Articles | Volume 15, issue 24
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-fidelity retrieval from instantaneous line-of-sight returns of nacelle-mounted lidar including supervised machine learning
Kenneth A. Brown
Wind Energy Computational Sciences, Sandia National Laboratories, Albuquerque, NM 87123, USA
Thomas G. Herges
Wind Energy Computational Sciences, Sandia National Laboratories, Albuquerque, NM 87123, USA
No articles found.
Davide Conti, Nikolay Dimitrov, Alfredo Peña, and Thomas Herges
Wind Energ. Sci., 6, 1117–1142,Short summary
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information RetrievalDual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approachHigh-resolution 3D winds derived from a modified WISSDOM synthesis scheme using multiple Doppler lidars and observationsAtmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitationsAssessing and mitigating the radar–radar interference in the German C-band weather radar networkSpectral 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 processesHorizontal small-scale variability of water vapor in the atmosphere: implications for intercomparison of data from different measuring systemsSatellite observations of gravity wave momentum flux in the mesosphere and lower thermosphere (MLT): feasibility and requirementsAn improved near-real-time precipitation retrieval for BrazilRadio frequency interference detection and mitigation in the DWD C-band weather radar networkQuality control and error assessment of the Aeolus L2B wind results from the Joint Aeolus Tropical Atlantic CampaignLong-distance propagation of 162 MHz shipping information links associated with sporadic EEstimation of refractivity uncertainties and vertical error correlations in collocated radio occultations, radiosondes, and model forecastsDeepPrecip: a deep neural network for precipitation retrievalsMachine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, SwitzerlandMeteor radar vertical wind observation biases and mathematical debiasing strategies including the 3DVAR+DIV algorithmAdaptive thermal image velocimetry of spatial wind movement on landscapes using near-target infrared camerasImage muting of mixed precipitation to improve identification of regions of heavy snow in radar dataExtending water vapor measurement capability of photon-limited differential absorption lidars through simultaneous denoising and inversionValidation of Aeolus wind profiles using ground-based lidar and radiosonde observations at La Réunion Island and the Observatoire de Haute ProvenceGPROF-NN: a neural-network-based implementation of the Goddard Profiling AlgorithmSensitivity analysis of DSD retrievals from polarimetric radar in stratiform rain based on the μ–Λ relationshipOn the use of high-frequency surface wave oceanographic research radars as bistatic single-frequency oblique ionospheric soundersA statistically optimal analysis of systematic differences between Aeolus horizontal line-of-sight winds and NOAA's Global Forecast SystemHierarchical deconvolution for incoherent scatter radar dataAn alternative cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V methodERUO: a spectral processing routine for the Micro Rain Radar PRO (MRR-PRO)On the derivation of zonal and meridional wind components from Aeolus horizontal line-of-sight windQuantification of lightning-produced NOx over the Pyrenees and the Ebro Valley by using different TROPOMI-NO2 and cloud research productsSensitivity analysis of attenuation in convective rainfall at X-band frequency using the mountain reference techniqueA new scanning scheme and flexible retrieval for mean winds and gusts from Doppler lidar measurementsAirborne measurements of directional reflectivity over the Arctic marginal sea ice zoneHigh-resolution typhoon precipitation integrations using satellite infrared observations and multisource dataContinuous temperature soundings at the stratosphere and lower mesosphere with a ground-based radiometer considering the Zeeman effectRetrieval of solar-induced chlorophyll fluorescence (SIF) from satellite measurements: comparison of SIF between TanSat and OCO-2Identification of tropical cyclones via deep convolutional neural network based on satellite cloud imagesTime evolution of temperature profiles retrieved from 13 years of infrared atmospheric sounding interferometer (IASI) data using an artificial neural networkEmissivity retrievals with FORUM's end-to-end simulator: challenges and recommendationsDetecting wave features in Doppler radial velocity radar observationsRemote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedoSnow microphysical retrieval from the NASA D3R radar during ICE-POP 2018Retrieval improvements for the ALADIN Airborne Demonstrator in support of the Aeolus wind product validationCloud-probability-based estimation of black-sky surface albedo from AVHRR dataA high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observationsDifferential absorption lidar measurements of water vapor by the High Altitude Lidar Observatory (HALO): retrieval framework and first resultsImproving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observationsCalibration of radar differential reflectivity using quasi-vertical profilesImprovement in algorithms for quality control of weather radar data (RADVOL-QC system)Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometryEvaluation of convective boundary layer height estimates using radars operating at different frequency bands
Anne-Claire Billault-Roux, Gionata Ghiggi, Louis Jaffeux, Audrey Martini, Nicolas Viltard, and Alexis Berne
Atmos. Meas. Tech., 16, 911–940,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,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,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,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,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,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.
Xavier Calbet, Cintia Carbajal Henken, Sergio DeSouza-Machado, Bomin Sun, and Tony Reale
Atmos. Meas. Tech., 15, 7105–7118,Short summary
Water vapor concentration in the atmosphere at small scales (< 6 km) is considered. The measurements show Gaussian random field behavior following Kolmogorov's theory of turbulence two-thirds law. These properties can be useful when estimating the water vapor variability within a given observed satellite scene or when different water vapor measurements have to be merged consistently.
Qiuyu Chen, Konstantin Ntokas, Björn Linder, Lukas Krasauskas, Manfred Ern, Peter Preusse, Jörn Ungermann, Erich Becker, Martin Kaufmann, and Martin Riese
Atmos. Meas. Tech., 15, 7071–7103,Short summary
Observations of phase speed and direction spectra as well as zonal mean net gravity wave momentum flux are required to understand how gravity waves reach the mesosphere–lower thermosphere and how they there interact with background flow. To this end we propose flying two CubeSats, each deploying a spatial heterodyne spectrometer for limb observation of the airglow. End-to-end simulations demonstrate that individual gravity waves are retrieved faithfully for the expected instrument performance.
