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Good concept mastery through critical thinking using various approaches, models, strategies, and methods of learning German is essential for the Education 4.0 era. Therefore, this study aimed to analyze the concept mastery and critical thinking skills of German language students at Pattimura University using collaborative learning that combines Schneeball-Wirbelgruppe learning. A one-group pretest-posttest design with a sample of 13 second-semester students in the academic year 2019/2020 was used. Furthermore, an essay test was used as an instrument while the data was analyzed using descriptive methods (N-gain test) and inferential methods (paired t-test). The descriptive analysis showed that the concept mastery and critical thinking skills of students were in the high and moderate categories. The paired t-test results showed a difference between the pretest and posttest using Schneeball-Wirbelgruppe learning model. Therefore, collaborative learning has a positive impact on improving students' concept mastery and critical thinking skills in learning phonology and morphology courses. As for the implications of this research, the Schneeball-Wirbelgruppe learning model is recommended as a learning model that can be applied to other concepts in German language learning.

In this study, we utilised ground-based in situ measurements of the aerosol chemical composition and particle size distribution, along with meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF), to predict vertical profiles of aerosol optical properties, including the aerosol scattering coefficient, backscatter coefficient, extinction coefficient, and lidar ratio. The predicted ambient profiles were compared to retrievals by a multi-wavelength Raman lidar during the Ruisdael Land–Atmosphere Interactions Intensive Trace-gas and Aerosol (RITA) campaign in the Netherlands in 2021 for 26 time periods of approximately 1 h each. Predicted and retrieved extensive aerosol properties (scattering, backscatter, and extinction coefficient) were comparable only approximately 35 % of the time, mostly under the condition of well-mixed boundary layers. In this case, ground-based measurements can provide a way to extend extinction profiles to lower altitudes, where they cannot be retrieved, and to verify the lidar-measured profiles. Accurate representation of hygroscopic growth is required for adjusting the dry size distribution to ambient size distribution, and the estimated relative humidity profile may have a substantial influence on the shape of the calculated profiles. On the other hand, the lidar ratio profiles predicted by ground-based data also compared reasonably well to the retrieved lidar profiles (starting at 800 m) for conditions where the predicted and retrieved backscatter profiles differed considerably. The difference in the predicted and retrieved lidar ratio is usually less than ±30 %. Our study thus shows that, for well-mixed boundary layers, a representative lidar ratio can be estimated from ground-based in situ measurements of chemical composition and dry size distribution. This approach offers a method of providing lidar ratios calculated from independent in situ measurements for simple backscatter lidars or at times when Raman lidar profiles cannot be measured (e.g. during the daytime). It only uses data that are routinely available at aerosol measurement stations and is therefore not only useful for further validating lidar measurements but also for bridging the gap between in situ measurements and lidar remote sensing.

A novel atmospheric layer detection approach has been developed based on deep learning techniques for image segmentation. Our proof of concept estimated the layering in the atmosphere, distinguishing between pollution-rich layers closer to the surface and cleaner layers aloft. Knowledge of the spatio-temporal development of atmospheric layers, such as the mixing boundary layer height (MBLH), is important for the dispersion of air pollutants and greenhouse gases, as well as for assessing the performance of numerical weather prediction systems. Existing lidar-based layer detection algorithms typically do not use the full resolution of the available data, require manual feature engineering, often do not enforce temporal consistency of the layers, and lack the ability to be applied in near-real time. To address these limitations, our Deep-Pathfinder algorithm represents the MBLH profile as a mask and directly predicts it from an image with backscatter lidar observations. Deep-Pathfinder was applied to range-corrected signal data from Lufft CHM15k ceilometers at five locations of the operational ceilometer network in the Netherlands. Input samples of 224 × 224 px were extracted, each covering a 45 min observation period. A customised U-Net architecture was developed with a nighttime indicator and MobileNetV2 encoder for fast inference times. The model was pre-trained on 19.4×106 samples of unlabelled data and fine-tuned using 50 d of high-resolution annotations. Qualitative and quantitative results showed competitive performance compared to two reference methods: the Lufft and STRATfinder algorithms, applied to the same dataset. Deep-Pathfinder enhances temporal consistency and provides near-real-time estimates at full spatial and temporal resolution. These properties make our approach valuable for application in operational networks, as near-real-time and high-resolution MBLH detection better meets the requirements of users, such as in aviation, weather forecasting, and air quality monitoring.

