气象垂直观测风廓线缺测数据插补研究

为评估ERA5再分析数据在贵阳机场高空气象要素方面的适用性，本文将ERA5数据与贵阳机场的实际观测数据进行了比较分析，深入探讨了其在温度、湿度、气压以及风场等常规气象要素上的具体表现。研究结果显示，ERA5数据与贵阳机场实际观测数据基本一致，其中ERA5的温度资料与微波辐射计的温度在低层的一致性较高，ERA5的值比微波辐射计测得的值偏大，总的平均偏差为1.9℃；ERA5的温度资料与微波辐射计的相对湿度随着高度的增加差异越大，均方根误差和平均偏差平均值分别为13%和10%；ERA5的风速资料比风廓线雷达的风速偏小，均方根误差和平均偏差的平均值分别为3.1 m/s和1.3 m/s，相关性高达0.93；ERA5的风向资料与风廓线雷达的风向越到高层差异越小，反应的主要方位角基本一致。

风廓线雷达作为前沿的大气探测设备，在气象及相关领域的重要性与日俱增。本文深入剖析风廓线雷达的工作原理，在气象预报、航空安全保障等多方面的应用，同时从日常定期维护、维护方法与要点，为提升风廓线雷达运行稳定性与数据准确性提供参考。

研究成都地区风廓线的演变特征具有重要的科学意义和实际应用价值。本文利用成都市温江气象站2020~2024年观测数据通过风速廓线、风向玫瑰图，总结归纳风廓线的演变规律，采用WRF数值模拟评估了常用的三种边界层参数化方案(YSU、MYJ、ACM2)对成都地区风场的模拟性能。结果表明：(1) 成都地区08时与20时风场垂直结构特征相似，多项式拟合可较好地表征风廓线变化特征；平均风速随高度呈现先增后减趋势，低层冬季平均风速最小夏季最大，中高层相反，春季、秋季特征相似；其中850 hPa主导风向为东北风，到700 hPa偏南风增加，500 hPa以西风为主导，季节主导风向在700 hPa及以上层次存在差异，而且夏季的风向转换较多。(2) 三种方案均能有效模拟出成都地区风场特征，通过模拟结果和观测对比显示，采用YSU边界层方案为最优参数化方案。

选取山东省威海海上、昌邑下营、海阳里口三座70 m高度测风塔连续一整年的观测数据，采用最小二乘法拟合等方法，对不同下垫面条件下风随高度的变化特征进行了分析。结果表明：三座测风塔风速随高度的变化有着显著差异，位于山区的里口测风塔风随高度变化最复杂，风切变指数最大，下营测风塔次之，海上测风塔风随高度变化相对最不明显，风切变指数最小，大风情况下，陆上测风塔风切变指数显著减小，但海上测风塔风切变指数变化不大。

Abstract Reproducing realistic date‐ and site‐specific unsteady wind conditions in large‐eddy simulations is becoming increasingly useful in wind energy. How to run a large‐eddy simulation to match observed conditions, however, remains an open research question. One approach that has received considerable attention is mesoscale‐to‐microscale coupling, in which information about the mesoscale weather, most commonly acquired from a mesoscale numerical weather model, is passed on to a microscale model. In this paper, we demonstrate how the recently developed profile‐assimilation technique, a form of mesoscale‐to‐microscale coupling, can be used to drive large‐eddy simulations solely based on observed mean‐flow profiles at a single location, bypassing the need for auxiliary mesoscale simulations. The new approach is evaluated for a diurnal cycle at the Scaled Wind Farm Technology site. Observed mean‐flow profiles from the ground up to a height of 2 km are reconstructed by aggregating measurements from multiple instruments, and gaps in the data are infilled with natural neighbor interpolation. We perform nine simulations using various forcing approaches to deal with data limitations. The results show that it is indeed possible to drive microscale large‐eddy simulation with observations using the profile‐assimilation technique, notwithstanding large gaps in virtual potential temperature measurements. However, profile assimilation with vertical smoothing of the error between the desired and actual profiles is required. Without that smoothing, the microscale simulations develop unrealistically high turbulence levels under many situations. Finally, we show that simulated mesoscale data can account for missing observations, although care is needed as both data sources are not necessarily compatible.

Abstract. A 2-year measurement campaign of the ZephIR 300 vertical profiling continuous-wave (CW) focusing wind lidar has been carried out by the Royal Netherlands Meteorological Institute (KNMI) at the Cabauw site. We focus on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213 m tall meteorological mast. We find an overall availability of quality-controlled wind lidar data of 97 % to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm h−1 or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m s−1 with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. The mean bias in the wind direction is within 2∘, which is of the same order as the combined uncertainty in the alignment of the wind lidars and the mast wind vanes. The well-known 180∘ error in the wind direction output for this type of instrument occurs about 9 % of the time. A correction scheme based on data of an auxiliary wind vane at a height of 10 m is applied, leading to a reduction of the 180∘ error below 2 %. This scheme can be applied in real-time applications in the situation that a nearby freely exposed mast with wind direction measurements at a single height is available.