Simon Pfreundschuh, Ingrid Ingemarsson, Patrick Eriksson, Daniel A. Vila, and Alan J. P. Calheiros
Atmos. Meas. Tech., 15, 6907–6933,Short summary
We used methods from the field of artificial intelligence to train an algorithm to estimate rain from satellite observations. In contrast to other methods, our algorithm not only estimates rain, but also the uncertainty of the estimate. Using independent measurements from rain gauges, we show that our method performs better than currently available methods and that the provided uncertainty estimates are reliable. Our method makes satellite-based measurements of rain more accurate and reliable.
Maximilian Schaper, Michael Frech, David Michaelis, Cornelius Hald, and Benjamin Rohrdantz
Atmos. Meas. Tech., 15, 6625–6642,Short summary
C-band weather radar data are commonly compromised by radio frequency interference (RFI) from external sources. It is not possible to separate a superimposed interference signal from the radar data. Therefore, the best course of action is to shut down RFI sources as quickly as possible. An automated RFI detection algorithm has been developed. Since its implementation, persistent RFI sources are eliminated much more quickly, while the number of short-lived RFI sources keeps steadily increasing.
Oliver Lux, Benjamin Witschas, Alexander Geiß, Christian Lemmerz, Fabian Weiler, Uwe Marksteiner, Stephan Rahm, Andreas Schäfler, and Oliver Reitebuch
Atmos. Meas. Tech., 15, 6467–6488,Short summary
We discuss the influence of different quality control schemes on the results of Aeolus wind product validation and present statistical tools for ensuring consistency and comparability among diverse validation studies with regard to the specific error characteristics of the Rayleigh-clear and Mie-cloudy winds. The developed methods are applied for the validation of Aeolus winds against an ECMWF model background and airborne wind lidar data from the Joint Aeolus Tropical Atlantic Campaign.
Alex T. Chartier, Thomas R. Hanley, and Daniel J. Emmons
Atmos. Meas. Tech., 15, 6387–6393,Short summary
This is a study of anomalous long-distance (>1000 km) radio propagation that was identified in United States Coast Guard monitors of automatic identification system (AIS) shipping transmissions at 162 MHz. Our results indicate this long-distance propagation is caused by dense sporadic E layers in the daytime ionosphere, which were observed by nearby ionosondes at the same time. This finding is surprising because it indicates these sporadic E layers may be far more dense than previously thought.
Johannes K. Nielsen, Hans Gleisner, Stig Syndergaard, and Kent B. Lauritsen
Atmos. Meas. Tech., 15, 6243–6256,Short summary
This paper provides a new way to estimate uncertainties and error correlations. The method is a generalization of a known method called the
three-cornered hat: Instead of calculating uncertainties from assumed knowledge about the observation method, uncertainties and error correlations are estimated statistically from tree independent observation series, measuring the same variable. The results are useful for future estimation of atmospheric-specific humidity from the bending of radio waves.
Fraser King, George Duffy, Lisa Milani, Christopher G. Fletcher, Claire Pettersen, and Kerstin Ebell
Atmos. Meas. Tech., 15, 6035–6050,Short summary
Under warmer global temperatures, precipitation patterns are expected to shift substantially, with critical impact on the global water-energy budget. In this work, we develop a deep learning model for predicting snow and rain accumulation based on surface radar observations of the lower atmosphere. Our model demonstrates improved skill over traditional methods and provides new insights into the regions of the atmosphere that provide the most significant contributions to high model accuracy.
Matthias Aichinger-Rosenberger, Elmar Brockmann, Laura Crocetti, Benedikt Soja, and Gregor Moeller
Atmos. Meas. Tech., 15, 5821–5839,Short summary
This study develops an innovative approach for the detection and prediction of foehn winds. The approach uses products generated from GNSS (Global Navigation Satellite Systems) in combination with machine learning-based classification algorithms to detect and predict foehn winds at Altdorf, Switzerland. Results are encouraging and comparable to similar studies using meteorological data, which might qualify the method as an additional tool for short-term foehn forecasting in the future.
Gunter Stober, Alan Liu, Alexander Kozlovsky, Zishun Qiao, Ales Kuchar, Christoph Jacobi, Chris Meek, Diego Janches, Guiping Liu, Masaki Tsutsumi, Njål Gulbrandsen, Satonori Nozawa, Mark Lester, Evgenia Belova, Johan Kero, and Nicholas Mitchell
Atmos. Meas. Tech., 15, 5769–5792,Short summary
Precise and accurate measurements of vertical winds at the mesosphere and lower thermosphere are rare. Although meteor radars have been used for decades to observe horizontal winds, their ability to derive reliable vertical wind measurements was always questioned. In this article, we provide mathematical concepts to retrieve mathematically and physically consistent solutions, which are compared to the state-of-the-art non-hydrostatic model UA-ICON.
Benjamin Schumacher, Marwan Katurji, Jiawei Zhang, Peyman Zawar-Reza, Benjamin Adams, and Matthias Zeeman
Atmos. Meas. Tech., 15, 5681–5700,Short summary
This investigation presents adaptive thermal image velocimetry (A-TIV), a newly developed algorithm to spatially measure near-surface atmospheric velocities using an infrared camera mounted on uncrewed aerial vehicles. A validation and accuracy assessment of the retrieved velocity fields shows the successful application of the algorithm over short-cut grass and turf surfaces in dry conditions. This provides new opportunities for atmospheric scientists to study surface–atmosphere interactions.
