scholarly journals Tailored Algorithms for the Detection of the Atmospheric Boundary Layer Height from Common Automatic Lidars and Ceilometers (ALC)

2020 ◽  
Vol 12 (19) ◽  
pp. 3259
Author(s):  
Simone Kotthaus ◽  
Martial Haeffelin ◽  
Marc-Antoine Drouin ◽  
Jean-Charles Dupont ◽  
Sue Grimmond ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Signal To Noise Ratio ◽  
Deep Convection ◽  
High Signal ◽  
Pollution Dispersion ◽  
Low Snr ◽  
Boundary Layer Height ◽  
Cloud Dynamics ◽  
New Generation

A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for the height of the ABL and its sublayers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Here, the advanced algorithm STRATfinder is presented for application to high signal-to-noise ratio (SNR) ALC observations, and results are compared to an automatic algorithm designed for low-SNR measurements (CABAM). The two algorithms are evaluated for application in an operational network setting. Results indicate that the ABL heights derived from low-SNR ALC have increased uncertainty during daytime deep convection, while high-SNR observations can have slightly reduced capabilities in detecting shallow nocturnal layers. Agreement between the ALC-based methods is similar when either is compared to the ABL heights derived from temperature profile data. The two independent methods describe very similar average diurnal and seasonal variations. Hence, high-quality products of ABL heights may soon become possible at national and continental scales.

scholarly journals Automatic detection of atmospheric boundary layer heights at European scale (ABL testbed)

2021 ◽  
Author(s):  
Simone Kotthaus ◽  
Martial Haeffelin ◽  
Marc-Antoine Drouin ◽  
Clement Laplace ◽  
Sophie Bouffies-Cloche ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Pollution Dispersion ◽  
Layer Height ◽  
Low Snr ◽  
Boundary Layer Height ◽  
Cloud Dynamics ◽  
New Generation

<p>A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for ABL layers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Initial evaluation of the advanced algorithms STRATfinder (for application to high signal-to-noise ratio (SNR) ALC observations) and CABAM (low-SNR measurements) to automatically derive ABL heights indicates promising performances (Kotthaus et al. 2020).</p><p>In the framework of the ABL testbed project (funded by ICOS, ACTRIS and E-PROFILE) the two algorithms are now being assessed for application in an operational network setting, such as EUMETNET E-PROFILE. A subset of 11 E-PROFILE sites in a range of geographical and land cover settings across Europe are selected where data from low-SNR and/or high-SNR ALC are available for multiple years. Automatic layer detection is implemented, including instrument-specific corrections and calibrations. Algorithm performance for layer height detection is being evaluated via comparison of results from different ALC and by including reference data from thermodynamic- and turbulence derived layer heights from radiosondes and other ground-based profiling sensors where available. Recommendations are formulated for implementation of automatic ABL height retrievals across a diverse sensor network. A prime example of collaborations within the EU COST action PROBE on profiling the atmospheric boundary layer, the ABL testbed is a crucial step towards harmonised ABL height products at the European scale.</p><p>References</p><p><strong>Kotthaus, S, M Haeffelin, MA Drouin, JC Dupont, S Grimmond, A Haefele, M Hervo, Y Poltera, M Wiegner, 2020: Tailored Algorithms for the Detection of the Atmospheric Boundary Layer Height from Common Automatic Lidars and Ceilometers (ALC). <em>Remote Sens</em>, 12, 3259, DOI: 10.3390/rs12193259.</strong></p><p> </p>

scholarly journals Lidar-based remote sensing of atmospheric boundary layer height over land and ocean

2014 ◽  
Vol 7 (1) ◽  
pp. 173-182 ◽  
Author(s):  
T. Luo ◽  
R. Yuan ◽  
Z. Wang
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Marine Boundary Layer ◽  
Mixing Layer ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Lidar Measurements ◽  
Mixing Layer Height ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Boundary Layer Height

Abstract. Atmospheric boundary layer (ABL) processes are important in climate, weather and air quality. A better understanding of the structure and the behavior of the ABL is required for understanding and modeling of the chemistry and dynamics of the atmosphere on all scales. Based on the systematic variations of the ABL structures over different surfaces, different lidar-based methods were developed and evaluated to determine the boundary layer height and mixing layer height over land and ocean. With Atmospheric Radiation Measurement Program (ARM) Climate Research Facility (ACRF) micropulse lidar (MPL) and radiosonde measurements, diurnal and season cycles of atmospheric boundary layer depth and the ABL vertical structure over ocean and land are analyzed. The new methods are then applied to satellite lidar measurements. The aerosol-derived global marine boundary layer heights are evaluated with marine ABL stratiform cloud top heights and results show a good agreement between them.

scholarly journals Graphics algorithm for deriving atmospheric boundary layer heights from CALIPSO data

2018 ◽  
Vol 11 (9) ◽  
pp. 5075-5085 ◽  
Author(s):  
Boming Liu ◽  
Yingying Ma ◽  
Jiqiao Liu ◽  
Wei Gong ◽  
Wei Wang ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Weather Forecasting ◽  
Correlation Coefficients ◽  
Horizontal Resolution ◽  
Backscatter Coefficient ◽  
Distribution Method ◽  
Boundary Layer Height ◽  
Lidar Measurements ◽  
Ground Based Lidar

