Articles | Volume 15, issue 1
Research article 03 Jan 2022
Research article | 03 Jan 2022
Wind speed and direction estimation from wave spectra using deep learning
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information RetrievalOptions to correct local turbulent flux measurements for large-scale fluxes using an approach based on large-eddy simulationGlobal ensemble of temperatures over 1850–2018: quantification of uncertainties in observations, coverage, and spatial modeling (GETQUOCS)Reconstruction of the mass and geometry of snowfall particles from multi-angle snowflake camera (MASC) imagesA new zenith hydrostatic delay model for real-time retrievals of GNSS-PWVAir temperature equation derived from sonic temperature and water vapor mixing ratio for air flow sampled through closed-path eddy-covariance flux systemsSampling error in aircraft flux measurements based on a high-resolution large eddy simulation of the marine boundary layerSeparation of convective and stratiform precipitation using polarimetric radar data with a support vector machine methodAn approach to minimize aircraft motion bias in multi-hole probe wind measurements made by small unmanned aerial systemsInterpolation uncertainty of atmospheric temperature profilesUnsupervised classification of snowflake images using a generative adversarial network and K-medoids classificationAn improved post-processing technique for automatic precipitation gauge time seriesRetrieval of eddy dissipation rate from derived equivalent vertical gust included in Aircraft Meteorological Data Relay (AMDAR)Atmospheric condition identification in multivariate data through a metric for total variationIdentifying persistent temperature inversion events in a subalpine basin using radon-222Evaluation of wake influence on high-resolution balloon-sonde measurementsImproving the mean and uncertainty of ultraviolet multi-filter rotating shadowband radiometer in situ calibration factors: utilizing Gaussian process regression with a new method to estimate dynamic input uncertaintyEmpirical high-resolution wind field and gust model in mountainous and hilly terrain based on the dense WegenerNet station networksPerformance of the FMI cosine error correction method for the Brewer spectral UV measurementsComputational efficiency for the surface renewal methodRaindrop fall velocities from an optical array probe and 2-D video disdrometerNovel approaches to estimating the turbulent kinetic energy dissipation rate from low- and moderate-resolution velocity fluctuation time seriesSmoothing data series by means of cubic splines: quality of approximation and introduction of a repeating spline approachData-driven clustering of rain events: microphysics information derived from macro-scale observationsSolid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake CameraDust opacities inside the dust devil column in the Taklimakan DesertComparison of GPS tropospheric delays derived from two consecutive EPN reprocessing campaigns from the point of view of climate monitoringEnsemble mean density and its connection to other microphysical properties of falling snow as observed in Southern FinlandAn automated nowcasting model of significant instability events in the flight terminal area of Rio de Janeiro, BrazilRetrieving atmospheric turbulence information from regular commercial aircraft using Mode-S and ADS-BAn automatic precipitation-phase distinction algorithm for optical disdrometer data over the global oceanCalibration methods for rotating shadowband irradiometers and optimizing the calibration durationError estimation for localized signal properties: application to atmospheric mixing height retrievalsAltitude misestimation caused by the Vaisala RS80 pressure bias and its impact on meteorological profilesA study of turbulent fluxes and their measurement errors for different wind regimes over the tropical Zongo Glacier (16° S) during the dry seasonMethodology for determining multilayered temperature inversionsTechniques for analyses of trends in GRUAN dataThe Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysisAdaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observationsCorrection of raindrop size distributions measured by Parsivel disdrometers, using a two-dimensional video disdrometer as a referenceUsing digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradianceReference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosondeReconstruction of global solar radiation time series from 1933 to 2013 at the Izaña Atmospheric ObservatoryHydrometeor classification from two-dimensional video disdrometer dataFunctional derivatives applied to error propagation of uncertainties in topography to large-aperture scintillometer-derived heat fluxesValidation of spectral sky radiance derived from all-sky camera images – a case studyThe response of superpressure balloons to gravity wave motionsCharacterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivityImplementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial resultsOn the effect of moisture on the detection of tropospheric turbulence from in situ measurementsNote on the application of planar-fit rotation for non-omnidirectional sonic anemometers
Matthias Mauder, Andreas Ibrom, Luise Wanner, Frederik De Roo, Peter Brugger, Ralf Kiese, and Kim Pilegaard
Atmos. Meas. Tech., 14, 7835–7850,Short summary
Turbulent flux measurements suffer from a general systematic underestimation. One reason for this bias is non-local transport by large-scale circulations. A recently developed model for this additional transport of sensible and latent energy is evaluated for three different test sites. Different options on how to apply this correction are presented, and the results are evaluated against independent measurements.