Laura M. Tomkins, Sandra E. Yuter, Matthew A. Miller, and Luke R. Allen
Atmos. Meas. Tech., 15, 5515–5525,Short summary
Locally higher radar reflectivity values in winter storms can mean more snowfall or a transition from snow to mixtures of snow, partially melted snow, and/or rain. We use the correlation coefficient to de-emphasize regions of mixed precipitation. Visual muting is valuable for analyzing and monitoring evolving weather conditions during winter storm events.
Willem J. Marais and Matthew Hayman
Atmos. Meas. Tech., 15, 5159–5180,Short summary
For atmospheric science and weather prediction, it is important to make water vapor measurements in real time. A low-cost lidar instrument has been developed by Montana State University and the National Center for Atmospheric Research. We developed an advanced signal-processing method to extend the scientific capability of the lidar instrument. With the new method we show that the maximum altitude at which the MPD can make water vapor measurements can be extended up to 8 km.
Mathieu Ratynski, Sergey Khaykin, Alain Hauchecorne, Robin Wing, Jean-Pierre Cammas, Yann Hello, and Philippe Keckhut
Aeolus is the first space-borne wind lidar providing global wind measurements since 2018. This study offers a comprehensive of 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.
Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad
Atmos. Meas. Tech., 15, 5033–5060,Short summary
The Global Precipitation Measurement mission is an international satellite mission providing regular global rain measurements. We present two newly developed machine-learning-based implementations of one of the algorithms responsible for turning the satellite observations into rain measurements. We show that replacing the current algorithm with a neural network improves the accuracy of the measurements. A neural network that also makes use of spatial information unlocks further improvements.
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 15, 4951–4969,Short summary
Knowledge of the raindrop size distribution (DSD) is crucial for understanding rainfall microphysics and quantifying uncertainty in quantitative precipitation estimates. In this study a general overview of the DSD retrieval approach from a polarimetric radar is discussed, highlighting sensitivity to potential sources of errors, either directly linked to the radar measurements or indirectly through the critical modeling assumptions behind the method such as the shape–size (μ–Λ) relationship.
Stephen R. Kaeppler, Ethan S. Miller, Daniel Cole, and Teresa Updyke
Atmos. Meas. Tech., 15, 4531–4545,Short summary
This investigation demonstrates how useful ionospheric parameters can be extracted from existing high-frequency radars that are used for oceanographic research. The methodology presented can be used by scientists and radio amateurs to understand ionospheric dynamics.
Hui Liu, Kevin Garrett, Kayo Ide, Ross N. Hoffman, and Katherine E. Lukens
Atmos. Meas. Tech., 15, 3925–3940,Short summary
A total least squares (TLS) regression is used to optimally estimate linear speed-dependent biases between Aeolus Level-2B winds and short-term (6 h) forecasts of NOAA’s FV3GFS. The winds for 1–7 September 2019 are examined. Clear speed-dependent biases for both Mie and Rayleigh winds are found, particularly in the tropics and Southern Hemisphere. Use of the TLS correction improves the forecast of the 26–28 November 2019 winter storm over the USA.
Snizhana Ross, Arttu Arjas, Ilkka I. Virtanen, Mikko J. Sillanpää, Lassi Roininen, and Andreas Hauptmann
Atmos. Meas. Tech., 15, 3843–3857,Short summary
Radar measurements of thermal fluctuations in the Earth's ionosphere produce weak signals, and tuning to specific altitudes results in suboptimal resolution for other regions, making an accurate analysis of these changes difficult. A novel approach to improve the resolution and remove measurement noise is considered. The method can capture variable characteristics, making it ideal for the study of a large range of data. Synthetically generated examples and two measured datasets were considered.
Benoît Tournadre, Benoît Gschwind, Yves-Marie Saint-Drenan, Xuemei Chen, Rodrigo Amaro E Silva, and Philippe Blanc
Atmos. Meas. Tech., 15, 3683–3704,Short summary
Solar radiation received by the Earth's surface is valuable information for various fields like the photovoltaic industry or climate research. Pictures taken from satellites can be used to estimate the solar radiation from cloud reflectivity. Two issues for a good estimation are different instrumentations and orbits. We modify a widely used method that is today only used on geostationary satellites, so it can be applied on instruments on different orbits and with different sensitivities.
Alfonso Ferrone, Anne-Claire Billault-Roux, and Alexis Berne
Atmos. Meas. Tech., 15, 3569–3592,Short summary
The Micro Rain Radar PRO (MRR-PRO) is a meteorological radar, with a relevant set of features for deployment in remote locations. We developed an algorithm, named ERUO, for the processing of its measurements of snowfall. The algorithm addresses typical issues of the raw spectral data, such as interference lines, but also improves the quality and sensitivity of the radar variables. ERUO has been evaluated over four different datasets collected in Antarctica and in the Swiss Jura.
Isabell Krisch, Neil P. Hindley, Oliver Reitebuch, and Corwin J. Wright
Atmos. Meas. Tech., 15, 3465–3479,Short summary
The Aeolus satellite measures global height resolved profiles of wind along a certain line-of-sight. However, for atmospheric dynamics research, wind measurements along the three cardinal axes are most useful. This paper presents methods to convert the measurements into zonal and meridional wind components. By combining the measurements during ascending and descending orbits, we achieve good derivation of zonal wind (equatorward of 80° latitude) and meridional wind (poleward of 70° latitude).