Me ro' tradition mandates families of a recently deceased person to abstain from consuming rice and entering rice fields until the deceased is buried. However, executing this tradition is considered challenging due to rice’s position as staple food with religious significance. Furthermore, the difficulty of adhering to Me ro' lies in the virtue of self-management of the family members. This exploratory study uses an observational method and in-depth interviews to collect information. Triangulation, involving documentation, recording, and peer-reviewed sources, is employed to ensure the validity, impartiality, and ethicality of the information. Two elders from Simbuang Village and two students who are originally from the village, but have studied outside of Simbuang Village serves as the informants of this study. The result of this study reveals Me ro' tradition to require stringent procedures, social sanctions, and economic obligations, which renders it to be challenging to fulfil. Moreover, to successfully accomplish Me ro', it depends on psychological, social, existential determinants, and lifestyle factors. Therefore, practicing Me ro' is not solely contingent on individual self-discipline, but also relies on the community's involvement to encourage, motivate, and supervise the Me ro' practice. In conclusion, it relies upon individual’s self-management and the community's ability to collectively support and supervise in the procedures.

Multicultural education in a diverse cultural society is one of the important aspects to be addressed in order that it is not viewed as a threat in the current era of education 4.0. This research aims at examining the implementation of multicultural education in German language learning, especially in the Aufbaustufe A2 course. The samples of this research were second semester students of class A in the Study Program of German Language Education with the total of 20 students. The instruments used in this research were questionnaires, interviews and document review. The data of the results of the questionnaire, interviews, and document review were analyzed by using descriptive statistics. The results of this research show that students are aware of their existence and also the existence of other students who have different cultural backgrounds. In addition, they respect their own culture even though they are also open to learning and understanding German culture.

Abstract. This article is an effort in developing the church ecclesiology as an ecological community. The effort was based on the reality of ecological damage that is currently occurring, especially in the Kei Islands-Southeast Maluku. To develop the church ecclesiology as an ecological community, Colossians 1:15-20 was interpreted using cosmic hermeneutics so that there is a fusion of horizons between the theological meaning of the text and the context of ecological damage in Kei islands- Moluccas. Based on the interpretation process, Colossians 1:15-20 showed the Christ-cosmic privilege so that God's concern for the life of all creation on earth is seen. The Christ-cosmic meaning that cares for the life of all creation then makes every believer aware of respecting, preserving, and maintaining the earth. Abstrak. Artikel ini merupakan pengembangan eklesiologi gereja sebagai komunitas ekologis. Upaya tersebut didasarkan pada kenyataan kerusakan ekologis yang sementara terjadi, terkhususnya di Kepulauan Kei-Maluku. Dalam upaya pengembangan eklesiologi gereja sebagai komunitas ekologis, teks Kolose 1:15-20 diinterpretasi menggunakan hermeneutik kosmis sehingga terjadi fusi horison antara makna teologi teks dengan konteks kerusakan ekologis di Kei-Maluku Tenggara. Berdasarkan proses penafsiran, Kolose 1:15-20 memperlihatkan keistimewaan Kristus-kosmis sehingga terlihat kepedulian Allah terhadap kehidupan seluruh ciptaan di bumi. Makna Kristus-kosmis yang peduli terhadap kehidupan seluruh ciptaan kemudian menyadarkan setiap orang percaya untuk menghormati, melestarikan, dan memelihara bumi.

Introduction: Cases of domestic violence are increasing in Indonesia, this is a manifestation of the unequal power relations between men and women and is also influenced by the culture/customs of the community, one of which is the Tobelo woman in the Hibualamo customs. Purposes of the Research: This study aims to explain the concept of Restorative Justice in the Indonesian criminal law system and the Restorative Justice Model in Resolving Domestic Violence Cases against Tobelo women. Methods of the Research: The research method used is a combination of empirical-normative legal research. The approach uses a socio-legal approach (social science) and a statutory approach (statute approach). Results of the Research: The results of this study include: 1. There is negative labeling of women in Tobelo customary law in the misunderstanding of the Hibualamo concept; 2. The concept of Restorative justice based on Hibualamo ideology which contains noble values that are fair and moral can be the basis for resolving cases of domestic violence in Tobelo to create harmonization and prevent multi-interpretation gaps in the implementation of restorative justice

