Showing papers by "Matthias Wiegner published in 2018"

MOPSMAP v1.0: a versatile tool for the modeling of aerosol optical properties

Abstract: . The spatiotemporal distribution and characterization of aerosol particles are usually determined by remote-sensing and optical in situ measurements. These measurements are indirect with respect to microphysical properties, and thus inversion techniques are required to determine the aerosol microphysics. Scattering theory provides the link between microphysical and optical properties; it is not only needed for such inversions but also for radiative budget calculations and climate modeling. However, optical modeling can be very time-consuming, in particular if nonspherical particles or complex ensembles are involved. In this paper we present the MOPSMAP package (Modeled optical properties of ensembles of aerosol particles), which is computationally fast for optical modeling even in the case of complex aerosols. The package consists of a data set of pre-calculated optical properties of single aerosol particles, a Fortran program to calculate the properties of user-defined aerosol ensembles, and a user-friendly web interface for online calculations. Spheres, spheroids, and a small set of irregular particle shapes are considered over a wide range of sizes and refractive indices. MOPSMAP provides the fundamental optical properties assuming random particle orientation, including the scattering matrix for the selected wavelengths. Moreover, the output includes tables of frequently used properties such as the single-scattering albedo, the asymmetry parameter, or the lidar ratio. To demonstrate the wide range of possible MOPSMAP applications, a selection of examples is presented, e.g., dealing with hygroscopic growth, mixtures of absorbing and non-absorbing particles, the relevance of the size equivalence in the case of nonspherical particles, and the variability in volcanic ash microphysics. The web interface is designed to be intuitive for expert and nonexpert users. To support users a large set of default settings is available, e.g., several wavelength-dependent refractive indices, climatologically representative size distributions, and a parameterization of hygroscopic growth. Calculations are possible for single wavelengths or user-defined sets (e.g., of specific remote-sensing application). For expert users more options for the microphysics are available. Plots for immediate visualization of the results are shown. The complete output can be downloaded for further applications. All input parameters and results are stored in the user's personal folder so that calculations can easily be reproduced. The web interface is provided at https://mopsmap.net (last access: 9 July 2018) and the Fortran program including the data set is freely available for offline calculations, e.g., when large numbers of different runs for sensitivity studies are to be made.

An automatic observation-based aerosol typing method for EARLINET

Abstract: . We present an automatic aerosol classification method based solely on the European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each unclassified measurement to a predefined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined 8 aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with literature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyze the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59 % (minimum) and 90 % (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in the literature further improves the accuracy with values for all the aerosol classes around 80 %. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite.

Evaluation of ECMWF-IFS (version 41R1) operational model forecasts of aerosol transport by using ceilometer network measurements

Abstract: . In this paper, we present a comparison of model simulations of aerosol profiles with measurements of the ceilometer network operated by the German Weather Service (DWD) over 1 year from September 2015 to August 2016. The aerosol forecasts are produced by the Copernicus Atmosphere Monitoring Service (CAMS) using the aerosol module developed within the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) and Monitoring Atmospheric Composition and Climate (MACC) projects and coupled into the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS). As the model output provides mass mixing ratios of different types of aerosol, whereas the ceilometers do not, it is necessary to determine a common physical quantity for the comparison. We have chosen the attenuated backscatter β∗ for this purpose. The β∗ profiles are calculated from the mass mixing ratios of the model output assuming the inherent aerosol microphysical properties. Comparison of the attenuated backscatter averaged between an altitude of 0.2 km (typical overlap range of ceilometers) and 1 km in general shows similar annual average values. However, the standard deviation of the difference between model and observation is larger than the average in 8 out of 12 sites. To investigate possible reasons for the differences, we have examined the role of the hygroscopic growth of particles and the particle shape. Our results show that using a more recent particle growth model would result in a ∼22 % reduction of particle backscatter for sea salt aerosols, corresponding to a 10 % reduction of the total backscatter signal on average. Accounting for nonspherical dust particles in the model would reduce attenuated backscatter of dust particles by ∼30 %. As the concentration of dust aerosol is in general very low in Germany, a significant effect on the total backscatter signal is restricted to dust episodes. In summary, consideration of both effects tends to improve the agreement between model and observations but without leading to a perfect consistency. In addition, a strong Saharan dust event was investigated to study the agreement of the spatiotemporal distribution of particles. It was found that the arrival time of the dust layer and its vertical extent very well agree between model and ceilometer measurements for several stations. This underlines the potential of a network of ceilometers to validate the dispersion of aerosol layers.

Evaluation of operational model forecasts of aerosol transport usingceilometer network measurements

Abstract: . In this paper, we present a comparison of European Centre for Medium-Range Weather Forecast Integrated Forecast System (ECMWF-IFS) model simulation of aerosol backscatter profiles with measurements of the ceilometer network operated by the German weather service (DWD) over 1 year from September 2015 to August 2016. As the model output provides mass mixing ratios of different types of aerosol whereas the ceilometers don't, it is necessary to determine a common physical quantity for the comparison. We have chosen the attenuated backscatter β * for this purpose. The β *-profiles are calculated from the mass mixing ratios of the model output assuming the inherent aerosol microphysical properties. Comparison of the attenuated backscatter, averaged between an altitude from 0.2 km (typical overlap range of ceilometers) and 1 km, showed slightly larger values from the model. To investigate possible reasons for the differences, we have examined the role of the hygroscopic growth of particles and the particle shape. Our results show that using a more recent particle growth model would result in a ~ 22 % reduction of particle backscatter for sea salt aerosols, corresponding to a 10 %-reduction of the total backscatter signal on average. Accounting for non-spherical dust particles in the model would reduce attenuated backscatter of dust particles by ~ 30 %. As the concentration of dust aerosol is in general very low in Germany, a significant effect on the total backscatter signal is restricted to dust episodes. In summary, consideration of both effects tend to improve the agreement between model and observations, but without leading to a perfect consistency. In addition a case study was conducted to investigate the agreement of the spatiotemporal distribution of particles. It was found that for a dust episode in April 2016 the arrival time of the dust layer and its vertical extent very well agree between model and ceilometer measurements for several stations. However, due to the large set of parameters characterizing the aerosol distribution and the complexity of the ceilometer retrieval an automated and quantitative comparison scheme for β *-profiles is still missing. Consequently, the representativeness of the case study remains open.

An automatic aerosol classification for earlinet: application and results

Abstract: Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented.

An automatic observation-based typing method for EARLINET

Abstract: . We present an automatic aerosol classification method based solely on European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each un-classified measurement to a pre-defined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined eight aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with literature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyse the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59 % (minimum) and 90 % (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in literature further improves the accuracy: the accuracy range is 69–93 % from 8 (69 %) to 4 (93 %) aerosol classes, respectively. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the Single Calculus Chain (SCC), the EARLINET centralised processing suite.