Abstract. The planetary boundary layer height (zi) is a key parameter used in atmospheric models for estimating the exchange of heat, momentum, and moisture between the surface and the free troposphere. Near-surface atmospheric and subsurface properties (such as soil temperature, relative humidity, etc.) are known to have an impact on zi. Nevertheless, precise relationships between these surface properties and zi are less well known and not easily discernible from the multi-year dataset. Machine learning approaches, such as random forest (RF), which use a multi-regression framework, help to decipher some of the physical processes linking surface-based characteristics to zi. In this study, a 4-year dataset from 2016 to 2019 at the Southern Great Plains site is used to develop and test a machine learning framework for estimating zi. Parameters derived from Doppler lidars are used in combination with over 20 different surface meteorological measurements as inputs to a RF model. The model is trained using radiosonde-derived zi values spanning the period from 2016 through 2018 and then evaluated using data from 2019. Results from 2019 showed significantly better agreement with the radiosonde compared to estimates derived from a thresholding technique using Doppler lidars only. Noteworthy improvements in daytime zi estimates were observed using the RF model, with a 50 % improvement in mean absolute error and an R2 of greater than 85 % compared to the Tucker method zi. We also explore the effect of zi uncertainty on convective velocity scaling and present preliminary comparisons between the RF model and zi estimates derived from atmospheric models.

The Aeolus satellite mission of the European Space Agency (ESA) has brought the first wind LiDAR to space to satisfy the long-existing need for global wind profile observations. Until the successful launch on 22 August 2018, pre-launch campaign activities supported the validation of the measurement principle, the instrument calibration, and the optimization of retrieval algorithms. Therefore, an airborne prototype instrument has been developed, the ALADIN Airborne Demonstrator (A2D), with ALADIN being the Atmospheric Laser Doppler Instrument of Aeolus. Two airborne campaigns were conducted over Greenland, Iceland and the Atlantic Ocean in September 2009 and May 2015, employing the A2D as the first worldwide airborne direct-detection Doppler Wind LiDAR (DWL) and a well-established coherent 2-µm wind LiDAR. Both wind LiDAR instruments were operated on the same aircraft measuring Mie backscatter from aerosols and clouds as well as Rayleigh backscatter from molecules in parallel. This paper particularly focuses on the instrument response calibration method of the A2D and its importance for accurate wind retrieval results. We provide a detailed description of the analysis of wind measurement data gathered during the two campaigns, introducing a dedicated aerial interpolation algorithm that takes into account the different resolution grids of the two LiDAR systems. A statistical comparison of line-of-sight (LOS) winds for the campaign in 2015 yielded estimations of the systematic and random (mean absolute deviation) errors of A2D observations of about 0.7 m/s and 2.1 m/s, respectively, for the Rayleigh, and 0.05 m/s and 2.3 m/s, respectively, for the Mie channel. In view of the launch of Aeolus, differences between the A2D and the satellite mission are highlighted along the way, identifying the particular assets and drawbacks.

Abstract. The WISSDOM (Wind Synthesis System using Doppler Measurements) synthesis scheme was developed to derive high-resolution 3-dimensional (3D) winds under clear-air conditions. From this variational-based scheme, detailed wind information was obtained from scanning Doppler lidars, automatic weather stations (AWSs), sounding observations, and local reanalysis datasets (LDAPS, Local Data Assimilation and Prediction System), which were utilized as constraints to minimize the cost function. The objective of this study is to evaluate the performance and accuracy of derived 3D winds from this modified scheme. A strong wind event was selected to demonstrate its performance over complex terrain in Pyeongchang, South Korea. The size of the test domain is 12×12 km2 extended up to 3 km a.m.s.l. (above mean sea level) height with a remarkably high horizontal and vertical resolution of 50 m. The derived winds reveal that reasonable patterns were explored from a control run, as they have significant similarity with the sounding observations. The results of intercomparisons show that the correlation coefficients between derived horizontal winds and sounding observations are 0.97 and 0.87 for u- and v-component winds, respectively, and the averaged bias (root mean square deviation, RMSD) of horizontal winds is between −0.78 and 0.09 (1.77 and 1.65) m s−1. The correlation coefficients between WISSDOM-derived winds and lidar QVP (quasi-vertical profile) are 0.84 and 0.35 for u- and v-component winds, respectively, and the averaged bias (RMSD) of horizontal winds is between 2.83 and 2.26 (3.69 and 2.92) m s−1. The statistical errors also reveal a satisfying performance of the retrieved 3D winds; the median values of wind directions are −5 to 5 (0 to 2.5)∘, the wind speed is approximately −1 to 3 m s−1 (−1 to 0.5 m s−1), and the vertical velocity is −0.2 to 0.6 m s−1 compared with the lidar QVP (sounding observations). A series of sensitivity tests with different weighting coefficients, radius of influence (RI) in interpolation, and various combination of different datasets were also performed. The results indicate that the present setting of the control run is the optimal reference to WISSDOM synthesis in this event and will help verify the impacts against various scenarios and observational references in this area.