Maryam Ilyas, Douglas Nychka, Chris Brierley, and Serge Guillas
Atmos. Meas. Tech., 14, 7103–7121,Short summary
Instrumental temperature records are fundamental to climate science. There are spatial gaps in the distribution of these measurements across the globe. This lack of spatial coverage introduces coverage error. In this research, a methodology is developed and used to quantify the coverage errors. It results in a data product that, for the first time, provides a full description of both the spatial coverage uncertainties along with the uncertainties in the modeling of these spatial gaps.
Jussi Leinonen, Jacopo Grazioli, and Alexis Berne
Atmos. Meas. Tech., 14, 6851–6866,Short summary
Measuring the shape, size and mass of a large number of snowflakes is a challenging task; it is hard to achieve in an automatic and instrumented manner. We present a method to retrieve these properties of individual snowflakes using as input a triplet of images/pictures automatically collected by a multi-angle snowflake camera (MASC) instrument. Our method, based on machine learning, is trained on artificially generated snowflakes and evaluated on 3D-printed snowflake replicas.
Longjiang Li, Suqin Wu, Kefei Zhang, Xiaoming Wang, Wang Li, Zhen Shen, Dantong Zhu, Qimin He, and Moufeng Wan
Atmos. Meas. Tech., 14, 6379–6394,Short summary
The zenith hydrostatic delay (ZHD) derived from blind models are of low accuracy, especially in mid- and high-latitude regions. To address this issue, the ratio of the ZHD to zenith total delay (ZTD) is firstly investigated; then, based on the relationship between the ZHD and ZTD, a new ZHD model was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The model outperforms blind models.
Xinhua Zhou, Tian Gao, Eugene S. Takle, Xiaojie Zhen, Andrew E. Suyker, Tala Awada, Jane Okalebo, and Jiaojun Zhu
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
Air temperature from sonic temperature and air moisture has been used without an exact equation. We present an exact equation of such air temperature for close-path eddy-covariance flux measurements. Air temperature from this equation is equivalent to sonic temperature in accuracy and frequency-response. It is a choice for advanced flux topics because, with this equation, thermodynamic variables in the flux measurements can be temporally synchronized and spatially matched at measurement scales.
Grant W. Petty
Atmos. Meas. Tech., 14, 1959–1976,Short summary
Aircraft measurements of turbulent fluxes of matter and energy are important in field investigations of the interaction of the Earth's surface and the atmosphere. Because these measurements are of randomly fluctuating quantities, averages must be taken over longer flight tracks to reduce uncertainty. This paper investigates the relationship between track length and measurement error using a computer model simulation of a marine environment and compares the results with published theory.
Yadong Wang, Lin Tang, Pao-Liang Chang, and Yu-Shuang Tang
Atmos. Meas. Tech., 14, 185–197,Short summary
The motivation of this work is to develop a precipitation separation approach that can be implemented on those radars with fast scanning schemes. In these schemes, the higher tilt radar data are not available, which poses a challenge for the traditional approaches. This approach uses artificial intelligence, which integrates polarimetric radar variables. The quantitative precipitation estimation will benefit from the output of this algorithm.
Loiy Al-Ghussain and Sean C. C. Bailey
Atmos. Meas. Tech., 14, 173–184,Short summary
Unmanned aerial vehicles equipped with multi-hole probes are an effective approach to measure the wind vector with high spatial and temporal resolution. However, the aircraft motion must be removed from the measured signal first, a process often introducing bias due to small errors in the relative orientation of coordinates. We present an approach that has successfully been applied in post-processing, which was found to minimize the influence of aircraft motion on wind measurements.
Alessandro Fassò, Michael Sommer, and Christoph von Rohden
Atmos. Meas. Tech., 13, 6445–6458,Short summary
Modern radiosonde balloons fly from ground level up to the lower stratosphere and take temperature measurements. What is the uncertainty of interpolated values in the resulting atmospheric temperature profiles? To answer this question, we introduce a general statistical–mathematical model for the computation of interpolation uncertainty. Analysing more than 51 million measurements, we provide some understanding of the consequences of filling missing data with interpolated ones.