A TROPOspheric Monitoring Instrument (TROPOMI) validation campaign was held in the Netherlands based at the CESAR (Cabauw Experimental Site for Atmospheric Research) observatory during September 2019. The TROpomi vaLIdation eXperiment (TROLIX-19) consisted of active and passive remote sensing platforms in conjunction with several balloon-borne and surface chemical (e.g., ozone and nitrogen dioxide) measurements. The goal of this joint NASA-KNMI geophysical validation campaign was to make intensive observations in the TROPOMI domain in order to be able to establish the quality of the L2 satellite data products under realistic conditions, such as non-idealized conditions with varying cloud cover and a range of atmospheric conditions at a rural site. The research presented here focuses on using ozone lidars from NASA's Goddard Space Flight Center to better evaluate the characterization of ozone throughout TROLIX-19. Results of comparisons to the lidar systems with balloon, space-borne and ground-based passive measurements are shown. In addition, results are compared to a global coupled chemistry meteorology model to illustrate the vertical variability and columnar amounts of both tropospheric and stratospheric ozone during the campaign period.

Abstract. The narrative of Genesis 22:1-19 has been the object of debate among Old Testament scholars. Even in this context, Genesis 22:1-19 is often used to legitimize actions that are not pro-life. Therefore, this paper aimed to reinterpret Genesis 22:1-19 using narrative criticism with a pro-life spirituality perspective. In this study, it was found that Genesis 22:1-19 narrative intends to emphasize the idea of pro-life, which was initiated by God. In the threat of religious rituals, God reveals His work of salvation. In the end, the pro-life idea in the Genesis 22:1-19 narrative can provide today’s inspiration in struggling for life for the entire creation. Abstrak. Narasi Kejadian 22:1-19 telah menjadi salah satu bahan perdebatan di kalangan ahli Perjanjian Lama. Bahkan, dalam konteks masa kini, teks tersebut sering dijadikan legitimasi terhadap tindakan yang tidak pro hidup. Oleh karena itu, tulisan ini bertujuan untuk menafsir kembali Kejadian 22:1-19 menggunakan kritik naratif dengan perspektif spiritualitas pro hidup. Dalam kajian ini ditemukan bahwa Kejadian 22:1-19 hendak mempertegas gagasan pro hidup, yang dipelopori oleh Allah. Di tengah-tengah ancaman ritual-ritual keagamaan, Allah menyatakan karya keselamatan-Nya. Pada akhirnya, gagasan pro hidup dalam narasi Kejadian 22:1-19 dapat memberikan inspirasi dalam konteks masa kini tentang pentingnya memperjuangkan hidup seluruh ciptaan.

The quantum learning model is a combination of various interactions that occur during the learning process. This learning model can be combined with the Assoziogram technique or also with audio-visual media as learning media. This research aims at investigating the implementation of a digital media-based quantum learning model with the Assoziogramm technique/concept map to improve students’ German writing, critical thinking, and learning motivation. This research uses one group pre-test and post-test experimental research design with a total sample of 20 students. The research data were collected using test and non-test (questionnaire) instruments. The research data were analysed using descriptive statistics and paired t-tests. The study results show an increase in the ability to write German, critical thinking, and learning motivation after the implementation of the digital media-based quantum learning model with the Assoziogramm technique. This study contributed to showing how variations in learning are crucial in providing a more engaging learning climate than the conventional learning done by lecturers.

Good concept mastery through critical thinking using various approaches, models, strategies, and methods of learning German is essential for the Education 4.0 era. Therefore, this study aimed to analyze the concept mastery and critical thinking skills of German language students at Pattimura University using collaborative learning that combines Schneeball-Wirbelgruppe learning. A one-group pretest-posttest design with a sample of 13 second-semester students in the academic year 2019/2020 was used. Furthermore, an essay test was used as an instrument while the data was analyzed using descriptive methods (N-gain test) and inferential methods (paired t-test). The descriptive analysis showed that the concept mastery and critical thinking skills of students were in the high and moderate categories. The paired t-test results showed a difference between the pretest and posttest using Schneeball-Wirbelgruppe learning model. Therefore, collaborative learning has a positive impact on improving students' concept mastery and critical thinking skills in learning phonology and morphology courses. As for the implications of this research, the Schneeball-Wırbelgruppe learning model is recommended as a learning model that can be applied to other concepts in German language learning. [ABSTRACT FROM AUTHOR]