Abstract. The atmospheric boundary layer is an important atmospheric feature that affects environmental health and weather forecasting. In this study, we proposed a graphics algorithm for the derivation of atmospheric boundary layer height (BLH) from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Owing to the differences in scattering intensity between molecular and aerosol particles, the total attenuated backscatter coefficient 532 and attenuated backscatter coefficient 1064 were used simultaneously for BLH detection. The proposed algorithm transformed the gradient solution into graphics distribution solution to overcome the effects of large noise and improve the horizontal resolution. This method was then tested with real signals under different horizontal smoothing numbers (1, 3, 15 and 30). Finally, the results of BLH obtained by CALIPSO data were compared with the results retrieved by the ground-based lidar measurements. Under the horizontal smoothing number of 15, 12 and 9, the correlation coefficients between the BLH derived by the proposed algorithm and ground-based lidar were both 0.72. Under the horizontal smoothing number of 6, 3 and 1, the correlation coefficients between the BLH derived by graphics distribution method (GDM) algorithm and ground-based lidar were 0.47, 0.14 and 0.12, respectively. When the horizontal smoothing number was large (15, 12 and 9), the CALIPSO BLH derived by the proposed method demonstrated a good correlation with ground-based lidar. The algorithm provided a reliable result when the horizontal smoothing number was greater than 9. This finding indicated that the proposed algorithm can be applied to the CALIPSO satellite data with 3 and 5 km horizontal resolution.

scholarly journals A Review of Techniques for Diagnosing the Atmospheric Boundary Layer Height (ABLH) Using Aerosol Lidar Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1590 ◽  
Author(s):  
Ruijun Dang ◽  
Yi Yang ◽  
Xiao-Ming Hu ◽  
Zhiting Wang ◽  
Shuwen Zhang
Keyword(s):  
Boundary Layer ◽  
Air Quality ◽  
Atmospheric Boundary Layer ◽  
Weather Prediction ◽  
Mixing Layer ◽  
Accurate Estimation ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Vertical Distributions ◽  
Multi Wavelength

The height of the atmospheric boundary layer (ABLH) or the mixing layer height (MLH) is a key parameter characterizing the planetary boundary layer, and the accurate estimation of that is critically important for boundary layer related studies, which include air quality forecasts and numerical weather prediction. Aerosol lidar is a powerful remote sensing instrument frequently used to retrieve the ABLH through detecting the vertical distributions of aerosol concentration. Presently available methods for ABLH determination from aerosol lidar are summarized in this review, including a lot of classical methodologies as well as some improved versions of them. Some new recently developed methods applying advanced techniques such as image edge detection, as well as some new methods based on multi-wavelength lidar systems, are also summarized. Although a lot of techniques have been proposed and have already given reasonable results in several studies, it is impossible to recommend a technique which is suitable in all atmospheric scenarios. More accurate instantaneous ABLH from robust techniques is required, which can be used to estimate or improve the boundary layer parameterization in the numerical model, or maybe possible to be assimilated into the weather and environment models to improve the simulation or forecast of weather and air quality in the future.

Atmospheric Boundary Layer Height Monitoring Using a Kalman Filter and Backscatter Lidar Returns

2014 ◽  
Vol 52 (8) ◽  
pp. 4717-4728 ◽  
Author(s):  
Diego Lange ◽  
Jordi Tiana-Alsina ◽  
Umar Saeed ◽  
Sergio Tomas ◽  
Francesc Rocadenbosch
Keyword(s):  
Boundary Layer ◽  
Kalman Filter ◽  
Atmospheric Boundary Layer ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Atmospheric Boundary Layer Height

scholarly journals A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes

2019 ◽  
Vol 9 (5) ◽  
pp. 831
Author(s):  
Yusheng Xing ◽  
Guofang Tu
Keyword(s):  
Error Correction ◽  
Error Rate ◽  
Complexity Analysis ◽  
Signal To Noise Ratio ◽  
Low Complexity ◽  
High Signal ◽  
Polar Codes ◽  
Decoding Algorithm ◽  
Low Snr ◽  
Ordered Statistics

In this paper, we propose a low-complexity ordered statistics decoding (OSD) algorithm called threshold-based OSD (TH-OSD) that uses a threshold on the discrepancy of the candidate codewords to speed up the decoding of short polar codes. To determine the threshold, we use the probability distribution of the discrepancy value of the maximal likelihood codeword with a predefined parameter controlling the trade-off between the error correction performance and the decoding complexity. We also derive an upper-bound of the word error rate (WER) for the proposed algorithm. The complexity analysis shows that our algorithm is faster than the conventional successive cancellation (SC) decoding algorithm in mid-to-high signal-to-noise ratio (SNR) situations and much faster than the SC list (SCL) decoding algorithm. Our addition of a list approach to our proposed algorithm further narrows the error correction performance gap between our TH-OSD and OSD. Our simulation results show that, with appropriate thresholds, our proposed algorithm achieves performance close to OSD’s while testing significantly fewer codewords than OSD, especially with low SNR values. Even a small list is sufficient for TH-OSD to match OSD’s error rate in short-code scenarios. The algorithm can be easily extended to longer code lengths.

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