Francisco J. Pérez-Invernón, Heidi Huntrieser, Thilo Erbertseder, Diego Loyola, Pieter Valks, Song Liu, Dale J. Allen, Kenneth E. Pickering, Eric J. Bucsela, Patrick Jöckel, Jos van Geffen, Henk Eskes, Sergio Soler, Francisco J. Gordillo-Vázquez, and Jeff Lapierre
Atmos. Meas. Tech., 15, 3329–3351,Short summary
Lightning, one of the major sources of nitrogen oxides in the atmosphere, contributes to the tropospheric concentration of ozone and to the oxidizing capacity of the atmosphere. In this work, we contribute to improving the estimation of lightning-produced nitrogen oxides in the Ebro Valley and the Pyrenees by using two different TROPOMI products and comparing the results.
Guy Delrieu, Anil Kumar Khanal, Frédéric Cazenave, and Brice Boudevillain
Atmos. Meas. Tech., 15, 3297–3314,Short summary
The RadAlp experiment aims at improving quantitative precipitation estimation in the Alps thanks to X-band polarimetric radars and in situ measurements deployed in Grenoble, France. We revisit the physics of propagation and attenuation of microwaves in rain. We perform a generalized sensitivity analysis in order to establish useful parameterization for attenuation corrections. Originality lies in the use of otherwise undesired mountain returns for constraining the considered physical model.
Julian Steinheuer, Carola Detring, Frank Beyrich, Ulrich Löhnert, Petra Friederichs, and Stephanie Fiedler
Atmos. Meas. Tech., 15, 3243–3260,Short summary
Doppler wind lidars (DWLs) allow the determination of wind profiles with high vertical resolution and thus provide an alternative to meteorological towers. We address the question of whether wind gusts can be derived since they are short-lived phenomena. Therefore, we compare different DWL configurations and develop a new method applicable to all of them. A fast continuous scanning mode that completes a full observation cycle within 3.4 s is found to be the best-performing configuration.
Sebastian Becker, André Ehrlich, Evelyn Jäkel, Tim Carlsen, Michael Schäfer, and Manfred Wendisch
Atmos. Meas. Tech., 15, 2939–2953,Short summary
Airborne radiation measurements are used to characterize the solar directional reflection of a mixture of Arctic sea ice and open-ocean surfaces in the transition zone between both surface types. The mixture reveals reflection properties of both surface types. It is shown that the directional reflection of the mixture can be reconstructed from the directional reflection of the individual surfaces, accounting for the special conditions present in the transition zone.
You Zhao, Chao Liu, Di Di, Ziqiang Ma, and Shihao Tang
Atmos. Meas. Tech., 15, 2791–2805,Short summary
A typhoon is a high-impact atmospheric phenomenon that causes most significant socioeconomic damage, and its precipitation observation is always needed for typhoon characteristics and disaster prevention. This study developed a typhoon precipitation fusion method to combine observations from satellite radiometers, rain gauges and reanalysis to provide much improved typhoon precipitation datasets.
Witali Krochin, Francisco Navas-Guzmán, David Kuhl, Axel Murk, and Gunter Stober
Atmos. Meas. Tech., 15, 2231–2249,Short summary
This study leverages atmospheric temperature measurements performed with a ground-based radiometer making use of data that was collected during a 4-year observational campaign applying a new retrieval algorithm that improves the maximal altitude range from 45 to 55 km. The measurements are validated against two independent data sets, MERRA2 reanalysis data and the meteorological analysis of NAVGEM-HA.
Lu Yao, Yi Liu, Dongxu Yang, Zhaonan Cai, Jing Wang, Chao Lin, Naimeng Lu, Daren Lyu, Longfei Tian, Maohua Wang, Zengshan Yin, Yuquan Zheng, and Sisi Wang
Atmos. Meas. Tech., 15, 2125–2137,Short summary
A physics-based SIF retrieval algorithm, IAPCAS/SIF, is introduced and applied to OCO-2 and TanSat measurements. The strong linear relationship between OCO-2 SIF retrieved by IAPCAS/SIF and the official product indicates the algorithm's reliability. The good consistency in the spatiotemporal patterns and magnitude of the OCO-2 and TanSat SIF products suggests that the combinative usage of multi-satellite products has potential and that such work would contribute to further research.
Biao Tong, Xiangfei Sun, Jiyang Fu, Yuncheng He, and Pakwai Chan
Atmos. Meas. Tech., 15, 1829–1848,Short summary
In recent years, there has been numerous research on tropical cyclone (TC) observation based on satellite cloud images (SCIs), but most methods are limited by low efficiency and subjectivity. To overcome subjectivity and improve efficiency of traditional methods, this paper uses deep learning technology to do further research on fingerprint identification of TCs. Results provide an automatic and objective method to distinguish TCs from SCIs and are convenient for subsequent research.
Marie Bouillon, Sarah Safieddine, Simon Whitburn, Lieven Clarisse, Filipe Aires, Victor Pellet, Olivier Lezeaux, Noëlle A. Scott, Marie Doutriaux-Boucher, and Cathy Clerbaux
Atmos. Meas. Tech., 15, 1779–1793,Short summary
The IASI instruments have been observing Earth since 2007. We use a neural network to retrieve atmospheric temperatures. This new temperature data record is validated against other datasets and shows good agreement. We use this new dataset to compute trends over the 2008–2020 period. We found a warming of the troposphere, more important at the poles. In the stratosphere, we found that temperatures decrease everywhere except at the South Pole. The cooling is more pronounced at the South pole.
Maya Ben-Yami, Hilke Oetjen, Helen Brindley, William Cossich, Dulce Lajas, Tiziano Maestri, Davide Magurno, Piera Raspollini, Luca Sgheri, and Laura Warwick
Atmos. Meas. Tech., 15, 1755–1777,Short summary
Spectral emissivity is a key property of the Earth's surface. Few measurements exist in the far-infrared, despite recent work showing that its contribution is important for accurate modelling of global climate. In preparation for ESA’s EE9 FORUM mission (launch in 2026), this study takes the first steps towards the development of an operational emissivity retrieval for FORUM by investigating the sensitivity of the emissivity product to different physical and operational parameters.
