scholarly journals Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

2021 ◽  
Vol 13 (13) ◽  
pp. 2464
Author(s):  
Wonei Choi ◽  
Hyeongwoo Kang ◽  
Dongho Shin ◽  
Hanlim Lee
Keyword(s):  
Random Forest ◽  
Classification Model ◽  
Seasonal Effects ◽  
Classification Models ◽  
Capital Cities ◽  
Microphysical Properties ◽  
Aerosol Classification ◽  
Absorbing Aerosols ◽  
Aerosol Types ◽  
Aerosol Type

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations.

scholarly journals Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model

2021 ◽  
Vol 13 (7) ◽  
pp. 1268
Author(s):  
Wonei Choi ◽  
Hanlim Lee ◽  
Daewon Kim ◽  
Serin Kim
Keyword(s):  
Random Forest ◽  
Wavelength Dependence ◽  
Single Scattering ◽  
Global Scale ◽  
Satellite Measurement ◽  
Spatial Coverage ◽  
Aerosol Classification ◽  
Input Variables ◽  
Aerosol Types ◽  
Aerosol Type

The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models.

scholarly journals Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask

2013 ◽  
Vol 6 (1) ◽  
pp. 1815-1858 ◽  
Author(s):  
S. P. Burton ◽  
R. A. Ferrare ◽  
M. A. Vaughan ◽  
A. H. Omar ◽  
R. R. Rogers ◽  
...  
Keyword(s):  
High Spectral Resolution ◽  
Dependent Observations ◽  
Aerosol Retrieval ◽  
Direct Measurements ◽  
Aerosol Classification ◽  
Lidar Ratio ◽  
Langley Research Center ◽  
High Spectral Resolution Lidar ◽  
Aerosol Types ◽  
Aerosol Type

Abstract. Aerosol classification products from the NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL-1) on the NASA B200 aircraft are compared with coincident V3.01 aerosol classification products from the CALIOP instrument on the CALIPSO satellite. For CALIOP, aerosol classification is a key input to the aerosol retrieval, and must be inferred using aerosol loading-dependent observations and location information. In contrast, HSRL-1 makes direct measurements of aerosol intensive properties, including the lidar ratio, that provide information on aerosol type. In this study, comparisons are made for 109 underflights of the CALIOP orbit track. We find that 62% of the CALIOP marine layers and 54% of the polluted continental layers agree with HSRL-1 classification results. In addition, 80% of the CALIOP desert dust layers are classified as either dust or dusty mix by HSRL-1. However, agreement is less for CALIOP smoke (13%) and polluted dust (35%) layers. Specific case studies are examined, giving insight into the performance of the CALIOP aerosol type algorithm. In particular, we find that the CALIOP polluted dust type is overused due to an attenuation-related depolarization bias. Furthermore, the polluted dust type frequently includes mixtures of dust plus marine aerosol. Finally, we find that CALIOP's identification of internal boundaries between different aerosol types in contact with each other frequently do not reflect the actual transitions between aerosol types accurately. Based on these findings, we give recommendations which may help to improve the CALIOP aerosol type algorithms.

scholarly journals Aerosol classification using airborne High Spectral Resolution Lidar measurements – methodology and examples

2012 ◽  
Vol 5 (1) ◽  
pp. 73-98 ◽  
Author(s):  
S. P. Burton ◽  
R. A. Ferrare ◽  
C. A. Hostetler ◽  
J. W. Hair ◽  
R. R. Rogers ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
High Spectral Resolution ◽  
Color Ratio ◽  
Lidar Measurements ◽  
Aerosol Classification ◽  
Langley Research Center ◽  
High Spectral Resolution Lidar ◽  
Aerosol Types ◽  
532 Nm ◽  
Aerosol Type

Abstract. The NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) on the NASA B200 aircraft has acquired extensive datasets of aerosol extinction (532 nm), aerosol optical depth (AOD) (532 nm), backscatter (532 and 1064 nm), and depolarization (532 and 1064 nm) profiles during 18 field missions that have been conducted over North America since 2006. The lidar measurements of aerosol intensive parameters (lidar ratio, depolarization, backscatter color ratio, and spectral depolarization ratio) are shown to vary with location and aerosol type. A methodology based on observations of known aerosol types is used to qualitatively classify the extensive set of HSRL aerosol measurements into eight separate types. Several examples are presented showing how the aerosol intensive parameters vary with aerosol type and how these aerosols are classified according to this new methodology. The HSRL-based classification reveals vertical variability of aerosol types during the NASA ARCTAS field experiment conducted over Alaska and northwest Canada during 2008. In two examples derived from flights conducted during ARCTAS, the HSRL classification of biomass burning smoke is shown to be consistent with aerosol types derived from coincident airborne in situ measurements of particle size and composition. The HSRL retrievals of AOD and inferences of aerosol types are used to apportion AOD to aerosol type; results of this analysis are shown for several experiments.

