Method for retrieval of vertical profiles of wind from Stream Line lidar data with allowance that the noise component of recorded signal differs from white noise

2016 ◽  
Author(s):  
I. N. Smalikho ◽  
V. A. Banakh
2020 ◽  
Author(s):  
Sagar Parajuli ◽  
Georgiy Stenchikov ◽  
Alexander Ukhov ◽  
Illia Shevchenko

<p>With the advances in modeling approaches, and the application of satellite and ground-based data in dust-related research, our understanding of the dust cycle is significantly improved in recent decades. However, two aspects of the dust cycle, the vertical profiles and diurnal cycles of dust aerosols have not been understood adequately, mainly due to the sparsity of observations. A micro-pulse LIDAR has been operating at the King Abdullah University of Science and Technology (KAUST) campus located on the east coast of the Red Sea (22.3N, 39.1E), measuring the backscattering from atmospheric aerosols at a high temporal resolution for several years since 2015. It is the only operating LIDAR system over the Arabian Peninsula. We use this LIDAR data together with other collocated observations and high-resolution WRF-Chem model simulations to study the 3-d structure of aerosols, with a focus on dust over the Red Sea Arabian coastal plains. </p><p>Firstly, we investigate the vertical profiles of aerosol extinction and concentration in terms of their seasonal and diurnal variability. Secondly, using the hourly model output and observations, we study the diurnal cycle of aerosols over the site. Thirdly, we explore the interactions between dust aerosols and land/sea breezes, which are the critical components of the local diurnal circulation in the region. </p><p>We found a substantial variation in the vertical profile of aerosols in different seasons. There is also a marked difference in the daytime and nighttime vertical distribution of aerosols in the study site, as shown by LIDAR data. A prominent dust layer is observed at ~5-7km at night in the LIDAR data, corresponding to the long-range transported dust of non-local origin. The vertical profiles of aerosol extinction are consistently reproduced in LIDAR, MERRA-2 reanalysis, and CALIOP data, as well as in WRF-Chem simulations in all seasons. Our results show that the sea breezes are much deeper (~1km) than the land breezes (~200m), and both of them prominently affect the distribution of dust aerosols over the study site. Sea breezes mainly trap the dust aerosols near the coast, brought by the northeasterly trade winds from inland deserts, causing elevated dust maxima at the height of ~1.5km. Also, sea and land breezes intensify dust emissions from the coastal region in daytime and nighttime, respectively. Such dust emissions caused by sea breezes and land breezes are most active in spring and winter. Finally, WRF-Chem successfully captures the onset, demise, and the height of some large-scale dust events as compared to LIDAR data qualitatively. </p>


2013 ◽  
Vol 52 (14) ◽  
pp. 3178 ◽  
Author(s):  
Detlef Müller ◽  
Igor Veselovskii ◽  
Alexei Kolgotin ◽  
Matthias Tesche ◽  
Albert Ansmann ◽  
...  

2020 ◽  
Author(s):  
Sagar P. Parajuli ◽  
Georgiy L. Stenchikov ◽  
Alexander Ukhov ◽  
Illia Shevchenko ◽  
Oleg Dubovik ◽  
...  

