scholarly journals A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data

2022 ◽  
Vol 15 (1) ◽  
pp. 149-164
Author(s):  
Alberto Sorrentino ◽  
Alessia Sannino ◽  
Nicola Spinelli ◽  
Michele Piana ◽  
Antonella Boselli ◽  
...  
Keyword(s):  
Size Distribution ◽  
Synthetic Data ◽  
Monte Carlo Algorithm ◽  
Lidar Data ◽  
Normal Distributions ◽  
Number Size Distribution ◽  
Lidar Measurements ◽  
Log Normal ◽  
Pm2.5 And Pm10 ◽  
The Right

Abstract. We consider the problem of reconstructing the number size distribution (or particle size distribution) in the atmosphere from lidar measurements of the extinction and backscattering coefficients. We assume that the number size distribution can be modeled as a superposition of log-normal distributions, each one defined by three parameters: mode, width and height. We use a Bayesian model and a Monte Carlo algorithm to estimate these parameters. We test the developed method on synthetic data generated by distributions containing one or two modes and perturbed by Gaussian noise as well as on three datasets obtained from AERONET. We show that the proposed algorithm provides good results when the right number of modes is selected. In general, an overestimate of the number of modes provides better results than an underestimate. In all cases, the PM1, PM2.5 and PM10 concentrations are reconstructed with tolerable deviations.

scholarly journals Retrieving the atmospheric number size distribution from lidar data

2021 ◽  
Author(s):  
Alberto Sorrentino ◽  
Alessia Sannino ◽  
Nicola Spinelli ◽  
Michele Piana ◽  
Antonella Boselli ◽  
...  
Keyword(s):  
Size Distribution ◽  
Synthetic Data ◽  
Monte Carlo Algorithm ◽  
Normal Distributions ◽  
Number Size Distribution ◽  
True Number ◽  
Lidar Measurements ◽  
Log Normal ◽  
Pm2.5 And Pm10 ◽  
The Right

Abstract. We consider the problem of reconstructing the number size distribution (or particle size distribution) in the atmosphere from lidar measurements of the extinction and backscattering coefficients. We assume that the number size distribution can be modelled as a superposition of log–normal distributions, each one defined by three parameters: mode, width and height. We use a Bayesian model and a Monte Carlo algorithm to estimate these parameters. We test the developed method on synthetic data generated by distributions containing one or two modes, and perturbed by Gaussian noise, and on three real datasets obtained from AERONET. We show that the proposed algorithm provides satisfactory results even when the assumed number of modes is different from the true number of modes, and substantially excellent results when the right number of modes is selected. In general, an over-estimate of the number of modes provides better results than an under-estimate. In all cases, the PM1, PM2.5 and PM10 concentrations are reconstructed with tolerable deviations.

scholarly journals A Method for Retrieving Stratospheric Aerosol Extinction and Particle Size from Ground-Based Rayleigh-Mie-Raman Lidar Observations

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 773
Author(s):  
Jacob Zalach ◽  
Christian von Savigny ◽  
Arvid Langenbach ◽  
Gerd Baumgarten ◽  
Franz-Josef Lübken ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Stratospheric Aerosol ◽  
Aerosol Extinction ◽  
Raman Lidar ◽  
Size Information ◽  
Lidar Measurements ◽  
Aerosol Particle Size ◽  
Log Normal

We report on the retrieval of stratospheric aerosol particle size and extinction coefficient profiles from multi-color backscatter measurements with the Rayleigh–Mie–Raman lidar operated at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) in northern Norway. The retrievals are based on a two-step approach. In a first step, the median radius of an assumed monomodal log-normal particle size distribution with fixed width is retrieved based on a color index formed from the measured backscatter ratios at the wavelengths of 1064 nm and 532 nm. An intrinsic ambiguity of the retrieved aerosol size information is discussed. In a second step, this particle size information is used to convert the measured lidar backscatter ratio to aerosol extinction coefficients. The retrieval is currently based on monthly-averaged lidar measurements and the results for March 2013 are discussed. A sensitivity study is presented that allows for establishing an error budget for the aerosol retrievals. Assuming a monomodal log-normal aerosol particle size distribution with a geometric width of S = 1.5, median radii on the order of below 100 nm are retrieved. The median radii are found to generally decrease with increasing altitude. The retrieved aerosol extinction profiles are compared to observations with the OSIRIS (Optical Spectrograph and InfraRed Imager System) and the OMPS-LP (Ozone Mapping Profiling Suite Limb Profiler) satellite instruments in the 60∘ N to 80∘ N latitude band. The extinction profiles that were retrieved from the lidar measurements show good agreement with the observations of the two satellite instruments when taking the different wavelengths of the instruments into account.