scholarly journals A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances

2015 ◽  
Vol 15 (18) ◽  
pp. 10597-10618 ◽  
Author(s):  
M. J. M. Penning de Vries ◽  
S. Beirle ◽  
C. Hörmann ◽  
J. W. Kaiser ◽  
P. Stammes ◽  
...  
Keyword(s):  
Classification Algorithm ◽  
Atmospheric Composition ◽  
Trace Gas ◽  
Aerosol Composition ◽  
Ångström Exponent ◽  
Aerosol Classification ◽  
Aerosol Index ◽  
Aerosol Types ◽  
Good Agreement ◽  
Aerosol Type

Abstract. Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broadband effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction Ångström exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (Ångström exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5° × 5° regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g., AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.

scholarly journals Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Shan Zeng ◽  
Ali Omar ◽  
Mark Vaughan ◽  
Macarena Ortiz ◽  
Charles Trepte ◽  
...  
Keyword(s):  
Machine Learning ◽  
Semantic Segmentation ◽  
Retrieval Algorithm ◽  
Orthogonal Polarization ◽  
Ice Clouds ◽  
Microphysical Properties ◽  
Texture Information ◽  
Vertical Distributions ◽  
Aerosol Classification ◽  
Aerosol Types

The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms.

scholarly journals A Short Note on the Potential of Utilization of Spectral AERONET-Derived Depolarization Ratios for Aerosol Classification

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 143 ◽  
Author(s):  
Il-Sung Zo ◽  
Sung-Kyun Shin
Keyword(s):  
Short Note ◽  
Single Scattering ◽  
Observation Data ◽  
Spectral Variation ◽  
Aerosol Classification ◽  
Fine Mode ◽  
Remote Sensing Techniques ◽  
Aerosol Types ◽  
Aerosol Type ◽  
Depolarization Ratios

We herein present the spectral linear particle depolarization ratios (δp) from an Aerosol Robotics NETwork (AERONET) sun/sky radiometer with respect to the aerosol type. AERONET observation sites, which are representative of each aerosol type, were selected for our study. The observation data were filtered using the Ångström exponent (Å), fine-mode fraction (FMF) and single scattering albedo (ω) to ensure that the obtained values of δp were representative of each aerosol condition. We report the spectral δp values provided in the recently released AERONET version 3 inversion product for observation of the following aerosol types: dust, polluted dust, smoke, non-absorbing, moderately-absorbing and high-absorbing pollution. The AERONET-derived δp values were generally within the range of the δp values measured from lidar observations for each aerosol type. In addition, it was found that the spectral variation of δp differed according to the aerosol type. From the obtained results, we concluded that our findings provide potential insight into the identification and classification of aerosol types using remote sensing techniques.

scholarly journals LAND USE AND LAND COVER CLASSIFICATION USING HYPERSPECTRAL IMAGERY: EVALUATING THE PERFORMANCE OF SPECTRAL ANGLE MAPPER, SUPPORT VECTOR MACHINE AND RANDOM FOREST

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1841-1847 ◽  
Author(s):  
L. E. Christovam ◽  
G. G. Pessoa ◽  
M. H. Shimabukuro ◽  
M. L. B. T. Galo
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Random Forest ◽  
Land Cover ◽  
Principal Components ◽  
Classification Model ◽  
Support Vector ◽  
Classification Models ◽  
Spectral Angle Mapper ◽  
Spectral Angle

<p><strong>Abstract.</strong> Land Use and Land Cover (LULC) information is an important data source for modeling environmental variables, so it is essential to develop high quality LULC maps. The hundreds of continuous spectral bands gathered with hyperspectral sensors provide high spectral detail and consequently confirm hyperspectral remote sensing as an appropriate option for many LULC applications. Despite increased spectral detail, issues like high dimensionality, huge volume of data and redundant information, mean that hyperspectral image classification is a complex task. It is therefore essential to develop classification approaches that deals with these issues. Since classification results are directly dependent on the dataset used, it is fundamental to compare and validate the classification approaches in public datasets. With this in mind, aiming to provide a baseline, four classification models in the relatively new hyperspectral HyRANK dataset were evaluated. The classification models were defined with three well-known classification algorithms: Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Random Forest (RF). A classification model with SAM and another with RF were defined with the 176 surface reflectance bands. A dimensionality reduction with principal component analysis was carried out and a classification model with SVM and another with RF were defined using 14 principal components as features. The results show that SVM and RF algorithms outperformed by far the SAM in terms of accuracy, and that the RF is slightly better than the SVM in this respect. It is also possible to see from the results that the use of principal components as features provided an improvement in the accuracy of the RF and an improvement of 28% in the time spent fitting the classification model.</p>

scholarly journals Seasonality Effect Analysis and Recognition of Charging Behaviors of Electric Vehicles: A Data Science Approach