The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986, last access: 5 November 2021). The alert products developed by the EUNADICS-AV EWS, i.e. near-real-time (NRT) observations, email notifications and netCDF (Network Common Data Form) alert data products (called NCAP files), have shown significant interest in using selective detection of natural airborne hazards from polar-orbiting satellites. The combination of several sensors inside a single global system demonstrates the advantage of using a triggered approach to obtain selective detection from observations, which cannot initially discriminate the different aerosol types. Satellite products from hyperspectral ultraviolet–visible (UV–vis) and infrared (IR) sensors (e.g. TROPOMI – TROPOspheric Monitoring Instrument – and IASI – Infrared Atmospheric Sounding Interferometer) and a broadband geostationary imager (Spinning Enhanced Visible and InfraRed Imager; SEVIRI) and retrievals from ground-based networks (e.g. EARLINET – European Aerosol Research Lidar Network, E-PROFILE and the regional network from volcano observatories) are combined by our system to create tailored alert products (e.g. selective ash detection, SO2 column and plume height, dust cloud, and smoke from wildfires). A total of 23 different alert products are implemented, using 1 geostationary and 13 polar-orbiting satellite platforms, 3 external existing service, and 2 EU and 2 regional ground-based networks. This allows for the identification and the tracking of extreme events. The EUNADICS-AV EWS has also shown the need to implement a future relay of radiological data (gamma dose rate and radionuclides concentrations in ground-level air) in the case of a nuclear accident. This highlights the interest of operating early warnings with the use of a homogenised dataset. For the four types of airborne hazard, the EUNADICS-AV EWS has demonstrated its capability to provide NRT alert data products to trigger data assimilation and dispersion modelling providing forecasts and inverse modelling for source term estimate. Not all of our alert data products (NCAP files) are publicly disseminated. Access to our alert products is currently restricted to key users (i.e. Volcanic Ash Advisory Centres, national meteorological services, the World Meteorological Organization, governments, volcano observatories and research collaborators), as these are considered pre-decisional products. On the other hand, thanks to the EUNADICS-AV–SACS (Support to Aviation Control Service) web interface (https://sacs.aeronomie.be, last access: 5 November 2021), the main part of the satellite observations used by the EUNADICS-AV EWS is shown in NRT, with public email notification of volcanic emission and delivery of tailored images and NCAP files. All of the ATM stakeholders (e.g. pilots, airlines and passengers) can access these alert products through this free channel.

In this study, we utilised ground-based in situ measurements of the aerosol chemical composition and particle size distribution, along with meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF), to predict vertical profiles of aerosol optical properties, including the aerosol scattering coefficient, backscatter coefficient, extinction coefficient, and lidar ratio. The predicted ambient profiles were compared to retrievals by a multi-wavelength Raman lidar during the Ruisdael Land–Atmosphere Interactions Intensive Trace-gas and Aerosol (RITA) campaign in the Netherlands in 2021 for 26 time periods of approximately 1 h each. Predicted and retrieved extensive aerosol properties (scattering, backscatter, and extinction coefficient) were comparable only approximately 35 % of the time, mostly under the condition of well-mixed boundary layers. In this case, ground-based measurements can provide a way to extend extinction profiles to lower altitudes, where they cannot be retrieved, and to verify the lidar-measured profiles. Accurate representation of hygroscopic growth is required for adjusting the dry size distribution to ambient size distribution, and the estimated relative humidity profile may have a substantial influence on the shape of the calculated profiles. On the other hand, the lidar ratio profiles predicted by ground-based data also compared reasonably well to the retrieved lidar profiles (starting at 800 m) for conditions where the predicted and retrieved backscatter profiles differed considerably. The difference in the predicted and retrieved lidar ratio is usually less than ±30 %. Our study thus shows that, for well-mixed boundary layers, a representative lidar ratio can be estimated from ground-based in situ measurements of chemical composition and dry size distribution. This approach offers a method of providing lidar ratios calculated from independent in situ measurements for simple backscatter lidars or at times when Raman lidar profiles cannot be measured (e.g. during the daytime). It only uses data that are routinely available at aerosol measurement stations and is therefore not only useful for further validating lidar measurements but also for bridging the gap between in situ measurements and lidar remote sensing. [ABSTRACT FROM AUTHOR]