Log-normal distributions in parasitology

1984 ◽  
Vol 93 (6) ◽  
pp. 591-598 ◽  
Author(s):  
Sandeep K Malhotra
Keyword(s):  
Normal Distributions ◽  
Log Normal

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Deriving Tree Size Distributions of Tropical Forests from Lidar

2021 ◽  
Vol 13 (1) ◽  
pp. 131
Author(s):  
Franziska Taubert ◽  
Rico Fischer ◽  
Nikolai Knapp ◽  
Andreas Huth
Keyword(s):  
Size Distribution ◽  
Spatial Scales ◽  
Basal Area ◽  
Tree Height ◽  
Tree Size ◽  
Diameter Distribution ◽  
Forest Inventories ◽  
Allometric Relations ◽  
Lidar Measurements ◽  
Derived Data

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

Number size distribution of particulate emissions of heavy-duty engines in real world test cycles

2003 ◽  
Vol 37 (37) ◽  
pp. 5247-5259 ◽  
Author(s):  
Urs Lehmann ◽  
Martin Mohr ◽  
Thomas Schweizer ◽  
Josef Rütter
Keyword(s):  
Size Distribution ◽  
Real World ◽  
Particulate Emissions ◽  
Heavy Duty ◽  
Number Size Distribution

scholarly journals Size-resolved and bulk activation properties of aerosols in the North China Plain

2011 ◽  
Vol 11 (8) ◽  
pp. 3835-3846 ◽  
Author(s):  
Z. Z. Deng ◽  
C. S. Zhao ◽  
N. Ma ◽  
P. F. Liu ◽  
L. Ran ◽  
...  
Keyword(s):  
Size Distribution ◽  
North China Plain ◽  
North China ◽  
Cloud Condensation Nuclei ◽  
Number Concentration ◽  
Anthropogenic Aerosol ◽  
Number Size Distribution ◽  
The North ◽  
The North China Plain ◽  
Activation Ratio

Abstract. Size-resolved and bulk activation properties of aerosols were measured at a regional/suburban site in the North China Plain (NCP), which is occasionally heavily polluted by anthropogenic aerosol particles and gases. A Cloud Condensation Nuclei (CCN) closure study is conducted with bulk CCN number concentration (NCCN) and calculated CCN number concentration based on the aerosol number size distribution and size-resolved activation properties. The observed CCN number concentration (NCCN-obs) are higher than those observed in other locations than China, with average NCCN-obs of roughly 2000, 3000, 6000, 10 000 and 13 000 cm−3 at supersaturations of 0.056, 0.083, 0.17, 0.35 and 0.70%, respectively. An inferred critical dry diameter (Dm) is calculated based on the NCCN-obs and aerosol number size distribution assuming homogeneous chemical composition. The inferred cut-off diameters are in the ranges of 190–280, 160–260, 95–180, 65–120 and 50–100 nm at supersaturations of 0.056, 0.083, 0.17, 0.35 and 0.7%, with their mean values 230.1, 198.4, 128.4, 86.4 and 69.2 nm, respectively. Size-resolved activation measurements show that most of the 300 nm particles are activated at the investigated supersaturations, while almost no particles of 30 nm are activated even at the highest supersaturation of 0.72%. The activation ratio increases with increasing supersaturation and particle size. The slopes of the activation curves for ambient aerosols are not as steep as those observed in calibrations with ammonium sulfate suggesting that the observed aerosols is an external mixture of more hygroscopic and hydrophobic particles. The calculated CCN number concentrations (NCCN-calc) based on the size-resolved activation ratio and aerosol number size distribution correlate well with the NCCN-obs, and show an average overestimation of 19%. Sensitivity studies of the CCN closure show that the NCCN at each supersaturation is well predicted with the campaign average of size-resolved activation curves. These results indicate that the aerosol number size distribution is critical in the prediction of possible CCN. The CCN number concentration can be reliably estimated using time-averaged, size-resolved activation efficiencies without accounting for the temporal variations.

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