Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations

Kotthaus, Simone; Bravo-Aranda, Juan Antonio; Coen, Martine Collaud; Guerrero-Rascado, Juan Luís; Costa, María João; Cimini, Domenico; O’Connor, Ewan; Hervo, Maxime; Alados-Arboledas, L.; Jiménez-Portaz, María; Mona, Lucia; Ruffieux, Dominique; Illingworth, Anthony J.; Haeffelin, Martial

doi:10.5194/amt-16-433-2023

Cited by 50 publications

(48 citation statements)

References 358 publications

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“…Note that using only ceilometer data as model input allows for integration of the algorithm in existing Automatic Lidars and Ceilometers networks (e.g., E-Profile, see Haefele et al, 2016). However, including these additional sensor data sources as model input could also further increase the accuracy of MLH detection models (e.g., Kotthaus et al, 2023).…”

Section: Using Alternative Neural Network Architecturesmentioning

confidence: 99%

“…The MLH is not easily and accurately identified in real-time. Existing methods for MLH detection are commonly based on (i) thermodynamic, (ii) wind and turbulence, or (iii) aerosol characteristics (Kotthaus et al, 2023). Thermodynamic methods, such as the parcel method (Holzworth, 1964) and the bulk-Richardson method (Vogelezang and Holtslag, 1996), use temperature and humidity profiles.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Pathfinder: A boundary layer height detection algorithm based on image segmentation

Wijnands

Apituley

Gouveia

et al. 2023

Preprint

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Abstract. The mixing layer height (MLH) indicates the change between vertical mixing of air near the surface and less turbulent air above. MLH is important for the dispersion of air pollutants and greenhouse gases, and for assessing the performance of numerical weather prediction systems. Existing lidar-based MLH detection algorithms typically do not use the full resolution of the ceilometer, require manual feature engineering, and often do not enforce temporal consistency of the MLH. To address these limitations, a novel MLH detection approach has been developed based on deep learning techniques for image segmentation. The concept of our Deep-Pathfinder algorithm is to represent the 24-hour MLH profile as a mask and directly predict it from an image with lidar observations. Therefore, range-corrected signal data was obtained from Lufft CHM 15k ceilometers at five locations in the Netherlands that were part of the operational ceilometer network. Input samples of 224 × 224 pixels were extracted, each covering a 45-minute observation period. A customised U-Net architecture was developed with a nighttime indicator and MobileNetV2 encoder for fast inference times. The model was pre-trained on 19.4 million samples of unlabelled data and fine-tuned using 50 days of high-resolution annotations. Qualitative and quantitative results showed competitive performance compared to two benchmark models: the Lufft and STRATfinder algorithms. Existing path optimisation algorithms have good temporal consistency, but can only be evaluated after a full day of ceilometer data has been recorded. Deep-Pathfinder retains the advantages of temporal consistency but can also provide real-time estimates. This makes our approach valuable for operational settings, as real-time MLH detection better meets the requirements of users such as in aviation, weather forecasting and air quality monitoring.

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Section: Using Alternative Neural Network Architecturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep-Pathfinder: A boundary layer height detection algorithm based on image segmentation

Wijnands

Apituley

Gouveia

et al. 2023

Preprint

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“…Accurate estimation of 𝜀 is crucial for understanding the structure of turbulence in the PBL. To date, a variety of instruments have been used to observe or retrieve the vertical profiles of 𝜀, including sodar, radar wind profiler (RWP), radiosonde, Doppler wind lidar (DWL) and ultrasonic anemometer (Dodson and Griswold, 2021;Jacoby-Koaly et al, 2002;Kotthaus et al, 2023;Lv et al, 2021). Compared with the DWL, the RWP exhibit better capability in capturing the turbulence structures in the cloudy sky.…”

Section: Introductionmentioning

confidence: 99%

Elucidating the boundary layer turbulence dissipation rate using high-resolution measurements from a radar wind profiler network over the Tibetan Plateau

Meng,

Guo,

Guo

et al. 2024

Preprint

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Abstract. The planetary boundary layer (PBL) over the Tibetan Plateau (TP) exerts a significant influence on regional and global climate, while its vertical structures of turbulence and evolution features remain poorly understood, largely due to the scarcity of observation. This study examines the vertical profile and daytime variation of turbulence dissipation rate (ε) in the PBL over the TP using the high-resolution (6 min and 120 m) measurements from the radar wind profiler (RWP) network, combined with the hourly data from the ERA5 reanalysis. Observational analyses show that the magnitude of ε below 3 km under all-sky conditions exhibits large spatial discrepancy over the six RWP sites over the TP. Particularly, the values of ε at Minfeng and Jiuquan over the northern TP and Dingri over the southern TP are roughly an order of magnitude greater than those at Lijiang, Ganzi and Hongyuan over the eastern TP. This could be partially attributed to the difference of land cover across the six RWP sites. In terms of the diurnal variation, ε rapidly intensifies from 0900 local standard time (LST) to 1400 LST, and then gradually levels off in the late afternoon. Under clear-sky conditions, both ε and planetary boundary layer height (zi) are greater, compared with cloudy-sky conditions. This reveals that clouds would suppress the turbulence development and deduce zi. In the lower PBL (0.2<z / zi <0.5, where z is the height above ground level), the dominant influential factor for the development of turbulence is the surface-air temperature difference (Ts – Ta). By comparison, in the upper PBL (0.6< z / zi <1.0), both the and vertical wind shear (VWS) affect the development of turbulence. Above the PBL (1.0< z / zi <2.0), the shear production resulting from VWS dominates the variation of turbulence. Under cloudy-sky conditions, clouds are found to decrease the surface total solar radiation, thereby reducing Ts – Ta and surface sensible heat flux. This weakened sensible heat flux tends to inhibit the turbulent motion within PBL especially in the lower PBL and decrease the growth rate of zi. On the other hand, the strong VWS induced by clouds enhances the turbulence above the PBL. The findings obtained here underscore the importance of RWP network in revealing the fine-scale structures of the PBL over the TP and gaining new insight into the PBL evolution.

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“…In micrometeorology, data assimilation is essential, but normally limited to surface information (stations on ground or buoys). Radiosondes, balloons, towers, radar and LiDAR instruments, airplanes, drones, and finally satellites are to some extent available to calibrate and improve models [ 10 ]. These processes are invaluable to keep accuracy at higher altitudes.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks, Mesoscale Models, and LiDAR Measurements

García-Gutiérrez

López

Domínguez

et al. 2023

Sensors

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This paper introduces a novel methodology that estimates the wind profile within the ABL by using a neural network along with predictions from a mesoscale model in conjunction with a single near-surface measurement. A major advantage of this solution compared to other solutions available in the literature is that it requires only near-surface measurements for prediction once the neural network has been trained. An additional advantage is the fact that it can be potentially used to explore the time evolution of the wind profile. Data collected by a LiDAR sensor located at the University of León (Spain) is used in the present research. The information obtained from the wind profile is valuable for multiple applications, such as preliminary calculations of the wind asset or CFD modeling.

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Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations

Cited by 50 publications

References 358 publications

Deep-Pathfinder: A boundary layer height detection algorithm based on image segmentation

Deep-Pathfinder: A boundary layer height detection algorithm based on image segmentation

Elucidating the boundary layer turbulence dissipation rate using high-resolution measurements from a radar wind profiler network over the Tibetan Plateau

Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks, Mesoscale Models, and LiDAR Measurements

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