Matthew A. Miller, Sandra E. Yuter, Nicole P. Hoban, Laura M. Tomkins, and Brian A. Colle
Atmos. Meas. Tech., 15, 1689–1702,Short summary
Apparent waves in the atmosphere and similar features in storm winds can be detected by taking the difference between successive Doppler weather radar scans measuring radar-relative storm air motions. Applying image filtering to the difference data better isolates the detected signal. This technique is a useful tool in weather research and forecasting since such waves can trigger or enhance precipitation.
Richard Müller and Uwe Pfeifroth
Atmos. Meas. Tech., 15, 1537–1561,Short summary
The great works of physics teach us that a central paradigm of science should be to make methods and theories as easy as possible and as complex as needed. This paper provides a brief review of remote sensing of solar surface irradiance based on this paradigm.
S. Joseph Munchak, Robert S. Schrom, Charles N. Helms, and Ali Tokay
Atmos. Meas. Tech., 15, 1439–1464,Short summary
The ability to measure snowfall with weather radar has greatly advanced with the development of techniques that utilize dual-polarization measurements, which provide information about the snow particle shape and orientation, and multi-frequency measurements, which provide information about size and density. This study combines these techniques with the NASA D3R radar, which provides dual-frequency polarimetric measurements, with data that were observed during the 2018 Winter Olympics.
Oliver Lux, Christian Lemmerz, Fabian Weiler, Uwe Marksteiner, Benjamin Witschas, Stephan Rahm, Alexander Geiß, Andreas Schäfler, and Oliver Reitebuch
Atmos. Meas. Tech., 15, 1303–1331,Short summary
The article discusses modifications in the wind retrieval of the ALADIN Airborne Demonstrator (A2D) – one of the key instruments for the validation of Aeolus. Thanks to the retrieval refinements, which are demonstrated in the context of two airborne campaigns in 2019, the systematic and random wind errors of the A2D were significantly reduced, thereby enhancing its validation capabilities. Finally, wind comparisons between A2D and Aeolus for the validation of the satellite data are presented.
Terhikki Manninen, Emmihenna Jääskeläinen, Niilo Siljamo, Aku Riihelä, and Karl-Göran Karlsson
Atmos. Meas. Tech., 15, 879–893,Short summary
A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.
Shihan Chen, Yuanjian Yang, Fei Deng, Yanhao Zhang, Duanyang Liu, Chao Liu, and Zhiqiu Gao
Atmos. Meas. Tech., 15, 735–756,Short summary
This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. The spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. The present RF model framework for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII.
Brian J. Carroll, Amin R. Nehrir, Susan A. Kooi, James E. Collins, Rory A. Barton-Grimley, Anthony Notari, David B. Harper, and Joseph Lee
Atmos. Meas. Tech., 15, 605–626,Short summary
HALO is a recently developed lidar system that demonstrates new technologies and advanced algorithms for profiling water vapor as well as aerosol and cloud properties. The high-resolution, high-accuracy measurements have unique advantages within the suite of atmospheric instrumentation, such as directly trading water vapor measurement resolution for precision. This paper provides the methodology and first water vapor results, showing agreement with in situ and spaceborne sounder measurements.
Irina V. Djalalova, David D. Turner, Laura Bianco, James M. Wilczak, James Duncan, Bianca Adler, and Daniel Gottas
Atmos. Meas. Tech., 15, 521–537,Short summary
In this paper we investigate the synergy obtained by combining active (radio acoustic sounding system – RASS) and passive (microwave radiometer) remote sensing observations to obtain temperature vertical profiles through a radiative transfer model. Inclusion of the RASS observations leads to more accurate temperature profiles from the surface to 5 km above ground, well above the maximum height of the RASS observations themselves (2000 m), when compared to the microwave radiometer used alone.
Daniel Sanchez-Rivas and Miguel A. Rico-Ramirez
Atmos. Meas. Tech., 15, 503–520,Short summary
In this work, we review the use of quasi-vertical profiles for monitoring the calibration of the radar differential reflectivity ZDR. We validate the proposed method by comparing its results against the traditional approach based on measurements taken at 90°; we observed good agreement as the errors are within 0.2 dB. Additionally, we compare the results of the proposed method with ZDR derived from disdrometers; the errors are reasonable considering factors discussed in the paper.
Katarzyna Ośródka and Jan Szturc
Atmos. Meas. Tech., 15, 261–277,Short summary
Weather radar data are used in weather monitoring and forecasting, but they are affected by numerous errors and require advanced corrections. Different systems are designed and implemented to suit specific local conditions, like the RADVOL-QC system. The radar errors are divided into several groups: disturbance by non-meteorological echoes (from the mountains, RLAN signals, wind turbines, etc.), beam blockage, attenuation, etc. Each of them has different properties and is corrected differently.
Xingou Xu and Ad Stoffelen
Atmos. Meas. Tech., 14, 7435–7451,Short summary
The support vector machine can effectively represent the increasing effect of rain affecting wind speeds. This research provides a correction of deviations that are skew- to Gaussian-like features caused by rain in Ku-band scatterometer wind. It demonstrates the effectiveness of a machine learning method when used based on elaborate analysis of the model establishment and result validation procedures. The corrected winds provide information previously lacking, which is vital for nowcasting.
