Observation of nocturnal low-level wind shear and particulate matter in urban Beijing using a Doppler wind lidar

Chen, Yong; An, Junling; Lin, Jian; Sun, Yele; Wang, Xiquan; Wang, Zifa; Duan, Jing

doi:10.1080/16742834.2017.1368349

Cited by 9 publications

(6 citation statements)

References 14 publications

(14 reference statements)

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“…In terms of the climate in Beijing, it typically belongs to a semi-humid continental climate in the north temperate zone, characterized by hot and humid summers due to the subsidence caused by the subtropical high, and cold, windy and dry winters which is mainly under the influence of the vast Siberian anticyclone [41][42][43]. Due to the huge amount of anthropogenic emission in the NCP (see Figure 1a), the atmospheric pollution (especially the PM 2.5 ) in Beijing has been intensively analyzed in recent years [38,39,41,44].…”

Section: Study Areamentioning

confidence: 99%

“…Among others, reanalysis data and model simulations were one of the most used approaches to analyze the variation of wind with different height in the lower troposphere and its impacts on air pollution, revealing a significant role of vertical wind shear (VWS, an important indicator of dynamically vertical mixing) in modulating particulate matter pollution [37,39,40]. In recent years, there has been a surge of interest in observational investigation of VWS in connection with atmospheric pollution, most of which are based on Doppler wind lidar [26,39]. However, the Doppler wind lidar has limited capability to offer vertically resolved wind observations under pretty clean or foggy conditions.…”

Section: Introductionmentioning

confidence: 99%

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Vertical Wind Shear Modulates Particulate Matter Pollutions: A Perspective from Radar Wind Profiler Observations in Beijing, China

et al. 2020

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Vertical wind shear (VWS) is one of the key meteorological factors in modulating ground-level particulate matter with an aerodynamic diameter of 2.5 µm or less (PM2.5). Due to the lack of high-resolution vertical wind measurements, how the VWS affects ground-level PM2.5 remains highly debated. Here we employed the wind profiling observations from the fine-time-resolution radar wind profiler (RWP), together with hourly ground-level PM2.5 measurements, to explore the wind features in the planetary boundary layer (PBL) and their association with aerosols in Beijing for the period from December 1, 2018, to February 28, 2019. Overall, southerly wind anomalies almost dominated throughout the whole PBL or even beyond the PBL under polluted conditions during the course of a day, as totally opposed to the northerly wind anomalies in the PBL under clean conditions. Besides, the ground-level PM2.5 pollution exhibited a strong dependence on the VWS. A much weaker VWS was observed in the lower part of the PBL under polluted conditions, compared with that under clean conditions, which could be due to the strong ground-level PM2.5 accumulation induced by weak vertical mixing in the PBL. Notably, weak northbound transboundary PM2.5 pollution mainly appeared within the PBL, where relatively small VWS dominated. Above the PBL, strong northerlies winds also favored the long-range transport of aerosols, which in turn deteriorated the air quality in Beijing as well. This was well corroborated by the synoptic-scale circulation and backward trajectory analysis. Therefore, we argued here that not only the wind speed in the vertical but the VWS were important for the investigation of aerosol pollution formation mechanism in Beijing. Also, our findings offer wider insights into the role of VWS from RWP in modulating the variation of PM2.5, which deserves explicit consideration in the forecast of air quality in the future.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vertical Wind Shear Modulates Particulate Matter Pollutions: A Perspective from Radar Wind Profiler Observations in Beijing, China

et al. 2020

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“…Kondo et al (1989) revealed that the upslope and downslope motions under the influence of valley wind circulation could affect the daytime and nighttime energy budgets over the plain areas; Kuwagata et al (1990aKuwagata et al ( , 1990b pointed out that the heating rates in basin and inland plain are often larger than mountainous area and coastal plain under the influence of the compensating airflow of the upslope flow. Further studies based on the data with finer temporal and spatial resolution (e.g., wind-profiler measurements, meteorological tower) have advanced the knowledge of the effects on complex topography (e.g., Chen et al, 2017;Solanki et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the PBL climate should be regional dependent. In the NCP, previous works mostly focused on PBLs in several metropolises, for example, Beijing, Tianjin (Chen et al, 2017;Miao et al, 2017;Solanki et al, 2021), and only a few have studied the southern parts (e.g., Li et al, 2015;Zhu et al, 2018). Their spatial and temporal representativeness to the whole NCP was insufficient.…”

Section: Introductionmentioning

confidence: 99%

Planetary boundary layer climatological features over North China Plain: Derived from intensive experiment data

