscale modelling and field observations at FINO1, J. Renew. Sustain. Ener., 4, 063136, https://doi.org/10.1063/1.4769201, 2012. a
Newsom, R., Berg, L., Shaw, W. J., and Fischer, M. L.: Turbine-scale wind field measurements using dual-Doppler lidar, Wind Energy, 18, 219–235, https://doi.org/10.1002/we.1691, 2014. a
Newsom, R. K. and Banta, R. M.: Assimilating Coherent Doppler Lidar Measurements into a Model of the Atmospheric Boundary Layer. Part I: Algorithm Development and Sensitivity to Measurement Error, J. Atmos. Ocean. Tech., 21, 1328–1345, https://doi.org/10.1175/1520-0426(2004)021<1328:ACDLMI>2.0.CO;2, 2004. a
Newsom, R. K., Calhoun, R., Ligon, D., and Allwine, J.: Linearly Organized Turbulence Structures Observed Over a Suburban Area by Dual-Doppler Lidar, Bound.-Lay. Meteorol., 127, 111–130, https://doi.org/10.1007/s10546-007-9243-0, 2008. a, b, c, d
Newsom, R. K., Brewer, W. A., Wilczak, J. M., Wolfe, D. E., Oncley, S. P., and Lundquist, J. K.: Validating precision estimates in horizontal wind measurements from a Doppler lidar, Atmos. Meas. Tech., 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, 2017. a
O'Connor, E. J., Illingworth, A. J., Brooks, I. M., Westbrook, C. D., Hogan, R. J., Davies, F., and Brooks, B. J.: A Method for Estimating the Turbulent Kinetic Energy Dissipation Rate from a Vertically Pointing Doppler Lidar and Independent Evaluation from Balloon-Borne In Situ Measurements, J. Atmos. Ocean. Tech., 27, 1652–1664, https://doi.org/10.1175/2010JTECHA1455.1, 2010. a, b, c, d, e
Orlanski, I.: A simple boundary condition for unbounded hyperbolic flows, J. Comput. Phys., 21, 251–269, https://doi.org/10.1016/0021-9991(76)90023-1, 1976. a
Pardyjak, E. R. and Stoll, R.: Improving measurement technology for the design of sustainable cities, Meas. Sci. Technol., 28, 092001, https://doi.org/10.1088/1361-6501/aa7c77, 2017. a
Pahlow, M., Parlange, M. B., and Porté-Agel, F.: On Monin–Obukhov similarity in the stable atmospheric boundary layer, Bound.-Lay. Meteorol., 99, 225–248, https://doi.org/10.1023/A, 2001. a
Pauscher, L., Vasiljevic, N., Callies, D., Lea, G., Mann, J., Klaas, T., Hieronimus, J., Gottschall, J., Schwesig, A., Kühn, M., and Courtney, M.: An inter-comparison study of multi- and DBS lidar measurements in complex terrain, Remote Sens.-Basel, 8, 782, https://doi.org/10.3390/rs8090782, 2016. a
Petersen, D. P. and Middleton, D.: Sampling and reconstruction of wave-number-limited functions in N-dimensional Euclidean spaces, Inf. Control., 5, 279–323, https://doi.org/10.1016/S0019-9958(62)90633-2, 1962. a
Puccioni, M. and Iungo, G. V.: Spectral correction of turbulent energy damping on wind LiDAR measurements due to range-gate averaging, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-27, in review, 2020. a, b, c, d
Rajewski, D. A., Takle, E. S., Lundquist, J. K., Oncley, S., Prueger, J. H., Horst, T. W., Rhodes, M. E., Pfeiffer, R., Hatfield, J. L., Spoth, K. K., and Doorenbos, R. K.: Crop wind energy experiment (CWEX): Observations of surface-layer, boundary layer and mesoscale interactions with a wind farm, Bull. Am. Meteorol. Soc., 94, 655–672, https://doi.org/10.1175/BAMS-D-11-00240.1, 2013. a
Rye, B. and Hardesty, M.: Discrete Spectral Peak Estimation in Incoherent Backscatter Heterodyne Lidar. I: Spectral Accumulation and the Cramer-Rao Lower Bound, IEEE T. Geosci. Remote, 31, 16–27, https://doi.org/10.1109/36.210440, 1993. a, b