2020 ◽  
Vol 12 (18) ◽  
pp. 7769
Author(s):  
Juan A. Dominguez-Jimenez ◽  
Javier E. Campillo ◽  
Oscar Danilo Montoya ◽  
Enrique Delahoz ◽  
Jesus C. Hernández
Keyword(s):  
Random Forest ◽  
Electric Vehicles ◽  
Data Science ◽  
Area Under The Curve ◽  
Principal Component ◽  
Poisson Model ◽  
Seasonal Effects ◽  
Percentage Error ◽  
Classification Models ◽  
Effect Analysis

Electric vehicles (EVs) presence in the power grid can bring about pivotal concerns regarding their energy requirements. EVs charging behaviors can be affected by several aspects including socio-economics, psychological, seasonal among others. This work proposes a case study to analyze seasonal effects on charging patterns, using a public real-world based dataset that contains information from the aggregated load of the total charging stations of Boulder, Colorado. Our approach targets to forecast and recognize EVs demand considering seasonal factors. Principal component analysis (PCA) was used to provide a visual representation of the variables and their contribution and the correlation among them. Then, twelve classification models were trained and tested to discriminate among seasons the charging load of electric vehicles. Later, a benchmark stage is presented for regression as well as for classification results. For regression models, examined through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), the random Forest provides better prediction than quasi-Poisson model widely. However, it was observed that for large variations in electric vehicles’ charging load, quasi-Poisson fits better than random forest. For the classification models, evaluated through Accuracy and the Area under the Curve, the Lasso and elastic-net regularized generalized linear (GLMNET) model provided the best global performance with accuracy up to 100% when evaluated on the test dataset. The results of this work offer great insights for enhancing demand response strategies that involve PEV charging regarding charging habits across seasons.

scholarly journals A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances

2015 ◽  
Vol 15 (9) ◽  
pp. 13551-13605
Author(s):  
M. J. M. Penning de Vries ◽  
S. Beirle ◽  
C. Hörmann ◽  
J. W. Kaiser ◽  
P. Stammes ◽  
...  
Keyword(s):  
Classification Algorithm ◽  
Atmospheric Composition ◽  
Trace Gas ◽  
Aerosol Composition ◽  
Ångström Exponent ◽  
Aerosol Classification ◽  
Aerosol Index ◽  
Aerosol Types ◽  
Good Agreement ◽  
Aerosol Type

Abstract. Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broad-band effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction Ångström exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (Ångström exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5° × 5° regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g. AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.

scholarly journals Impact of absorbing and non-absorbing aerosols on radiation and low-level clouds over the Southeast Atlantic from co-located satellite observations

2020 ◽  
Author(s):  
Alejandro Baró Pérez ◽  
Abhay Devasthale ◽  
Frida Bender ◽  
Annica M. L. Ekman
Keyword(s):  
Satellite Observation ◽  
Clear Correlation ◽  
Heating Rates ◽  
Cooling Rates ◽  
Thermodynamic Structure ◽  
Aerosol Loading ◽  
Stratocumulus Clouds ◽  
Aerosol Classification ◽  
Absorbing Aerosols ◽  
Aerosol Type

Abstract. We use data derived from instruments onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat satellites as well as meteorological parameters from reanalysis to explore situations when moist aerosol layers overlie stratocumulus clouds over the Southeast Atlantic during the biomass burning season (June to October). One main goal is to separate and quantify the impacts of aerosol loading, aerosol type, and humidity on the radiative fluxes, including cloud top cooling. To achieve our objectives we split the data into different levels of aerosol and moisture loadings. By using the aerosol classification available from the CALIPSO products, we also separate and compare situations with pristine air, with smoke, and with other (mixed) types of aerosols. We find a substantial number of cases with mixed aerosols above clouds that occur under similar meteorological conditions as the smoke cases. In contrast, the meteorology is substantially different for the pristine situations, making a direct comparison with the aerosol cases ambiguous. The moisture content is enhanced within the aerosol layers, but we do not find a monotonous increase of the relative humidity with increasing aerosol optical depth. Shortwave (SW) heating rates within the moist aerosol plumes increase with increasing aerosol loading and are higher in the smoke cases compared to the mixed cases. However, there is no clear correlation between moisture changes and SW absorption. Cloud top cooling rates tend to decrease with increasing moisture within the overlying aerosol layers, but the influence is relatively weak and confounded by the strong variability of the cooling rates caused by other meteorological factors (most notably cloud top temperature). No clear influence of aerosol type or loading on cloud top cooling rates is detected. We also do not find any correlation between aerosol loading and the thermodynamic structure of the atmosphere nor the cloud top height, i.e. no indication of a semi-direct aerosol effect. This result is consistent with previous studies that examined clearly separated aerosol and cloud layers (in our case at least 0.4 km).

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