A 2-year measurement campaign of the ZephIR 300 vertical profiling continuous-wave (CW) focusing wind lidar has been carried out by the Royal Netherlands Meteorological Institute (KNMI) at the Cabauw site. We focus on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213 m tall meteorological mast. We find an overall availability of quality-controlled wind lidar data of 97 % to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm h−1 or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m s−1 with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. The mean bias in the wind direction is within 2∘, which is of the same order as the combined uncertainty in the alignment of the wind lidars and the mast wind vanes. The well-known 180∘ error in the wind direction output for this type of instrument occurs about 9 % of the time. A correction scheme based on data of an auxiliary wind vane at a height of 10 m is applied, leading to a reduction of the 180∘ error below 2 %. This scheme can be applied in real-time applications in the situation that a nearby freely exposed mast with wind direction measurements at a single height is available.

The second Cabauw Intercomparison of Nitrogen Dioxide measuring Instruments (CINDI-2) took place in Cabauw (the Netherlands) in September 2016 with the aim of assessing the consistency of multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of tropospheric species (NO2, HCHO, O3, HONO, CHOCHO and O4). This was achieved through the coordinated operation of 36 spectrometers operated by 24 groups from all over the world, together with a wide range of supporting reference observations (in situ analysers, balloon sondes, lidars, long-path DOAS, direct-sun DOAS, Sun photometer and meteorological instruments). In the presented study, the retrieved CINDI-2 MAX-DOAS trace gas (NO2, HCHO) and aerosol vertical profiles of 15 participating groups using different inversion algorithms are compared and validated against the colocated supporting observations, with the focus on aerosol optical thicknesses (AOTs), trace gas vertical column densities (VCDs) and trace gas surface concentrations. The algorithms are based on three different techniques: six use the optimal estimation method, two use a parameterized approach and one algorithm relies on simplified radiative transport assumptions and analytical calculations. To assess the agreement among the inversion algorithms independent of inconsistencies in the trace gas slant column density acquisition, participants applied their inversion to a common set of slant columns. Further, important settings like the retrieval grid, profiles of O3, temperature and pressure as well as aerosol optical properties and a priori assumptions (for optimal estimation algorithms) have been prescribed to reduce possible sources of discrepancies. The profiling results were found to be in good qualitative agreement: most participants obtained the same features in the retrieved vertical trace gas and aerosol distributions; however, these are sometimes at different altitudes and of different magnitudes. Under clear-sky conditions, the root-mean-square differences (RMSDs) among the results of individual participants are in the range of 0.01–0.1 for AOTs, (1.5–15) ×1014molec.cm-2 for trace gas (NO2, HCHO) VCDs and (0.3–8)×1010molec.cm-3 for trace gas surface concentrations. These values compare to approximate average optical thicknesses of 0.3, trace gas vertical columns of 90×1014molec.cm-2 and trace gas surface concentrations of 11×1010molec.cm-3 observed over the campaign period. The discrepancies originate from differences in the applied techniques, the exact implementation of the algorithms and the user-defined settings that were not prescribed. For the comparison against supporting observations, the RMSDs increase to a range of 0.02–0.2 against AOTs from the Sun photometer, (11–55)×1014molec.cm-2 against trace gas VCDs from direct-sun DOAS observations and (0.8–9)×1010molec.cm-3 against surface concentrations from the long-path DOAS instrument. This increase in RMSDs is most likely caused by uncertainties in the supporting data, spatiotemporal mismatch among the observations and simplified assumptions particularly on aerosol optical properties made for the MAX-DOAS retrieval. As a side investigation, the comparison was repeated with the participants retrieving profiles from their own differential slant column densities (dSCDs) acquired during the campaign. In this case, the consistency among the participants degrades by about 30 % for AOTs, by 180 % (40 %) for HCHO (NO2) VCDs and by 90 % (20 %) for HCHO (NO2) surface concentrations. In former publications and also during this comparison study, it was found that MAX-DOAS vertically integrated aerosol extinction coefficient profiles systematically underestimate the AOT observed by the Sun photometer. For the first time, it is quantitatively shown that for optimal estimation algorithms this can be largely explained and compensated by considering biases arising from the reduced sensitivity of MAX-DOAS observations to higher altitudes and associated a priori assumptions.