Santoni, C., Ciri, U., Rotea, M., and Leonardi, S.: Development of a high fidelity CFD code for wind farm control, Proceedings of the American Control Conference, 1–3 July 2015, 1715–1720, https://doi.org/10.1109/ACC.2015.7170980, 2015. a
Santoni, C., Carrasquillo, K., Arenas-Navarro, I., and Leonardi, S.: Effect of tower and nacelle on the flow past a wind turbine, Wind Energy, 20, 1927–1939, https://doi.org/10.1002/we.2130, 2017. a
Sasaki, Y.: A theoretical interpretation of anisotropically weighted smoothing on the basis of numerical variational analysis, Mon. Weather Rev., 99, 698–707, 1971. a
Sathe, A. and Mann, J.: A review of turbulence measurements using ground-based wind lidars, Atmos. Meas. Tech., 6, 3147–3167, https://doi.org/10.5194/amt-6-3147-2013, 2013. a
Sathe, A., Mann, J., Gottschall, J., and Courtney, M. S.: Can Wind Lidars Measure Turbulence?, J. Atmos. Ocean. Tech., 28, 853–868, https://doi.org/10.1175/JTECH-D-10-05004.1, 2011. a, b, c, d
Schween, J. H., Hirsikko, A., Löhnert, U., and Crewell, S.: Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment, Atmos. Meas. Tech., 7, 3685–3704, https://doi.org/10.5194/amt-7-3685-2014, 2014. a
Seaman, R. S.: Tuning the Barnes Objective Analysis Parameters by Statistical Interpolation Theory, J. Atmos. Ocean. Tech., 6, 993–1000, https://doi.org/10.1175/1520-0426(1989)006<0993:TTBOAP>2.0.CO;2, 1989. a
Sekar, A. P. K., van Dooren, M. F., Mikkelsen, T., Sjöholm, M., Astrup, P., and Kühn, M.: Evaluation of the LINCOM wind field reconstruction method with simulations and full-scale measurements, J. Phys. Conf. Ser., 1037, 052 008, https://doi.org/10.1088/1742-6596/1037/5/052008, 2018. a
Shannon, C. E.: Communication in the Presence of Noise, Proceedings of the IEEE, 72, 1192–1201, https://doi.org/10.1109/PROC.1984.12998, 1984. a
Smith, D. R. and Leslie, F. W.: Error Determination of a Successive Correction Type Objective Analysis Scheme, J. Atmos. Ocean. Tech., 1, 120–130, https://doi.org/10.1175/1520-0426(1984)001<0120:EDOASC>2.0.CO;2, 1984. a, b
Smith, D. R., Pumphry, M. E., and Snow, J. T.: A Comparison of Errors in objectively Analyzed Fields for Uniform and Nonuniform Station Distributions, J. Atmos. Ocean. Tech., 3, 84–97, https://doi.org/10.1175/1520-0426(1986)003<0084:ACOEIO>2.0.CO;2, 1986. a
Stawiarski, C., Traumner, K., Knigge, C., and Calhoun, R.: Scopes and challenges of dual-Doppler lidar wind measurements-an error analysis, J. Atmos. Ocean. Tech., 30, 2044–2062, https://doi.org/10.1175/JTECH-D-12-00244.1, 2013. a, b
Stawiarski, C., Träumner, K., Kottmeier, C., Knigge, C., and Raasch, S.: Assessment of Surface-Layer Coherent Structure Detection in Dual-Doppler Lidar Data Based on Virtual Measurements, Bound.-Lay. Meteorol., 156, 371–393, https://doi.org/10.1007/s10546-015-0039-3, 2015. a, b
Stull, R. B.: Preface, in: An introduction to Boundary Layer Meteorology, p. xi, Kluwer Academic Publisher, P.O. 80x 17. 3300 AA Dordredt, the Netherlands, 1988. a
Tang, W., Chan, P. W., and Haller, G.: Lagrangian coherent structure analysis of terminal winds detected by lidar. Part I: Turbulence structures, J. Appl. Meteorol. Clim., 50, 325–338, https://doi.org/10.1175/2010JAMC2508.1, 2011. a
Taylor, P. A. and Teunissen, H. W.: The Askervin hill project: overview and background data, Bound.-Lay. Meteorol., 39, 15–39, 1987. a