Jussi Leinonen and Alexis Berne
Atmos. Meas. Tech., 13, 2949–2964,Short summary
The appearance of snowflakes provides a signature of the atmospheric processes that created them. To get this information from large numbers of snowflake images, automated analysis using computer image recognition is needed. In this work, we use a neural network that learns the structure of the snowflake images to divide a snowflake dataset into classes corresponding to different sizes and structures. Unlike with most comparable methods, only minimal input from a human expert is needed.
Amber Ross, Craig D. Smith, and Alan Barr
Atmos. Meas. Tech., 13, 2979–2994,Short summary
The raw data derived from most automated accumulating precipitation gauges often suffer from non-precipitation-related fluctuations in the measurement of the gauge bucket weights from which the precipitation amount is determined. This noise can be caused by electrical interference, mechanical noise, and evaporation. This paper presents an automated filtering technique that builds on the principle of iteratively balancing noise to produce a clean precipitation time series.
Soo-Hyun Kim, Hye-Yeong Chun, Jung-Hoon Kim, Robert D. Sharman, and Matt Strahan
Atmos. Meas. Tech., 13, 1373–1385,Short summary
We retrieve the eddy dissipation rate (EDR) from the derived equivalent vertical gust included in the Aircraft Meteorological Data Relay data for more reliable and consistent observations of aviation turbulence globally with the single preferred EDR metric. We convert the DEVG to the EDR using two methods (lognormal mapping scheme and best-fit curve between EDR and DEVG), and the DEVG-derived EDRs are evaluated against in situ EDR data reported by US-operated carriers.
Atmos. Meas. Tech., 13, 1019–1032,Short summary
The identification of atmospheric conditions within a multivariable atmospheric data set is an important step in validating emerging and existing models used to simulate wind plant flows and operational strategies. The total variation approach developed here offers a method founded in tested mathematical metrics and can be used to identify and characterize periods corresponding to quiescent conditions or specific events of interest for study or wind energy development.
Dafina Kikaj, Janja Vaupotič, and Scott D. Chambers
Atmos. Meas. Tech., 12, 4455–4477,Short summary
A new method was developed to identify persistent temperature inversion events in a subalpine basin using a radon-based method (RBM). By comparing with an existing pseudo-vertical temperature gradient method, the RBM was shown to be more reliable and seasonally independent. The RBM has the potential to increase the understanding of meteorological controls on air pollution episodes in complex terrain beyond the capability of contemporary atmospheric stability classification tools.
Jens Söder, Michael Gerding, Andreas Schneider, Andreas Dörnbrack, Henrike Wilms, Johannes Wagner, and Franz-Josef Lübken
Atmos. Meas. Tech., 12, 4191–4210,Short summary
Atmospheric measurements on rising balloons can be compromised by the balloon's wake. The aim of this study is to provide a tool for assessing the likelihood of encountering the balloon's wake at the position of the gondola. This includes an uncertainty analysis of the calculation and a retrieval of vertical winds. We find an average wake encounter probability of 28 % for a standard radiosonde. Additionally, we evaluate the influence of wake from smaller objects on turbulence measurements.
Maosi Chen, Zhibin Sun, John M. Davis, Yan-An Liu, Chelsea A. Corr, and Wei Gao
Atmos. Meas. Tech., 12, 935–953,Short summary
Combining a new dynamic uncertainty estimation method with Gaussian process regression (GP), we provide a generic and robust solution to estimate the underlying mean and uncertainty functions of time series with variable mean, noise, sampling density, and length of gaps. The GP solution was applied and validated on three UV-MFRSR Vo time series at three ground sites with improved accuracy of the smoothed time series in terms of aerosol optical depth compared with two other smoothing methods.
Christoph Schlager, Gottfried Kirchengast, and Juergen Fuchsberger
Atmos. Meas. Tech., 11, 5607–5627,Short summary
In this work we further developed and evaluated an operational weather diagnostic application, the WegenerNet Wind Product Generator (WPG), and applied it to the WegenerNet Johnsbachtal (JBT), a dense meteorological station network located in a mountainous Alpine region. The WPG automatically generates gridded high-resolution wind fields in near-real time with a temporal resolution of 30 min and a spatial resolution of 100 m x 100 m.