Anna Franck, Dmitri Moisseev, Ville Vakkari, Matti Leskinen, Janne Lampilahti, Veli-Matti Kerminen, and Ewan O'Connor
Atmos. Meas. Tech., 14, 7341–7353,Short summary
We proposed a method to derive a convective boundary layer height, using insects in radar observations, and we investigated the consistency of these retrievals among different radar frequencies (5, 35 and 94 GHz). This method can be applied to radars at other measurement stations and serve as additional way to estimate the boundary layer height during summer. The entrainment zone was also observed by the 5 GHz radar above the boundary layer in the form of a Bragg scatter layer.
A2e (Atmosphere to Elections) Data Archive and Portal, U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Wake Steering Experiment, https://a2e.energy.gov/about/dap# (last access: 22 September 2020), 2019.
Albers, A., Janssen, A., and Mander, J.: German Test Station for Remote Wind Sensing Devices, EWEC, Marseille, https://www.researchgate.net/profile/Axel_Albers/publication/237616810_German_Test_Station_for_Remote_Wind_Sensing_Devices/links/568e2aee08ae78cc0514b121.pdf (last access: 19 September 2020), 2009.
Angelou, N., Abari, F. F., Mann, J., Mikkelsen, T., and Sjöholm, M.: Challenges in noise removal from Doppler spectra acquired by a continuous-wave lidar, Proc. 26th Int. Laser Radar Conf., Porto Heli, Greece, 10 pp., 2012.
Beck, H. and Kühn, M.: Dynamic data filtering of long-range Doppler LiDAR wind speed measurements, Remote Sens., 9, 561, https://doi.org/10.3390/rs9060561, 2017.
Benedict, L. and Gould, R.: Towards better uncertainty estimates for turbulence statistics, Exp. Fluids, 22, 129–136, https://doi.org/10.1007/s003480050030, 1996.
Branlard, E., Pedersen, A. T., Mann, J., Angelou, N., Fischer, A., Mikkelsen, T., Harris, M., Slinger, C., and Montes, B. F.: Retrieving wind statistics from average spectrum of continuous-wave lidar, Atmos. Meas. Tech., 6, 1673–1683, https://doi.org/10.5194/amt-6-1673-2013, 2013.
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, https://doi.org/10.1007/BF00058655, 1996.
Brown, K. and Herges, T.: Residual uncertainty in processed line-of-sight returns from nacelle-mounted lidar due to spectral artifacts, J. Phys. Conference Series, 1618, 032052, https://doi.org/10.1088/1742-6596/1618/3/032052, 2020.
Brown, K., Hsieh, A., Herges, T., and Maniaci, D.: Representation of coherent structures and turbulence spectra from a virtual SpinnerLidar for future LES wake validation, J. Physics: Conference Series, 1618, 062070, https://doi.org/10.1088/1742-6596/1618/6/062070, 2020.
Cariou, J., Thobois, L., Parmentier, R., Boquet, M., and Loaec, S.: Assessing the metrological capabilities of Wind Doppler Lidars, in: Proceedings of the CLRC, Barcelona, Spain, 17–20 June 2013.
Commission, I. E.: Wind turbines-Part 12-1: Power performance measurements of electricity producing wind turbines, International Electrotechnical Commission, IEC 61400-12-1, 1–90, ISBN 2-8318-8333-4, 2005.
Courtney, M., Wagner, R., and Lindelöw, P.: Testing and comparison of lidars for profile and turbulence measurements in wind energy, IOP Conference Series: Earth and Environmental Science, 1, 012021, https://doi.org/10.1088/1755-1315/1/1/012021, 2008.
Debnath, M., Doubrawa, P., Herges, T., Martínez-Tossas, L., Maniaci, D., and Moriarty, P.: Evaluation of wind speed retrieval from continuous-wave lidar measurements of a wind turbine wake using virtual lidar techniques, J. Physics: Conference Series, 1256, 012008, https://doi.org/10.1088/1742-6596/1256/1/012008, 2019.
Doubrawa, P., Quon, E. W., Martinez-Tossas, L. A., Shaler, K., Debnath, M., Hamilton, N., Herges, T. G., Maniaci, D., Kelley, C. L., and Hsieh, A. S.: Multimodel validation of single wakes in neutral and stratified atmospheric conditions, Wind Energ., 23, 2027–2055, https://doi.org/10.1002/we.2543, 2020.
Forsting, A. M. and Troldborg, N.: A finite difference approach to despiking in-stationary velocity data-tested on a triple-lidar, J. Physics: Conference Series, 753, 072017, https://doi.org/10.1088/1742-6596/753/7/072017, 2016.
Forsting, A. R. M., Troldborg, N., and Borraccino, A.: Modelling lidar volume-averaging and its significance to wind turbine wake measurements, J. Physics: Conference Series, 854, 012014, https://doi.org/10.1088/1742-6596/854/1/012014, 2017.
Frehlich, R.: Simulation of coherent Doppler lidar performance in the weak-signal regime, J. Atmos. Ocean. Technol., 13, 646–658, https://doi.org/10.1175/1520-0426(1996)013<0646:SOCDLP>2.0.CO;2, 1996.
Garber, D. P.: On the use of the noncentral chi-square density function for the distribution of helicopter spectral estimates, NASA Langley Research Center NASA Contractor Report 191546, 44 pp., https://ntrs.nasa.gov/citations/19940011014 (last access: 9 October 2021), 1993.
Geman, S., Bienenstock, E., and Doursat, R.: Neural networks and the bias/variance dilemma, Neural Comput., 4, 1–58, https://doi.org/10.1162/neco.19188.8.131.52, 1992.