Abstract This study introduces an efficient and precise method for gathering atmospheric wind field data in specific regions, utilizing unmanned aerial vehicles (UAVs) equipped with anemometers. We conducted outdoor wind field measurements over complex terrains using a six-rotor UAV equipped with an ultrasonic anemometer. The results obtained include a vertical wind field profile at the center of the measured site, along with two planar wind field profiles at 20 m and 40 m heights. The analysis reveals a remarkably high fitting accuracy for the wind profile and turbulence intensity results obtained. Furthermore, the planar wind field data partially demonstrates the impact of terrain on the upper-level wind field within the surveyed area. Lastly, we established a three-dimensional wind field visualization approach using the data gathered through the UAV wind measurement method, implementing Kriging interpolation. This study’s outcomes offer novel insights and methodologies for local wind field measurement, micro-siting in wind farms, and the creation of visualized wind fields.

This paper presents a framework for the conditional simulation of a 3D nonstationary wind field for super-long sea-crossing bridges based on wind speed time histories measured by limited anemometers on site. The variations of wind characteristics along the bridge deck with multi-elements are considered in this framework. The wind characteristics, including mean wind speeds, mean wind directions, turbulence intensities, turbulence integral scales, evolutionary wind spectra and spectral parameters, coherence functions and function parameters, at the measurement points are first obtained by analyzing the measured wind speed time histories. The wind characteristics are then extended over all the simulation points along the deck of the sea-crossing bridge using the spatial interpolation method and considering the variation of deck height above the sea level using the profiles of mean wind speeds, turbulence intensities and turbulence integral scales estimated from the measured wind speed time histories. After working out wind cross correlation functions, wind speed time histories at all simulation points over the sea-crossing bridge can be conditionally simulated. The proposed framework is finally applied to a real 22.9 km sea-crossing bridge under Typhoon Higos. The conditionally simulated wind speed time histories at all simulation points of the bridge are acceptable and consist a complete wind field which can be used for the buffeting analysis of the bridge.

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Abstract. Profiling nacelle lidars probe the wind at several heights and several distances upstream of the rotor. The development of such lidar systems is relatively recent, and it is still unclear how to condense the lidar raw measurements into useful wind field characteristics such as speed, direction, vertical and longitudinal gradients (wind shear). In this paper, we demonstrate an innovative method to estimate wind field characteristics using nacelle lidar measurements taken within the induction zone. Model-fitting wind field reconstruction techniques are applied to nacelle lidar measurements taken at multiple distances close to the rotor, where a wind model is combined with a simple induction model. The method allows robust determination of free-stream wind characteristics. The method was applied to experimental data obtained with two different types of nacelle lidar (five-beam Demonstrator and ZephIR Dual Mode). The reconstructed wind speed was within 0.5 % of the wind speed measured with a mast-top-mounted cup anemometer at 2.5 rotor diameters upstream of the turbine. The technique described in this paper overcomes measurement range limitations of the currently available nacelle lidar technology.

LiDAR-based wind speed measurements have seen a significant increase in interest in wind energy. However, reconstruction of wind speed vector from a LiDAR-measured radial wind speed is still a challenge. Furthermore, for extensive application of LiDAR technology, it can be used as a means to validate simulation and analytical models. To that end, this study employed scanning Doppler LiDAR for assessment of wind fields at an offshore site and compared Weather Research and Forecasting (WRF)-based mesoscale simulations and several wake models with the measurements. Firstly, the effect of carrier-to-noise-ratio (CNR) and data availability on the quality of scanning LiDAR measurements was evaluated. Analysis of vertical profiles show that the average wind speed is higher for wind blowing from the sea than that blowing from the land. Furthermore, profiles obtained from the WRF simulation also show a similar tendency in the LiDAR measurements in general, though it overestimates the wind speeds at higher altitudes. A method for reconstruction of wind fields from plan-position indicator (PPI) and range height indicator (RHI) scans of LiDAR-measured line of sight velocities was then proposed and first used to investigate the effect of coastal terrain. An internal boundary layer with strong shear could be observed to develop from the coastline. Finally, the flow field around wind turbine was measured using PPI scan and used to validate wake models.