Kaisa Lakkala, Antti Arola, Julian Gröbner, Sergio Fabian León-Luis, Alberto Redondas, Stelios Kazadzis, Tomi Karppinen, Juha Matti Karhu, Luca Egli, Anu Heikkilä, Tapani Koskela, Antonio Serrano, and José Manuel Vilaplana
Atmos. Meas. Tech., 11, 5167–5180,Short summary
The performance of the cosine error correction method for correcting spectral UV measurements of the Brewer spectroradiometer was studied. The correction depends on the sky radiation distribution, which can change during one spectral scan. The results showed that the correction varied between 4 and 14 %, and that the relative differences between the reference and the Brewer diminished by 10 %. The method is applicable to other instruments as long as the required input parameters are available.
Jason Kelley and Chad Higgins
Atmos. Meas. Tech., 11, 2151–2158,Short summary
Measuring fluxes of energy and trace gases using the surface renewal (SR) method can be economical and robust, but it requires computationally intensive calculations. Several new algorithms were written to perform the required calculations more efficiently and rapidly, and were tested with field data and computationally rigorous SR methods. These efficient algorithms facilitate expanded use of SR in atmospheric experiments, for applied monitoring, and in novel field implementations.
Viswanathan Bringi, Merhala Thurai, and Darrel Baumgardner
Atmos. Meas. Tech., 11, 1377–1384,Short summary
Raindrop fall velocities are important for rain rate estimation, soil erosion studies and in numerical modelling of rain formation in clouds. The assumption that the fall velocity is uniquely related to drop size is made inherently based on laboratory measurements under still air conditions from nearly 68 years ago. There have been very few measurements of drop fall speeds in natural rain under both still and turbulent wind conditions. We report on fall speed measurements in natural rain shafts.
Marta Wacławczyk, Yong-Feng Ma, Jacek M. Kopeć, and Szymon P. Malinowski
Atmos. Meas. Tech., 10, 4573–4585,Short summary
We propose two novel methods to estimate turbulent kinetic energy dissipation rate applicable to airborne measurements. In this way we increase robustness of the dissipation rate retrieval and extend its applicability to a wider range of data sets. The new approaches relate the predicted form of the dissipation spectrum to the mean of zero crossings of the measured velocity fluctuations. The methods are easy to implement numerically, and estimates remain unaffected by certain measurement errors.
Sabine Wüst, Verena Wendt, Ricarda Linz, and Michael Bittner
Atmos. Meas. Tech., 10, 3453–3462,Short summary
Cubic splines with equidistant spline sampling points are a common method in atmospheric science for the approximation of background conditions by means of filtering superimposed fluctuations from a data series. However, splines can generate considerable artificial oscillations in the background and the residuals. We introduce a repeating spline approach which is able to significantly reduce this phenomenon and to apply it to TIMED-SABER vertical temperature profiles from 2010 to 2014.
Mohamed Djallel Dilmi, Cécile Mallet, Laurent Barthes, and Aymeric Chazottes
Atmos. Meas. Tech., 10, 1557–1574,Short summary
The concept of a rain event is used to obtain a parsimonious characterisation of rain events using a minimal subset of variables at macrophysical scale. A classification in five classes is obtained in a unsupervised way from this subset. Relationships between these classes of microphysical parameters of precipitation are highlighted. There are several implications especially for remote sensing in the context of weather radar applications and quantitative precipitation estimation.
Christophe Praz, Yves-Alain Roulet, and Alexis Berne
Atmos. Meas. Tech., 10, 1335–1357,Short summary
The Multi-Angle Snowflake Camera (MASC) provides high-resolution pictures of individual falling snowflakes and ice crystals. A method is proposed to automatically classify these pictures into six classes of snowflakes as well to estimate the degree of riming and to detect whether or not the particles are melting. Multinomial logistic regression is used with a manually classified reference set. The evaluation demonstrates the good and reliable performance of the proposed technique.