Giyanani, A., Bierbooms, W., and van Bussel, G.: Lidar uncertainty and beam averaging correction, Adv. Sci. Res., 12, 85–89, https://doi.org/10.5194/asr-12-85-2015, 2015.
Godwin, K., De Wekker, S., and Emmitt, G.: Retrieving winds in the surface layer over land using an airborne Doppler lidar, J. Atmos. Ocean. Technol., 29, 487–499, https://doi.org/10.1175/JTECH-D-11-00139.1, 2012.
Gottschall, J., Courtney, M. S., Wagner, R., Jørgensen, H. E., and Antoniou, I.: Lidar profilers in the context of wind energy-a verification procedure for traceable measurements, Wind Energ., 15, 147–159, https://doi.org/10.1002/we.518, 2012.
Gryning, S.-E., Floors, R., Peña, A., Batchvarova, E., and Brümmer, B.: Weibull wind-speed distribution parameters derived from a combination of wind-lidar and tall-mast measurements over land, coastal and marine sites, Bound.-Lay. Meteorol., 159, 329–348, https://doi.org/10.1007/s10546-015-0113-x, 2016.
Harris, M., Hand, M., and Wright, A.: Lidar for turbine control, National Renewable Energy Laboratory, Golden, CO, Report No. NREL/TP-500-39154, 1–47, 2006.
Hasager, C., Stein, D., Courtney, M., Peña, A., Mikkelsen, T., Stickland, M., and Oldroyd, A.: Hub height ocean winds over the North Sea observed by the NORSEWInD lidar array: measuring techniques, quality control and data management, Remote Sens., 5, 4280–4303, https://doi.org/10.3390/rs5094280, 2013.
Held, D. P. and Mann, J.: Comparison of methods to derive radial wind speed from a continuous-wave coherent lidar Doppler spectrum, Atmos. Meas. Tech., 11, 6339–6350, https://doi.org/10.5194/amt-11-6339-2018, 2018.
Herges, T. and Keyantuo, P.: Robust Lidar Data Processing and Quality Control Methods Developed for the SWiFT Wake Steering Experiment, J. Physics: Conference Series, 1256, 012005, https://doi.org/10.1088/1742-6596/1256/1/012005, 2019.
Herges, T. G., Maniaci, D. C., Naughton, B., Hansen, K., Sjoholm, M., Angelou, N., and Mikkelsen, T.: Scanning Lidar Spatial Calibration and Alignment Method for Wind Turbine Wake Characterization, 35th Wind Energy Symposium, Grapevine, Texas, 9–13 January 2017, 1–10, https://doi.org/10.2514/6.2017-0455, 2017.
Hoaglin, D. C., Mosteller, F., and Tukey, J. W.: Understanding robust and exploratory data analysis, J. Roy. Stat. Soc. Series D (The Statistician), 33, 320–321, https://doi.org/10.2307/2988240, 1984.
Hojstrup, J.: A statistical data screening procedure, Measure. Sci. Technol., 4, 153, https://doi.org/10.1088/0957-0233/4/2/003, 1993.
Hsieh, A. S., Brown, K. A., deVelder, N. B., Herges, T. G., Knaus, R. C., Sakievich, P. J., Cheung, L. C., Houchens, B. C., Blaylock, M. L., and Maniaci, D. C.: High-Fidelity Wind Farm Simulation Methodology with Experimental Validation, J. Wind Eng. Indust. Aerodynam., 218, 1–18, https://doi.org/10.1016/j.jweia.2021.104754, 2021.
Kelley, C. L. and Ennis, B. L.: SWiFT site atmospheric characterization, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), https://doi.org/10.2172/1237403, 2016.
Kelley, C. L., Herges, T. G., Martinez, L. A., and Mikkelsen, T.: Wind turbine aerodynamic measurements using a scanning lidar, J. Phys. Conference Series, 1037, 052014, https://doi.org/10.1088/1742-6596/1037/5/052014, 2018.
Lindelöw-Marsden, P.: Upwind D1. Uncertainties in wind assessment with LIDAR, Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Roskilde, Denmark, Risø-R-1681(EN), 1–54, 2009.
Lindelöw, P.: Fiber based coherent lidars for remote wind sensing, Ørsted, DTU, Technical University of Denmark, 187 pp., 2007.
Liu, Z., Hunt, W., Vaughan, M., Hostetler, C., McGill, M., Powell, K., Winker, D., and Hu, Y.: Estimating random errors due to shot noise in backscatter lidar observations, Appl. Optics, 45, 4437–4447, https://doi.org/10.1364/AO.45.004437, 2006.
Lombard, L., Valla, M., Canat, G., and Dolfi-Bouteyre, A.: Performance of Frequency Estimators for real time display of high PRF pulsed fibered Lidar wind map, CLRC 18th Coherent Laser Radar, Boulder, United States, June 2016.
Mikkelsen, T., Angelou, N., Hansen, K., Sjöholm, M., Harris, M., Slinger, C., Hadley, P., Scullion, R., Ellis, G., and Vives, G.: A spinner-integrated wind lidar for enhanced wind turbine control, Wind Energ., 16, 625–643, https://doi.org/10.1002/we.1564, 2013.
Mudholkar, G. S. and Hutson, A. D.: The epsilon–skew–normal distribution for analyzing near-normal data, J. Stat. Plann. Infer., 83, 291–309, https://doi.org/10.1016/S0378-3758(99)00096-8, 2000.
Newman, J. F., Klein, P. M., Wharton, S., Sathe, A., Bonin, T. A., Chilson, P. B., and Muschinski, A.: Evaluation of three lidar scanning strategies for turbulence measurements, Atmos. Meas. Tech., 9, 1993–2013, https://doi.org/10.5194/amt-9-1993-2016, 2016.
