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 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 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 Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events

2012 ◽  
Vol 12 (19) ◽  
pp. 9057-9077 ◽  
Author(s):  
P. Wang ◽  
O. N. E. Tuinder ◽  
L. G. Tilstra ◽  
M. de Graaf ◽  
P. Stammes
Keyword(s):  
Trace Gas ◽  
Aerosol Layer ◽  
Satellite Measurements ◽  
Dominant Effect ◽  
Cloud Data ◽  
Retrieval Scheme ◽  
Absorbing Aerosols ◽  
Aerosol Effects ◽  
Aerosol Index ◽  
Aerosol Types

Abstract. Cloud and aerosol information is needed in trace gas retrievals from satellite measurements. The Fast REtrieval Scheme for Clouds from the Oxygen A band (FRESCO) cloud algorithm employs reflectance spectra of the O2 A band around 760 nm to derive cloud pressure and effective cloud fraction. In general, clouds contribute more to the O2 A band reflectance than aerosols. Therefore, the FRESCO algorithm does not correct for aerosol effects in the retrievals and attributes the retrieved cloud information entirely to the presence of clouds, and not to aerosols. For events with high aerosol loading, aerosols may have a dominant effect, especially for almost cloud free scenes. We have analysed FRESCO cloud data and Absorbing Aerosol Index (AAI) data from the Global Ozone Monitoring Experiment (GOME-2) instrument on the Metop-A satellite for events with typical absorbing aerosol types, such as volcanic ash, desert dust and smoke. We find that the FRESCO effective cloud fractions are correlated with the AAI data for these absorbing aerosol events and that the FRESCO cloud pressure contains information on aerosol layer pressure. For cloud free scenes, the derived FRESCO cloud pressure is close to the aerosol layer pressure, especially for optically thick aerosol layers. For cloudy scenes, if the strongly absorbing aerosols are located above the clouds, then the retrieved FRESCO cloud pressure may represent the height of the aerosol layer rather than the height of the clouds. Combining FRESCO and AAI data, an estimate for the aerosol layer pressure can be given.

scholarly journals Classifying aerosol type using in situ surface spectral aerosol optical properties

2017 ◽  
Vol 17 (19) ◽  
pp. 12097-12120 ◽  
Author(s):  
Lauren Schmeisser ◽  
Elisabeth Andrews ◽  
John A. Ogren ◽  
Patrick Sheridan ◽  
Anne Jefferson ◽  
...  
Keyword(s):  
Optical Properties ◽  
Carbonaceous Aerosol ◽  
Spatiotemporal Variability ◽  
Classification Methods ◽  
Aerosol Optical Properties ◽  
Angstrom Exponent ◽  
Ångström Exponent ◽  
Aerosol Classification ◽  
Ångstrom Exponent ◽  
Aerosol Type

Abstract. Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatiotemporal variability in aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of the scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol type using previously published aerosol classification schemes.Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station.The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations.

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 The Cluster Low-Streams Regression Method for Fast Computations of Top-of-the-Atmosphere Radiances in Absorption Bands

2020 ◽  
pp. short25-1-short25-9
Author(s):  
Ana del Aguila ◽  
Dmitry Efremenko
Keyword(s):  
Performance Enhancement ◽  
Regression Method ◽  
Atmospheric Composition ◽  
Trace Gas ◽  
Huge Amount ◽  
Absorption Bands ◽  
Clear Sky ◽  
Median Error ◽  
On Line ◽  
Aerosol Types

Atmospheric composition sensors provide a huge amount of data. A key component of trace gas retrieval algorithms are radiative transfer models (RTMs), which are used to simulate the spectral radiances in the absorption bands. Accurate RTMs based on line-by-line techniques are time-consuming. In this paper we analyze the efficiency of the cluster low-streams regression (CLSR) technique to accelerate computations in the absorption bands. The idea of the CLRS method is to use the fast two-stream RTM model in conjunction with the line-by-line model and then to refine the results by constructing the regression model between two- and multi-stream RTMs. The CLSR method is applied to the Hartley-Huggins, O2 A-, water vapour and CO2 bands for the clear sky and several aerosol types. The median error of the CLSR method is below 0.001 %, the interquartile range (IQR) is below 0.1 %, while the performance enhancement is two orders of magnitude.

scholarly journals A NOVEL INDEX FOR ATMOSPHERIC AEROSOL TYPES CATEGORIZATION WITH SPECTRAL OPTICAL DEPTHS FROM SATELLITE RETRIEVAL