This paper presents a method for reconstructing the wake wind field of a wind turbine based on planar light detection and ranging (LiDAR) scans crossing the wake transversally in the vertical and horizontal directions. Volumetric measurements enable the study of wake characteristics in these two directions. Due to a lack of highly resolved wind speed measurements as reference data, we evaluate the reconstruction in a synthetic environment and determine the reconstruction errors. The wake flow of a multi-megawatt wind turbine is calculated within a 10-min large-eddy simulation (LES) for high-thrust loading conditions. We apply a numerical LiDAR simulator to this wake wind field to achieve realistic one-dimensional velocity data. We perform a nacelle-based set-up with combined plan position indicator and range height indicator scans with eight scanning velocities each. We temporally up-sample the synthetic LiDAR data with a weighted combination of forward- and backward-oriented space–time conversion to retrospectively extract high-resolution wake characteristic dynamics. These dynamics are used to create a dynamic volumetric wake deficit. Finally, we reconstruct the dynamic wake wind field in three spatial dimensions by superposing an ambient wind field with the dynamic volumetric wake deficit. These results demonstrate the feasibility of wake field reconstruction using long-range LiDAR measurements.

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Abstract. Turbulent velocity spectra derived from velocity–azimuth display (VAD) scanning wind lidars deviate from spectra derived from one-point measurements due to averaging effects and cross-contamination among the velocity components. This work presents two novel methods for minimizing these effects through advanced raw data processing. The squeezing method is based on the assumption of frozen turbulence and introduces a time delay into the raw data processing in order to reduce cross-contamination. The two-beam method uses only certain laser beams in the reconstruction of wind vector components to overcome averaging along the measurement circle. Models are developed for conventional VAD scanning and for both new data processing methods to predict the spectra and identify systematic differences between the methods. Numerical modeling and comparison with measurement data were both used to assess the performance of the methods. We found that the squeezing method reduces cross-contamination by eliminating the resonance effect caused by the longitudinal separation of measurement points and also considerably reduces the averaging along the measurement circle. The two-beam method eliminates this averaging effect completely. The combined use of the squeezing and two-beam methods substantially improves the ability of VAD scanning wind lidars to measure in-wind (u) and vertical (w) fluctuations.

We present a preliminary analysis of inflow measurements performed with a SpinnerLidar on a turbine’s nacelle and those from three grounded-based short-range continuous- wave lidars (WindScanners) during the ScanFlow experiment. After proper filtering for blade contamination and hub/nacelle shading of the beam, the SpinnerLidar measurements capture the structure of the inflow in detail. The WindScanners’ 3D measurements provide estimations of the three wind speed components without any flow assumptions. These 3D wind field measurements are used as reference to evaluate SpinnerLidar reconstructed winds. A wind reconstruction methodology for the SpinnerLidar measurements is evaluated against a numerical wind inflow simulation successfully. An intercomparison between reconstructed longitudinal velocity components from the WindScanners and the Spinnerlidar shows good agreement (no bias and high correlation) at hub height and close to zero biases for all vertical levels measured by the SpinnerLidar.

Preview measurements of the inflow by turbine-mounted lidar systems can be used to optimise wind turbine performance or alleviate structural loads. However, nacelle-mounted lidars suffer data losses due to unfavourable environmental conditions and laser beam obstruction by the rotating blades. Here, we apply proper orthogonal decomposition (POD) to the simulated line-of-sight wind speed measurements of a turbine-mounted scanning lidar obtained from two large eddy simulations. This work aimed at identifying the dominant POD modes that can be used to subsequently derive a reduced-order representation of the turbine inflow. Secondly, we reconstructed the data points lost due to blade passage by using Gappy-POD. We found that only a few modes are required to capture the dynamics of the wind field parameters commonly used for lidar-assisted wind turbine control, such as the effective wind speed, vertical shear and directional misalignment. By evaluating turbine-relevant metrics in the time and frequency domain, we found that a ten-mode reconstruction could accurately describe most spatio-temporal variations in the inflow. Furthermore, a modal interpretation is presented by direct comparison with these wind field parameters. We found that the Gappy-POD method performs substantially better than spatial interpolation techniques, accurately reconstructing up to even 50% of missing data. A POD-based wind field reconstruction offers a trade-off between wind field reconstruction techniques requiring flow assumptions and more complex physics-based representations, offers dimensional reduction and can overcome the blade passage limitation of nacelle-mounted lidar systems.