Yan

Jin

et al. 2023

Intl Journal of Climatology

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A field campaign was carried out in the North China Plain (NCP) spanning 4 years and formed a dataset of more than 2,700 planetary boundary layer (PBL) sounding samples (872 in summer and 1,841 in winter). Based on these data, fundamental aspects of the PBL climatology over this region were investigated. First, the ensemble mean features of the PBL were revealed. The maximum PBL height in the daytime reaches 1,079 versus 786 m (summer vs. winter, hereafter the same), while about 200 m at nighttime. The bulk mean temperature and specific humidity across the PBL in summer are significantly higher than those in winter (24.0 vs. −0.5°C; 8.9 vs. 1.7 g⋅kg−1, respectively). Mean wind speed within the PBL keeps almost the same in winter and summer (4.5 m⋅s−1) but with a seasonal difference in wind direction (southerly vs. northwesterly). Second, significant diurnal variations within/above the PBL were quantified. Day‐night temperature contrast is largest near the ground (7.9 vs. 5.1°C), and it decreases with height to almost a constant above the PBL. Diurnal variation amplitude of specific humidity is about 1.0 versus 0.3 g⋅kg−1. Diurnal variation of winds also supports the extended influence of the earth surface across the PBL. Third, the local influence on the PBL over the NCP is also presented. PBL height is higher in the plain centre; temperature is lower in coast than inland; winds in the PBL are strongly modulated by local or mesoscale circulations including the plain wind and sea breeze; low‐level jets are common at summer night over the central and eastern parts of the NCP. Analysis reveals possible energy and water vapour exchange across the PBL, and additional heating mechanism above the PBL (e.g., solar radiation absorption).

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“…Other instruments i.e., Lidar Doppler and Terminal Doppler Weather Radar (TDWR) developed to detect wind shear is remote sensing-based tool (Chen et al, 2017;Chun et al, 2017;International Civil Aviation Organization, 2005;Nechaj et al, 2019;Shun and Chan, 2008;Thobois et al, 2019). Those Remote sensing instruments can detect wind shear at any height unlike LLWAS, but they are susceptible to the weather condition.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Low-Level Wind Shear Alert Prediction System Based on Anemometer Sensor Network and Temporal Convolutional Network (Tcn)

RYAN¹,

SAPUTRO²,

SOPAHELUWAKAN³

2022

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Wind shear is one of the dangerous meteorological phenomena for aviation. This phenomenon is significant, especially at the lower level. The duration of wind shear events varies greatly, ranging from short to long. The best way to avoid accidents caused by wind shear is by predicting the event and the duration. Recent studies use Machine Learning (ML) as a nonlinear geostatistical method to predict wind shear utilizing wind observing instruments data. The data is conditioned into temporal data which is fed to the ML model. However, the ML model used is not a temporal ML model for time-series data but a generic model for a common type of data. Many studies claimed temporal models are better than generic ones to tackle temporal data. In this study, we propose Temporal Convolutional Network (TCN) to predict incoming wind shear duration and occurrence using an anemometer sensor network i.e., Low-level Wind Shear Alert System (LLWAS). The wind shear occurrence is derived from wind shear duration prediction. The proposed model is compared with other temporal models, i.e., Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Different schemes of total predictor were tested to find the best predictor scheme for wind shear prediction. To measure the performance of all models in all schemes, accuracy, False Alarm Ratio (FAR), Probability of Detection (POD), and Root Mean Squared Error (RMSE) metrics are used. The result is TCN dominating almost in all metrics used i.e., Accuracy, FAR, and RMSE for all schemes against LSTM and GRU. Scheme with 4 predictors proved to bring the best performance of all models for wind shear duration prediction. The result proves TCN is the best temporal model for wind shear forecasting using LLWAS. For better wind shear duration prediction, the best scheme choice is the 4-predictor scheme.

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Observation of nocturnal low-level wind shear and particulate matter in urban Beijing using a Doppler wind lidar

Cited by 9 publications

References 14 publications

Vertical Wind Shear Modulates Particulate Matter Pollutions: A Perspective from Radar Wind Profiler Observations in Beijing, China

Vertical Wind Shear Modulates Particulate Matter Pollutions: A Perspective from Radar Wind Profiler Observations in Beijing, China

Planetary boundary layer climatological features over North China Plain: Derived from intensive experiment data

Intelligent Low-Level Wind Shear Alert Prediction System Based on Anemometer Sensor Network and Temporal Convolutional Network (Tcn)

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