2016 ◽  
Vol XLI-B8 ◽  
pp. 277-279 ◽  
Author(s):  
Tang-Huang Lin ◽  
Gin-Rong Liu ◽  
Chian-Yi Liu
Keyword(s):  
Atmospheric Aerosols ◽  
Single Scattering ◽  
Anthropogenic Pollutants ◽  
Satellite Retrieval ◽  
Modis Aod ◽  
Mineral Dusts ◽  
Normalized Gradient ◽  
Aerosol Index ◽  
Aerosol Types ◽  
Aerosol Type

In general, the type of atmospheric aerosols can be efficiently identified with the characteristics of optical properties, such as Ångström exponent (AE) and single scattering albedo (SSA). However, the retrieval of SSA is not frequently available to global area which may cause the difficulty in the identification of aerosol type. Since aerosol optical depth (AOD) can be easily requested, a novel index in terms of AOD, Normalized Gradient Aerosol Index (NGAI), is proposed to get over the constraint on SSA providing. With the NGAI derived from MODIS AOD products, the type of atmospheric aerosols can be clearly categorized between mineral dusts, biomass burning and anthropogenic pollutants. The results of aerosol type categorization show the well agreement with the ground-based observations (AERONET) in AE and SSA properties, implying that the proposed index equips highly practical for the application of aerosols type categorization by means of remote sensing. In addition, the fraction of AOD compositions can be potentially determined according to the value of index after compared with the products of CALIPSO Aerosol Subtype.

Aerosol classification using sun photometer and lidar data within a machine learning algorithm-a possible nowcasting application

2021 ◽  
Author(s):  
Joelle Buxmann ◽  
Martin Osborne ◽  
Mike Protts ◽  
Debbie O'Sullivan
Keyword(s):  
Machine Learning ◽  
Biomass Burning ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Training Set ◽  
Angstrom Exponent ◽  
Ångström Exponent ◽  
Aerosol Classification ◽  
Aerosol Types ◽  
Ångstrom Exponent

<p>The Met Office operates a ground based operational network of nine polarisation Raman lidars (aerosol profiling instruments) and sun photometers (column integrated information). An aerosol classification scheme using supervised machine learning has been developed. The concept of Mahalanobis (~normalized) distance to identify the aerosol type  from individual Aerosol Robotic Network (AERONET) measurements including Extinction Angstrom Exponent, Absorption Angstrom Exponent, Single Scattering Albedo and Index of refraction is used for a subset of AERONET stations around the globe of known main aerosol types (training set). The aerosol types  include maritime, urban industrial, biomass burning and dust. We build a predictive model from this training set using K nearest neighbour machine learning algorithms. The relation of particle polarisation ratio and lidar ratio from the Raman lidar is used as a sanity check.  We apply the model to 3- 4 years of AERONET and profiling data across the UK, with instruments evenly distributed across the country, from Camborne in Cornwall to Lerwick in the Shetland Islands. We are showing more detailed data of a dust event in May 2016, dust/biomass burning aerosol mix from October 2017 (hurricane Ophelia) and more recent aerosol transported from the Canadian wild fires in September 2020. AERONET Level 2.0  data is compared to level 1.5 in order to determine the implications for the aerosol classification. Level 1.5 data are cloud-screened, but not quality assured and may not have the final calibration applied. Level 2.0  data have pre- and post-field calibration applied, are cloud-screened, and quality-assured data. As level 2.0 data is usually only available after 1-2 years (after a new calibration has been performed), it is important to understand the  usefulness of more readily available level 1.5 (cloud screened) data.</p><p>The aim is to build a real time aerosol classification application that can be used in Nowcasting.</p>

scholarly journals Classifying aerosol type using in situ surface spectral aerosol optical properties

2017 ◽  
Author(s):  
Lauren Schmeisser ◽  
Elisabeth Andrews ◽  
John A. Ogren ◽  
Patrick Sheridan ◽  
Anne Jefferson ◽  
...  
Keyword(s):  
Optical Properties ◽  
Carbonaceous Aerosol ◽  
Classification Methods ◽  
Aerosol Optical Properties ◽  
Classification Schemes ◽  
Angstrom Exponent ◽  
Ångström Exponent ◽  
Aerosol Classification ◽  
Ångstrom Exponent ◽  
Aerosol Type

Abstract. Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources, and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatio-temporal variability of aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA federated aerosol network to infer aerosol type using previously published aerosol classification schemes. Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics, and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station. The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt), and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites, and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations.

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