Zhaopeng Luan, Yongxiang Han, Tianliang Zhao, Feng Liu, Chong Liu, Mark J. Rood, Xinghua Yang, Qing He, and Huichao Lu
Atmos. Meas. Tech., 10, 273–279,
Zofia Baldysz, Grzegorz Nykiel, Andrzej Araszkiewicz, Mariusz Figurski, and Karolina Szafranek
Atmos. Meas. Tech., 9, 4861–4877,Short summary
In this paper two official processing strategies of GPS observations were analysed. The main purpose was to assess differences in long-term (linear trends) and short-term (oscillations) changes between these two sets of data. Investigation was based on 18-year and 16-year time series and showed that, despite the general consistency, for selected stations a change of processing strategy may have caused significant differences (compared to the uncertainties) in estimated linear trend values.
Jussi Tiira, Dmitri N. Moisseev, Annakaisa von Lerber, Davide Ori, Ali Tokay, Larry F. Bliven, and Walter Petersen
Atmos. Meas. Tech., 9, 4825–4841,Short summary
In this study winter measurements collected in Southern Finland are used to document microphysical properties of falling snow. It is shown that a new video imager can be used for such studies. Snow properties do vary between winters.
Gutemberg Borges França, Manoel Valdonel de Almeida, and Alessana C. Rosette
Atmos. Meas. Tech., 9, 2335–2344,Short summary
This paper presents a novel model, based on neural network techniques, to produce short-term and locally specific forecasts of significant instability for flights in the terminal area of Rio de Janeiro's airport, Brazil. Twelve years of data were used for neural network training/validation and test. The test showed that the proposed model can grab the physical content inside the data set, and its performance is encouraging for the first and second hours to nowcast significant instability events.
Jacek M. Kopeć, Kamil Kwiatkowski, Siebren de Haan, and Szymon P. Malinowski
Atmos. Meas. Tech., 9, 2253–2265,Short summary
This paper is presenting a feasibility study focused on methods of estimating the turbulence intensity based on a class of navigational messages routinely broadcast by the commercial aircraft (known as ADS-B and Mode-S). Using this kind of information could have potentially significant impact on aviation safety. Three methods have been investigated.
Jörg Burdanowitz, Christian Klepp, and Stephan Bakan
Atmos. Meas. Tech., 9, 1637–1652,Short summary
We develop a new automatic algorithm to distinguish oceanic precipitation into rain, snow and mixed phase using optical disdrometers deployed on board research vessels. In combination, air temperature, relative humidity and the maximum precipitation particle diameter outperform human observer data and yield highest skill to predict the precipitation phase. This knowledge allows deriving accurate rain and snowfall rates with dense global ocean sampling, which enables satellite sensor validation.
Wilko Jessen, Stefan Wilbert, Bijan Nouri, Norbert Geuder, and Holger Fritz
Atmos. Meas. Tech., 9, 1601–1612,Short summary
This paper covers procedures and requirements of two calibration methods and examines the necessary duration of acquisition of test measurements. Site-specific seasonal changes of environmental conditions cause small but noticeable fluctuation of calibration results. Calibration results within certain periods show a higher likelihood of deviation. These effects can partially be attenuated by including more measurements from outside these periods.
G. Biavati, D. G. Feist, C. Gerbig, and R. Kretschmer
Atmos. Meas. Tech., 8, 4215–4230,Short summary
The goal of this work is to present a method that can be used to estimate the uncertainty for a singular estimate for the mixing height. It is defined here as the localization error. The method is based on the actual signal (radiosonde) and its measurement errors, ant it does not consider the physics causing the signal. It can be applied to all kind of signals and algorithm when standard error propagation cannot be used to asses the uncertainty of a location of a localized property.
Y. Inai, M. Shiotani, M. Fujiwara, F. Hasebe, and H. Vömel
Atmos. Meas. Tech., 8, 4043–4054,Short summary
For conventional soundings, the pressure bias of radiosonde leads to an altitude misestimation, which can lead to offsets in any meteorological profile. Therefore, we must take this issue into account to improve historical data sets.
M. Litt, J.-E. Sicart, and W. Helgason
Atmos. Meas. Tech., 8, 3229–3250,Short summary
We deal with surface turbulent flux calculations on a tropical glacier and analyse the related errors. We use data from two eddy-covariance systems and wind speed and temperature profiles collected during a 2-month measurement campaign undertaken within the atmospheric surface layer of the glacier. We show the largest error sources are related to roughness length uncertainties and to nonstationarity of the flow induced by the interaction of outer-layer eddies with the surface-layer flow.