Abstract. With advances in modeling approaches and the application of satellite and ground-based data in dust-related research, our understanding of the dust cycle has significantly improved in recent decades. However, two aspects of the dust cycle, namely the vertical profiles and diurnal cycles, are not yet adequately understood, mainly due to the sparsity of direct observations. Measurements of backscattering caused by atmospheric aerosols have been ongoing since 2014 at the King Abdullah University of Science and Technology (KAUST) campus using a micro-pulse LIDAR with a high temporal resolution. KAUST is located on the east coast of the Red Sea (22.3° N, 39.1° E), and currently hosts the only operating LIDAR system in the Arabian Peninsula. We use the data from this LIDAR together with other collocated observations and high-resolution WRF-Chem model simulations to study the following aspects of aerosols, with a focus on dust over the Red Sea Arabian coastal plains. Firstly, we investigate the vertical profiles of aerosol extinction and concentration in terms of their seasonal and diurnal variability. Secondly, we evaluate how well the WRF-Chem model performs in representing the vertical distribution of aerosols over the study site. Thirdly, we explore the interactions between dust aerosols and land/sea breezes, which are the most influential components of the local diurnal circulation in the region. We found a substantial variation in the vertical profile of aerosols in different seasons. We also discovered a marked difference in the daytime and nighttime vertical distribution of aerosols at the study site, as revealed by the LIDAR data. The LIDAR data also identified a prominent dust layer at ∼5–7 km during the nighttime, which represented the long-range transported dust brought to the site by the easterly flow from remote inland deserts. The vertical profiles of aerosol extinction in different seasons were largely consistent between the LIDAR, MERRA-2 reanalysis, and CALIOP data, as well as in the WRF-Chem simulations. The sea breeze circulation was much deeper (∼2 km) than the land breeze circulation (∼1 km), but both breeze systems prominently affected the distribution of dust aerosols over the study site. We observed that sea breezes push the dust aerosols upwards along the western slope of the Sarawat Mountains, which eventually collide with the dust-laden northeasterly trade winds coming from nearby inland deserts, causing elevated dust maxima at a height of ∼1.5 km above sea level over the mountains. Moreover, the sea and land breezes intensified dust emissions from the coastal region during the daytime and nighttime, respectively. The WRF-Chem model successfully captured the onset, demise, and height of a large-scale dust event that occurred in 2015, compared to LIDAR data. Our study, although focused on a particular region, has broader environmental implications as it highlights how aerosols and dust emissions from the coastal plains can affect the Red Sea climate and marine habitats.


2015 ◽  
Vol 8 (5) ◽  
pp. 5363-5424
Author(s):  
M. Iarlori ◽  
F. Madonna ◽  
V. Rizi ◽  
T. Trickl ◽  
A. Amodeo

Abstract. Since its first establishment in 2000, EARLINET (European Aerosol Research Lidar NETwork) has been devoted to providing, through its database, exclusively quantitative aerosol properties, such as aerosol backscatter and aerosol extinction coefficients, the latter only for stations able to retrieve it independently (from Raman or High Spectral Resolution Lidars). As these coefficients are provided in terms of vertical profiles, EARLINET database must also include the details on the range resolution of the submitted data. In fact, the algorithms used in the lidar data analysis often alter the spectral content of the data, mainly working as low pass filters with the purpose of noise damping. Low pass filters are mathematically described by the Digital Signal Processing (DSP) theory as a convolution sum. As a consequence, this implies that each filter's output, at a given range (or time) in our case, will be the result of a linear combination of several lidar input data relative to different ranges (times) before and after the given range (time): a first hint of loss of resolution of the output signal. The application of filtering processes will also always distort the underlying true profile whose relevant features, like aerosol layers, will then be affected both in magnitude and in spatial extension. Thus, both the removal of noise and the spatial distortion of the true profile produce a reduction of the range resolution. This paper provides the determination of the effective resolution (ERes) of the vertical profiles of aerosol properties retrieved starting from lidar data. Large attention has been addressed to provide an assessment of the impact of low-pass filtering on the effective range resolution in the retrieval procedure.


2018 ◽  
Vol 45 (5) ◽  
pp. 418-421 ◽  
Author(s):  
Suliman A. Gargoum ◽  
Karim El-Basyouny ◽  
Amr Shalkamy ◽  
Maged Gouda

Producing as-built drawings is an important task in any road construction project. In fact, in an ideal situation, these drawings must be updated whenever major maintenance work takes place. Unfortunately, constantly updating those drawings is not always feasible due to the amount of manual work associated with the data collection in traditional surveying practice. The increase in computing power and the advancement in technology has led many transportation agencies to consider utilizing remote sensing techniques to extract roadway design features and prepare as-builts of roads. In this note, a procedure to generate as-built drawings of vertical profiles on highways using light detection and ranging (LiDAR) point cloud data are proposed. The procedure is a multistep procedure where the road centerline of each segment is first defined, after that a best fit alignment of points along the road’s centerline is generated. A digital surface model (DSM) of the LiDAR highway is created and the centerline is relayed onto the DSM before generating the road profile. The proposed method is tested using LiDAR data collected on two highways in the province of Alberta, Canada. The profiles extracted using the proposed method are compared against vertical profiles that were generated for the same segments using data collected in GPS surveys and as-built drawings developed in manual surveys. The results show the feasibility of accurately extracting road profiles from LiDAR data. The average difference in grades estimated using the proposed method and the GPS data ranged from 0.023% to 0.061%. In fact, the proposed method was able to capture details in the road profile that were not detected using GPS data, demonstrating the value of using LiDAR for road profile extraction.