Thobois, L., Cariou, J. P., and Gultepe, I.: Review of Lidar-Based Applications for Aviation Weather, Pure Appl. Geophys., 176, 1959–1976, https://doi.org/10.1007/s00024-018-2058-8, 2019. a
Trapp, R. J. and Doswell, C. A.: Radar data objective analysis, J. Atmos. Ocean. Tech., 17, 105–120, https://doi.org/10.1175/1520-0426(2000)017<0105:RDOA>2.0.CO;2, 2000. a, b, c
Trujillo, J. J., F., B., Larsen, G. C., Mann, J., and Kühn, M.: Light detection and ranging measurements of wake dynamics. Part II: two-dimensional scanning, Wind Energy, 14, 61–75, https://doi.org/10.1002/we.402, 2011. a, b, c
Trujillo, J. J., Seifert, J. K., Würth, I., Schlipf, D., and Kühn, M.: Full-field assessment of wind turbine near-wake deviation in relation to yaw misalignment, Wind Energ. Sci., 1, 41–53, https://doi.org/10.5194/wes-1-41-2016, 2016. a, b, c
Vakkari, V., O'Connor, E. J., Nisantzi, A., Mamouri, R. E., and Hadjimitsis, D. G.: Low-level mixing height detection in coastal locations with a scanning Doppler lidar, Atmos. Meas. Tech., 8, 1875–1885, https://doi.org/10.5194/amt-8-1875-2015, 2015. a
Vakkari, V., Manninen, A. J., O'Connor, E. J., Schween, J. H., van Zyl, P. G., and Marinou, E.: A novel post-processing algorithm for Halo Doppler lidars, Atmos. Meas. Tech., 12, 839–852, https://doi.org/10.5194/amt-12-839-2019, 2019. a
Van Dooren, M. F., Trabucchi, D., and Kühn, M.: A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements, Remote Sens.-Basel, 8, 809, https://doi.org/10.3390/rs8100809, 2016.
a, b
Viola, F., Iungo, G. V., Camarri, S., Porté-Agel, F., and Gallaire, F.: Prediction of the hub vortex instability in a wind turbine wake: Stability analysis with eddy-viscosity models calibrated on wind tunnel data, J. Fluid Mech., 750, R1, https://doi.org/10.1017/jfm.2014.263, 2014. a, b
Wang, H. and Barthelmie, R. J.: Wind turbine wake detection with a single Doppler wind lidar, J. Phys. Conf. Ser., 625, 012017, https://doi.org/10.1088/1742-6596/625/1/012017, 2015. a, b
Wheeler, A. J. and Ganji, A. R.: Measuring Fluid Flow Rate, Fluid Velocity, Fluid Level and Combustion Pollutants, in: Introduction to engineering experimentation, Pearson Higher Education, 1 Lake St., Upper Saddle River, New Jersey, 07458., 2010a. a
Wilson, D. A.: Doppler Radar Studies of Boundary Layer, Wind Profiles and Turbulence in Snow Conditions, Proc. 14th Conference on Radar Meteorology, Tucson, USA, p. 191–196, 1970. a
Xia, Q., Lin, C. L., Calhoun, R., and Newsom, R. K.: Retrieval of Urban Boundary Layer Structures from Doppler Lidar Data. Part I: Accuracy Assessment, J. Atmos. Sci., 65, 3–20, https://doi.org/10.1175/2007JAS2328.1, 2008. a, b
Xu, Q. and Gong, J.: Background error covariance functions for Doppler radial-wind analysis, Q. J. Roy. Meteorol. Soc., 129, 1703–1720, https://doi.org/10.1256/qj.02.129, 2002. a
Zieba, A. and Ramza, P.: Standard deviation of the mean of autocorrelated observations estimated with the use of the autocorrelation function estimated from the data, Metrol. Meas. Syst., XVIII, 529–542, https://doi.org/10.1017/cbo9780511921247.018, 2011. a
Short summary
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of velocity statistics is proposed. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field.
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and...