We present the inter-comparison of delta slant column densities (SCDs) and vertical profiles of nitrous acid (HONO) derived from measurements of different multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments and using different inversion algorithms during the Second Cabauw Inter-comparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) in September 2016 at Cabauw, the Netherlands (51.97∘ N, 4.93∘ E). The HONO vertical profiles, vertical column densities (VCDs), and near-surface volume mixing ratios are compared between different MAX-DOAS instruments and profile inversion algorithms for the first time. Systematic and random discrepancies of the HONO results are derived from the comparisons of all data sets against their median values. Systematic discrepancies of HONO delta SCDs are observed in the range of ±0.3×1015 molec. cm−2, which is half of the typical random discrepancy of 0.6×1015 molec. cm−2. For a typical high HONO delta SCD of 2×1015 molec. cm−2, the relative systematic and random discrepancies are about 15 % and 30 %, respectively. The inter-comparison of HONO profiles shows that both systematic and random discrepancies of HONO VCDs and near-surface volume mixing ratios (VMRs) are mostly in the range of ∼±0.5×1014 molec. cm−2 and ∼±0.1 ppb (typically ∼20 %). Further we find that the discrepancies of the retrieved HONO profiles are dominated by discrepancies of the HONO delta SCDs. The profile retrievals only contribute to the discrepancies of the HONO profiles by ∼5 %. However, some data sets with substantially larger discrepancies than the typical values indicate that inappropriate implementations of profile inversion algorithms and configurations of radiative transfer models in the profile retrievals can also be an important uncertainty source. In addition, estimations of measurement uncertainties of HONO dSCDs, which can significantly impact profile retrievals using the optimal estimation method, need to consider not only DOAS fit errors, but also atmospheric variability, especially for an instrument with a DOAS fit error lower than ∼3×1014 molec. cm−2. The MAX-DOAS results during the CINDI-2 campaign indicate that the peak HONO levels (e.g. near-surface VMRs of ∼0.4 ppb) often appeared in the early morning and below 0.2 km. The near-surface VMRs retrieved from the MAX-DOAS observations are compared with those measured using a co-located long-path DOAS instrument. The systematic differences are smaller than 0.15 and 0.07 ppb during early morning and around noon, respectively. Since true HONO values at high altitudes are not known in the absence of real measurements, in order to evaluate the abilities of profile inversion algorithms to respond to different HONO profile shapes, we performed sensitivity studies using synthetic HONO delta SCDs simulated by a radiative transfer model with assumed HONO profiles. The tests indicate that the profile inversion algorithms based on the optimal estimation method with proper configurations can reproduce the different HONO profile shapes well. Therefore we conclude that the features of HONO accumulated near the surface derived from MAX-DOAS measurements are expected to represent the ambient HONO profiles well.

In September 2016, 36 spectrometers from 24 institutes measured a number of key atmospheric pollutants for a period of 17 d during the Second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) that took place at Cabauw, the Netherlands (51.97∘ N, 4.93∘ E). We report on the outcome of the formal semi-blind intercomparison exercise, which was held under the umbrella of the Network for the Detection of Atmospheric Composition Change (NDACC) and the European Space Agency (ESA). The three major goals of CINDI-2 were (1) to characterise and better understand the differences between a large number of multi-axis differential optical absorption spectroscopy (MAX-DOAS) and zenith-sky DOAS instruments and analysis methods, (2) to define a robust methodology for performance assessment of all participating instruments, and (3) to contribute to a harmonisation of the measurement settings and retrieval methods. This, in turn, creates the capability to produce consistent high-quality ground-based data sets, which are an essential requirement to generate reliable long-term measurement time series suitable for trend analysis and satellite data validation. The data products investigated during the semi-blind intercomparison are slant columns of nitrogen dioxide (NO2), the oxygen collision complex (O4) and ozone (O3) measured in the UV and visible wavelength region, formaldehyde (HCHO) in the UV spectral region, and NO2 in an additional (smaller) wavelength range in the visible region. The campaign design and implementation processes are discussed in detail including the measurement protocol, calibration procedures and slant column retrieval settings. Strong emphasis was put on the careful alignment and synchronisation of the measurement systems, resulting in a unique set of measurements made under highly comparable air mass conditions. The CINDI-2 data sets were investigated using a regression analysis of the slant columns measured by each instrument and for each of the target data products. The slope and intercept of the regression analysis respectively quantify the mean systematic bias and offset of the individual data sets against the selected reference (which is obtained from the median of either all data sets or a subset), and the rms error provides an estimate of the measurement noise or dispersion. These three criteria are examined and for each of the parameters and each of the data products, performance thresholds are set and applied to all the measurements. The approach presented here has been developed based on heritage from previous intercomparison exercises. It introduces a quantitative assessment of the consistency between all the participating instruments for the MAX-DOAS and zenith-sky DOAS techniques.