G. J. Fochesatto
Atmos. Meas. Tech., 8, 2051–2060,Short summary
Temperature inversion layers originate based on the combined forcing of local- and large-scale synoptic meteorology. A numerical procedure based on a linear interpolation function of variable length that minimizes an error function set a priori is proposed to extract thermodynamic information of the multilayered thermal structure. The method is demonstrated to detect surface-based inversion and multilayered elevated inversions present often in high-latitude atmospheres.
G. E. Bodeker and S. Kremser
Atmos. Meas. Tech., 8, 1673–1684,Short summary
This paper discusses, and demonstrates, the methodology for reliably determining long-term trends in upper air climate data records and how measurement uncertainties should be used in trend analyses. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper describes the construction of linear least squares regression models for trend analysis and the boot-strapping approach to determine the uncertainty on the derived trends.
M. P. Jensen, T. Toto, D. Troyan, P. E. Ciesielski, D. Holdridge, J. Kyrouac, J. Schatz, Y. Zhang, and S. Xie
Atmos. Meas. Tech., 8, 421–434,Short summary
A major component of the 2011 Midlatitude Continental Convective Clouds Experiment (MC3E) was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state. This manuscript describes the details of the MC3E radiosonde operations including the instrumentation, data processing and analysis of the impacts of bias correction and algorithm assumptions on the determination of forcing data sets.
K. Ramesh, A. P. Kesarkar, J. Bhate, M. Venkat Ratnam, and A. Jayaraman
Atmos. Meas. Tech., 8, 369–384,Short summary
The study of atmospheric convection is important for the understanding of evolution of diurnal cycles of rainfall. High-resolution observations of vertical profiles of temperature and relative humidity are very useful for understanding the behaviour of these convections. Microwave radiometers are becoming useful tools for it. In this paper, we propose a new method to retrieve these profiles based on adaptive neuro-fuzzy interface systems and find that this method has a better skill of retrieval.
T. H. Raupach and A. Berne
Atmos. Meas. Tech., 8, 343–365,Short summary
Using the 2-D video disdrometer (2DVD) as a reference, a technique to correct the spectra of drop size distribution (DSD) measured by Parsivel disdrometers (1st and 2nd generation) is proposed. The measured velocities and equivolume diameters are corrected to better match those from the 2DVD. The correction is evaluated using data from southern France and the Swiss Plateau. It appears to be similar for both climatologies, and to improve the consistency with colocated 2DVDs and rain gauges.
A. Sanchez-Romero, J. A. González, J. Calbó, and A. Sanchez-Lorenzo
Atmos. Meas. Tech., 8, 183–194,
R. J. Dirksen, M. Sommer, F. J. Immler, D. F. Hurst, R. Kivi, and H. Vömel
Atmos. Meas. Tech., 7, 4463–4490,
R. D. García, E. Cuevas, O. E. García, V. E. Cachorro, P. Pallé, J. J. Bustos, P. M. Romero-Campos, and A. M. de Frutos
Atmos. Meas. Tech., 7, 3139–3150,
J. Grazioli, D. Tuia, S. Monhart, M. Schneebeli, T. Raupach, and A. Berne
Atmos. Meas. Tech., 7, 2869–2882,
M. A. Gruber, G. J. Fochesatto, O. K. Hartogensis, and M. Lysy
Atmos. Meas. Tech., 7, 2361–2371,
K. Tohsing, M. Schrempf, S. Riechelmann, and G. Seckmeyer
Atmos. Meas. Tech., 7, 2137–2146,
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Atmos. Meas. Tech., 7, 1043–1055,
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Atmos. Meas. Tech., 6, 3635–3648,
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Atmos. Meas. Tech., 6, 697–702,
M. Li, W. Babel, K. Tanaka, and T. Foken
Atmos. Meas. Tech., 6, 221–229,
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Sea surface wind and waves are important ocean parameters that can be continuously observed by meteorological buoys. Meteorological buoys are sparse in the ocean due to their high cost of deployment and maintenance. In contrast, low-cost compact wave buoys are suited for deployment in large numbers. Although wave buoys are not designed for wind measurement, we found that deep learning can estimate wind from wave measurements accurately, making wave buoys a good-quality data source for sea wind.
Sea surface wind and waves are important ocean parameters that can be continuously observed by...