2015 ◽  
Vol 8 (12) ◽  
pp. 5157-5176 ◽  
Author(s):  
M. Iarlori ◽  
F. Madonna ◽  
V. Rizi ◽  
T. Trickl ◽  
A. Amodeo

Abstract. Since its establishment in 2000, EARLINET (European Aerosol Research Lidar NETwork) has provided, through its database, quantitative aerosol properties, such as aerosol backscatter and aerosol extinction coefficients, the latter only for stations able to retrieve it independently (from Raman or high-spectral-resolution lidars). These coefficients are stored in terms of vertical profiles, and the EARLINET database also includes the details of the range resolution of the vertical profiles. In fact, the algorithms used in the lidar data analysis often alter the spectral content of the data, mainly acting as low-pass filters to reduce the high-frequency noise. Data filtering is described by the digital signal processing (DSP) theory as a convolution sum: each filtered signal output at a given range is the result of a linear combination of several signal input data samples (relative to different ranges from the lidar receiver), and this could be seen as a loss of range resolution of the output signal. Low-pass filtering always introduces distortions in the lidar profile shape. Thus, both the removal of high frequency, i.e., the removal of details up to a certain spatial extension, and the spatial distortion produce a reduction of the range resolution. This paper discusses the determination of the effective resolution (ERes) of the vertical profiles of aerosol properties retrieved from lidar data. Large attention has been dedicated to providing an assessment of the impact of low-pass filtering on the effective range resolution in the retrieval procedure.


2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Wenkang Gong ◽  
Qi Liu ◽  
Wenhao Du ◽  
Weichen Xu ◽  
Gang Wang

In this paper, we propose a new denoising algorithm for electromagnetic ultrasonic signals based on the improved EEMD method, which can adaptively adjust for added noise and average times in different noisy environments, so that the effect of the residual difference of white noise on the results can be eliminated as far as possible. First, the way to add white noise in the EEMD method is processed, and then the permutation entropy algorithm is used to identify the nature of the components obtained during the decomposition. Then the wavelet transform modulus maximum denoising method is used to deal with the IMF components of the high-frequency part obtained before. Finally, the processed IMF results and residual difference are summed up. The results show that after processing, the noise component in the signal is less and the original information is more reserved, which prevents the signal distortion to a great extent and provides more effective data for subsequent processing. In the experiment, the crack defect data collected by the electromagnetic ultrasonic experiment system were processed by the improved EEMD method. Compared with the traditional EEMD method, it can retain the information of crack location more accurately, which proves the effectiveness of the proposed method.


2018 ◽  
Vol 176 ◽  
pp. 09005
Author(s):  
Tomoaki Nishizawa ◽  
Nobuo Sugimoto ◽  
Atsushi Shimizu ◽  
Itsushi Uno ◽  
Yukari Hara ◽  
...  

We deployed multi-wavelength Mie-Raman lidars (MMRL) at three sites of the AD-Net and have conducted continuous measurements using them since 2013. To analyze the MMRL data and better understand the externally mixing state of main aerosol components (e.g., dust, sea-salt, and black carbon) in the atmosphere, we developed an integrated package of aerosol component retrieval algorithms, which have already been developed or are being developed, to estimate vertical profiles of the aerosol components. This package applies to the other ground-based lidar network data (e.g., EARLINET) and satellite-borne lidar data (e.g., CALIOP/CALIPSO and ATLID/EarthCARE) as well as the other lidar data of the AD-Net.


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