A novel atmospheric layer detection approach has been developed based on deep learning techniques for image segmentation. Our proof of concept estimated the layering in the atmosphere, distinguishing between pollution-rich layers closer to the surface and cleaner layers aloft. Knowledge of the spatio-temporal development of atmospheric layers, such as the mixing boundary layer height (MBLH), is important for the dispersion of air pollutants and greenhouse gases, as well as for assessing the performance of numerical weather prediction systems. Existing lidar-based layer detection algorithms typically do not use the full resolution of the available data, require manual feature engineering, often do not enforce temporal consistency of the layers, and lack the ability to be applied in near-real time. To address these limitations, our Deep-Pathfinder algorithm represents the MBLH profile as a mask and directly predicts it from an image with backscatter lidar observations. Deep-Pathfinder was applied to range-corrected signal data from Lufft CHM15k ceilometers at five locations of the operational ceilometer network in the Netherlands. Input samples of 224 × 224 px were extracted, each covering a 45 min observation period. A customised U-Net architecture was developed with a nighttime indicator and MobileNetV2 encoder for fast inference times. The model was pre-trained on 19.4×106 samples of unlabelled data and fine-tuned using 50 d of high-resolution annotations. Qualitative and quantitative results showed competitive performance compared to two reference methods: the Lufft and STRATfinder algorithms, applied to the same dataset. Deep-Pathfinder enhances temporal consistency and provides near-real-time estimates at full spatial and temporal resolution. These properties make our approach valuable for application in operational networks, as near-real-time and high-resolution MBLH detection better meets the requirements of users, such as in aviation, weather forecasting, and air quality monitoring. [ABSTRACT FROM AUTHOR]

The research aimed to analyze the effect of several different liquid smokes on the fatty acid profile of smoked tuna. The liquid smokes were processed from coconut shells, sago bark, and Eucalyptus sp. Fatty acids were analyzed using gas chromatography and the lipid quality of smoked tuna was determined by computing the Atherogenic Index (AI) and Thrombogenic Index (TI). The results showed that the fatty acids profile of smoked tuna contained 28 fatty acids, 11 saturated and 17 unsaturated fatty acids. The polyunsaturated fatty acid (PUFA) content of smoked tuna was greater than the saturated fatty acid and monounsaturated fatty acid contents. The high ratio of PUFA-ω3/PUFA-ω6 and the low value of the AI and TI of the smoked tuna with liquid smoke from sago bark showed that its lipid quality was better than the lipid quality of control tuna and the other smoked tuna.

We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) aboard the International Space Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter measurements within a 50 km distance between the ground station and the ISS overpass and as close in time as possible, typically with the starting time or stopping time of the EARLINET performed measurement time window within 90 min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are analyzed here to demonstrate the performance of the CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions, CATS is in good agreement with EARLINET, independent of daytime and nighttime conditions. CATS low negative biases are observed, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds; these are biases which may lead to systematic deviations and slight underestimations of the total aerosol optical depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low negative biases in CATS backscatter coefficient profiles, of the order of 6.1 %, indicate the good nighttime performance of CATS. During daytime, a reduced signal-to-noise ratio by solar background illumination prevents retrievals of weakly scattering atmospheric layers that would otherwise be detectable during nighttime, leading to higher negative biases, of the order of 22.3 %.

We present a new MAX-DOAS profiling algorithm for aerosols and trace gases, BOREAS, which utilizes an iterative solution method including Tikhonov regularization and the optimal estimation technique. The aerosol profile retrieval is based on a novel approach in which the absorption depth of O4 is directly used in order to retrieve extinction coefficient profiles instead of the commonly used perturbation theory method. The retrieval of trace gases is done with the frequently used optimal estimation method but significant improvements are presented on how to deal with wrongly weighted a priori constraints and for scenarios in which the a priori profile is inaccurate. Performance tests are separated into two parts. First, we address the general sensitivity of the retrieval to the example of synthetic data calculated with the radiative transfer model SCIATRAN. In the second part of the study, we demonstrate BOREAS profiling accuracy by validating the results with the help of ancillary measurements carried out during the CINDI-2 campaign in Cabauw, the Netherlands, in 2016. The synthetic sensitivity tests indicate that the regularization between measurement and a priori constraints is insufficient when knowledge of the true state of the atmosphere is poor. We demonstrate a priori pre-scaling and extensive regularization tests as a tool for the optimization of retrieved profiles. The comparison of retrieval results with in situ, ceilometer, NO2 lidar, sonde and long-path DOAS measurements during the CINDI-2 campaign always shows high correlations with coefficients greater than 0.75. The largest differences can be found in the morning hours, when the planetary boundary layer is not yet fully developed and the concentration of trace gases and aerosol, as a result of a low night-time boundary layer having formed, is focused in a shallow, near-surface layer.

