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- ItemGlyoxal and methylglyoxal in Atlantic seawater and marine aerosol particles: Method development and first application during the polarstern cruise ANT XXVII/4(München : European Geopyhsical Union, 2013) van Pinxteren, M.; Herrmann, H.An analytical method for the determination of the alpha dicarbonyls glyoxal (GLY) and methylglyoxal (MGLY) from seawater and marine aerosol particles is presented. The method is based on derivatization with o-(2,3,4,5,6-Pentafluorobenzyl)-hydroxylamine (PFBHA) reagent, solvent extraction and GC-MS (SIM) analysis. The method showed good precision (RSD < 10%), sensitivity (detection limits in the low ng L−1 range), and accuracy (good agreement between external calibration and standard addition). The method was applied to determine GLY and MGLY in oceanic water sampled during the Polarstern cruise ANT XXVII/4 from Capetown to Bremerhaven in spring 2011. GLY and MGLY were determined in the sea surface microlayer (SML) of the ocean and corresponding bulk water (BW) with average concentrations of 228 ng L−1 (GLY) and 196 ng L−1 (MGLY). The results show a significant enrichment (factor of 4) of GLY and MGLY in the SML. Furthermore, marine aerosol particles (PM1) were sampled during the cruise and analyzed for GLY (average concentration 0.19 ng m−3) and MGLY (average concentration 0.15 ng m−3). On aerosol particles, both carbonyls show a very good correlation with oxalate, supporting the idea of a secondary formation of oxalic acid via GLY and MGLY. Concentrations of GLY and MGLY in seawater and on aerosol particles were correlated to environmental parameters such as global radiation, temperature, distance to the coastline and biological activity. There are slight hints for a photochemical production of GLY and MGLY in the SML (significant enrichment in the SML, higher enrichment at higher temperature). However, a clear connection of GLY and MGLY to global radiation as well as to biological activity cannot be concluded from the data. A slight correlation between GLY and MGLY in the SML and in aerosol particles could be a hint for interactions, in particular of GLY, between seawater and the atmosphere.
- ItemPeakTree: A framework for structure-preserving radar Doppler spectra analysis(Göttingen : Copernicus GmbH, 2019) Radenz, M.; Bühl, J.; Seifert, P.; Griesche, H.; Engelmann, R.Clouds are frequently composed of more than one particle population even at the smallest scales. Cloud radar observations frequently contain information on multiple particle species in the observation volume when there are distinct peaks in the Doppler spectrum. Multi-peaked situations are not taken into account by established algorithms, which only use moments of the Doppler spectrum. In this study, we propose a new algorithm that recursively represents the subpeaks as nodes in a binary tree. Using this tree data structure to represent the peaks of a Doppler spectrum, it is possible to drop all a priori assumptions on the number and arrangement of subpeaks. The approach is rigid, unambiguous and can provide a basis for advanced analysis methods. The applicability is briefly demonstrated in two case studies, in which the tree structure was used to investigate particle populations in Arctic multilayered mixed-phase clouds, which were observed during the research vessel Polarstern expedition PS106 and the Atmospheric Radiation Measurement Program BAECC campaign.
- ItemTarget categorization of aerosol and clouds by continuous multiwavelength-polarization lidar measurements(Katlenburg-Lindau : Copernicus, 2017) Baars, Holger; Seifert, Patric; Engelmann, Ronny; Wandinger, UllaAbsolute calibrated signals at 532 and 1064 nm and the depolarization ratio from a multiwavelength lidar are used to categorize primary aerosol but also clouds in high temporal and spatial resolution. Automatically derived particle backscatter coefficient profiles in low temporal resolution (30 min) are applied to calibrate the lidar signals. From these calibrated lidar signals, new atmospheric parameters in temporally high resolution (quasi-particle-backscatter coefficients) are derived. By using thresholds obtained from multiyear, multisite EARLINET (European Aerosol Research Lidar Network) measurements, four aerosol classes (small; large, spherical; large, non-spherical; mixed, partly nonspherical) and several cloud classes (liquid, ice) are defined. Thus, particles are classified by their physical features (shape and size) instead of by source. The methodology is applied to 2 months of continuous observations (24 h a day, 7 days a week) with the multiwavelength-Raman-polarization lidar PollyXT during the High-Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE) in spring 2013. Cloudnet equipment was operated continuously directly next to the lidar and is used for comparison. By discussing three 24 h case studies, it is shown that the aerosol discrimination is very feasible and informative and gives a good complement to the Cloudnet target categorization. Performing the categorization for the 2-month data set of the entire HOPE campaign, almost 1 million pixel (5 min×30 m) could be analysed with the newly developed tool. We find that the majority of the aerosol trapped in the planetary boundary layer (PBL) was composed of small particles as expected for a heavily populated and industrialized area. Large, spherical aerosol was observed mostly at the top of the PBL and close to the identified cloud bases, indicating the importance of hygroscopic growth of the particles at high relative humidity. Interestingly, it is found that on several days non-spherical particles were dispersed from the ground into the atmosphere.
- ItemSpatiotemporal data analysis with chronological networks([London] : Nature Publishing Group UK, 2020) Ferreira, Leonardo N.; Vega-Oliveros, Didier A.; Cotacallapa, Moshé; Cardoso, Manoel F.; Quiles, Marcos G.; Zhao, Liang; Macau, Elbert E. N.The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically. Strong links in the network represent consecutive recurrent events between cells. The chronnet construction process is fast, making the model suitable to process large data sets. Using artificial and real data sets, we show how chronnets can capture data properties beyond simple statistics, like frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Therefore, we conclude that chronnets represent a robust tool for the analysis of spatiotemporal data sets.