Pedersen, A. T. and Courtney, M.: Flywheel calibration of a continuous-wave coherent Doppler wind lidar, Atmos. Meas. Tech., 14, 889–903, https://doi.org/10.5194/amt-14-889-2021, 2021.
Pe˜na, A., Hasager, C. B., Lange, J., Anger, J., Badger, M., Bingöl, F., Bischoff, O., Cariou, J.-P., Dunne, F., Emeis, S., Harris, M., Hofsäss, M., Karagali, I., Laks, J., Larsen, S., Mann, J., Mikkelsen, T., Pao, L. Y., Pitter, M., Rettenmeier, A., Sathe, A., Scanzani, F., Schlipf, D., Simley, E., Slinger, C., Wagner, R., and Würth, I.: Remote Sensing for Wind Energy, Technical University of Denmark, DTU Wind Energy, DTU Wind EnergyE-Report-0029(EN), 2013.
Peña, A., Mann, J., and Dimitrov, N.: Turbulence characterization from a forward-looking nacelle lidar, Wind Energ. Sci., 2, 133–152, https://doi.org/10.5194/wes-2-133-2017, 2017.
Phelippeau, H., Talbot, H., Akil, M., and Bara, S.: Shot noise adaptive bilateral filter, 2008 9th International Conference on Signal Processing, Beijing, China, 26–29 October 2008, 864–867, https://doi.org/10.1109/ICOSP.2008.4697265, 2008.
Rye, B. J. and Hardesty, R.: 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.
Sekar, A. P. K., Rott, A., van Dooren, M. F., and Kühn, M.: How much flow information can a turbine-mounted lidar capture?, J. Physics Conference Series, 1618, 032050, https://doi.org/10.1088/1742-6596/1618/3/032050, 2020.
Simley, E., Fürst, H., Haizmann, F., and Schlipf, D.: Optimizing Lidars for wind turbine control applications – Results from the IEA wind task 32 Workshop, Remote Sens., 10, 863, https://doi.org/10.3390/rs10060863, 2018.
Simley, E., Pao, L. Y., Frehlich, R., Jonkman, B., and Kelley, N.: Analysis of light detection and ranging wind speed measurements for wind turbine control, Wind Energy, 17, 413–433, https://doi.org/10.1002/we.1584, 2014.
Sjöholm, M., Pedersen, A. T., Angelou, N., Abari, F. F., Mikkelsen, T., Harris, M., Slinger, C., and Kapp, S.: Full two-dimensional rotor plane inflow measurements by a spinner-integrated wind lidar, European Wind Energy Conference & Exhibition, Barcelona, Spain, 4–7 February 2013.
Slinger, C. and Harris, M.: Introduction to continuous-wave Doppler lidar, Summer School in Remote sensing for Wind Energy, Boulder, USA, 2012.
Smith, D. A., Harris, M., Coffey, A. S., Mikkelsen, T., Jørgensen, H. E., Mann, J., and Danielian, R.: Wind lidar evaluation at the Danish wind test site in Høvsøre, Wind Energ., 9, 87–93, https://doi.org/10.1002/we.193, 2006.
Stawiarski, C., Träumner, K., Knigge, C., and Calhoun, R.: Scopes and Challenges of Dual-Doppler Lidar Wind Measurements – An Error Analysis, J. Atmos. Ocean. Technol., 30, 2044–2062, https://doi.org/10.1175/jtech-d-12-00244.1, 2013.
Tibshirani, R.: A comparison of some error estimates for neural network models, Neural Comput., 8, 152–163, https://doi.org/10.1162/neco.19184.108.40.206, 1996.
van Dooren, M. F.: Doppler Lidar Inflow Measurements, Handbook of Wind Energ. Aerodynam., 1–34, https://doi.org/10.1007/978-3-030-05455-7_35-1, 2021.
van Dooren, M. F., Kidambi Sekar, A. P., Neuhaus, L., Mikkelsen, T., Hölling, M., and Kühn, M.: Modelling the spectral shape of continuous-wave lidar measurements in a turbulent wind tunnel, Atmos. Meas. Tech., 15, 1355–1372, https://doi.org/10.5194/amt-15-1355-2022, 2022.
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O., Clifton, A., Green, J., Green, P., and Holttinen, H.: Grand challenges in the science of wind energy, Science, 366, eaau2027, https://doi.org/10.1126/science.aau2027, 2019.
Vickers, D. and Mahrt, L.: Quality control and flux sampling problems for tower and aircraft data, J. Atmos. Ocean. Technol.y, 14, 512–526, https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2, 1997.
Wagner, R. and Bejdic, J.: Windcube+ FCR test at Hrgud, Bosnia and Herzegovina, Final report, DTU Wind Energy E-0039TS27, ISBN 978-87-92896-66-7, 2014.
Wang, H., Barthelmie, R. J., Clifton, A., and Pryor, S. C.: Wind Measurements from Arc Scans with Doppler Wind Lidar, J. Atmos. Ocean. Technol., 32, 2024–2040, https://doi.org/10.1175/jtech-d-14-00059.1, 2015.
Wang, H., Barthelmie, R. J., Pryor, S. C., and Brown, Gareth.: Lidar arc scan uncertainty reduction through scanning geometry optimization, Atmos. Meas. Tech., 9, 1653–1669, https://doi.org/10.5194/amt-9-1653-2016, 2016.
Yang, J., Zhou, K., Li, Y., and Liu, Z.: Generalized out-of-distribution detection: A survey, arXiv [preprint], https://doi.org/10.48550/arXiv.2110.11334, 2021.
- Full-text XML
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.
The character of the airflow around and within wind farms has a significant impact on the energy...