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.

This study aims to examine the relationship between the rupiah exchange rate and inflation rates on CSPI in Indonesia Stock Exchange. The data used are secondary data of Rupiah exchange rate toward US Dollar and inflation rate data obtained from Bank Indonesia publication through www.bi.go.id website and movement data of CSPI through website www.finance.yahoo.com. Determination of the sample based on monthly time series data period December 2011 - January 2014 by using the method of saturated samples, i.e. as many as 48 samples. The data obtained were analyzed by multiple linear regression approaches. There was a negative influence of inflation rate on CSPI for the period of December 2011 - January 2014 with the regression coefficient of 0.279; the negative value indicates that there is a negative correlation of inflation rate to CSPI. While the relationship between the Rupiah exchange rate To CSPI showed a positive relationship with the regression coefficient of 0.888.

This paper presents a weighted least squares approach to retrieve aerosol layer height from top-of-atmosphere reflectance measurements in the oxygen A band (758–770 nm) over bright surfaces. A property of the measurement error covariance matrix is discussed, due to which photons travelling from the surface are given a higher preference over photons that scatter back from the aerosol layer. This is a potential source of biases in the estimation of aerosol properties over land, which can be mitigated by revisiting the design of the measurement error covariance matrix. The alternative proposed in this paper, which we call the dynamic scaling method, introduces a scene-dependent and wavelength-dependent modification in the measurement signal-to-noise ratio in order to influence this matrix. This method is generally applicable to other retrieval algorithms using weighted least squares. To test this method, synthetic experiments are done in addition to application to GOME-2A and GOME-2B measurements of the oxygen A band over the August 2010 Russian wildfires and the October 2017 Portugal wildfire plume over western Europe.

This overview paper highlights the successes of the Ozone Monitoring Instrument (OMI) on board the Aura satellite spanning a period of nearly 14 years. Data from OMI has been used in a wide range of applications and research resulting in many new findings. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. With the operational very fast delivery (VFD; direct readout) and near real-time (NRT) availability of the data, OMI also plays an important role in the development of operational services in the atmospheric chemistry domain.

The height of the atmospheric boundary layer or mixing layer is an important parameter for understanding the dynamics of the atmosphere and the dispersion of trace gases and air pollution. The height of the mixing layer (MLH) can be retrieved, among other methods, from lidar or ceilometer backscatter data. These instruments use the vertical backscatter lidar signal to infer MLHL, which is feasible because the main sources of aerosols are situated at the surface and vertical gradients are expected to go from the aerosol loaded mixing layer close to the ground to the cleaner free atmosphere above. Various lidar/ceilometer algorithms are currently applied, but accounting for MLH temporal development is not always well taken care of. As a result, MLHL retrievals may jump between different atmospheric layers, rather than reliably track true MLH development over time. This hampers the usefulness of MLHL time series, e.g. for process studies, model validation/verification and climatology. Here, we introduce a new method pathfinder, which applies graph theory to simultaneously evaluate time frames that are consistent with scales of MLH dynamics, leading to coherent tracking of MLH. Starting from a grid of gradients in the backscatter profiles, MLH development is followed using Dijkstra's shortest path algorithm (Dijkstra, 1959). Locations of strong gradients are connected under the condition that subsequent points on the path are limited to a restricted vertical range. The search is further guided by rules based on the presence of clouds and residual layers. After being applied to backscatter lidar data from Cabauw, excellent agreement is found with wind profiler retrievals for a 12-day period in 2008 (R2 = 0.90) and visual judgment of lidar data during a full year in 2010 (R2 = 0.96). These values compare favourably to other MLHL methods applied to the same lidar data set and corroborate more consistent MLH tracking by pathfinder.