Influences of deep convective cloud systems on tropospheric trace gases and photochemistry over the tropical West Pacific

A numerical model for studying the influences of deep convective cloud systems on photochemistry was developed based on a non-hydrostatic meteorological model and chemistry from a global chemistry transport model. The transport of trace gases, the scavenging of soluble trace gases, and the influences of lightning produced nitrogen oxides (NOx=NO+NO2) on the local ozone-related photochemistry were investigated in a multi-day case study for an oceanic region located in the tropical western Pacific. Model runs considering influences of large scale flows, previously neglected in multi-day cloud resolving and single column model studies of tracer transport, yielded that the influence of the mesoscale subsidence (between clouds) on trace gas transport was considerably overestimated in these studies. The simulated vertical transport and scavenging of highly soluble tracers were found to depend on the initial profiles, reconciling contrasting results from two previous studies. Influences of the modeled uptake of trace gases by hydrometeors in the liquid and the ice phase were studied in some detail for a small number of atmospheric trace gases and novel aspects concerning the role of the retention coefficient (i.e. the fraction of a dissolved trace gas that is retained in the ice phase upon freezing) on the vertical transport of highly soluble gases were illuminated. Including lightning NOx production inside a 500 km 2-D model domain was found to be important for the NOx budget and caused small to moderate changes in the domain averaged ozone concentrations. A number of sensitivity studies yielded that the fraction of lightning associated NOx which was lost through photochemical reactions in the vicinity of the lightning source was considerable, but strongly depended on assumptions about the magnitude and the altitude of the lightning NOx source. In contrast to a suggestion from an earlier study, it was argued that the near zero upper tropospheric ozone mixing ratios which were observed close to the study region were most probably not caused by the formation of NO associated with lightning. Instead, it was argued in agreement with suggestions from other studies that the deep convective transport of ozone-poor air masses from the relatively unpolluted marine boundary layer, which have most likely been advected horizontally over relatively large distances (both before and after encountering deep convection) probably played a role. In particular, it was suggested that the ozone profiles observed during CEPEX (Central Equatorial Pacific Experiment) were strongly influenced by the deep convection and the larger scale flow which are associated with the intra-seasonal oscillation.

Measurement and modeling of cloud condensation nuclei in continental air

Atmospheric aerosol particles serving as cloud condensation nuclei (CCN) are key elements of the hydrological cycle and climate. Knowledge of the spatial and temporal distribution of CCN in the atmosphere is essential to understand and describe the effects of aerosols in meteorological models. In this study, CCN properties were measured in polluted and pristine air of different continental regions, and the results were parameterized for efficient prediction of CCN concentrations.The continuous-flow CCN counter used for size-resolved measurements of CCN efficiency spectra (activation curves) was calibrated with ammonium sulfate and sodium chloride aerosols for a wide range of water vapor supersaturations (S=0.068% to 1.27%). A comprehensive uncertainty analysis showed that the instrument calibration depends strongly on the applied particle generation techniques, Köhler model calculations, and water activity parameterizations (relative deviations in S up to 25%). Laboratory experiments and a comparison with other CCN instruments confirmed the high accuracy and precision of the calibration and measurement procedures developed and applied in this study.The mean CCN number concentrations (NCCN,S) observed in polluted mega-city air and biomass burning smoke (Beijing and Pearl River Delta, China) ranged from 1000 cm−3 at S=0.068% to 16 000 cm−3 at S=1.27%, which is about two orders of magnitude higher than in pristine air at remote continental sites (Swiss Alps, Amazonian rainforest). Effective average hygroscopicity parameters, κ, describing the influence of chemical composition on the CCN activity of aerosol particles were derived from the measurement data. They varied in the range of 0.3±0.2, were size-dependent, and could be parameterized as a function of organic and inorganic aerosol mass fraction. At low S (≤0.27%), substantial portions of externally mixed CCN-inactive particles with much lower hygroscopicity were observed in polluted air (fresh soot particles with κ≈0.01). Thus, the aerosol particle mixing state needs to be known for highly accurate predictions of NCCN,S. Nevertheless, the observed CCN number concentrations could be efficiently approximated using measured aerosol particle number size distributions and a simple κ-Köhler model with a single proxy for the effective average particle hygroscopicity. The relative deviations between observations and model predictions were on average less than 20% when a constant average value of κ=0.3 was used in conjunction with variable size distribution data. With a constant average size distribution, however, the deviations increased up to 100% and more. The measurement and model results demonstrate that the aerosol particle number and size are the major predictors for the variability of the CCN concentration in continental boundary layer air, followed by particle composition and hygroscopicity as relatively minor modulators. Depending on the required and applicable level of detail, the measurement results and parameterizations presented in this study can be directly implemented in detailed process models as well as in large-scale atmospheric and climate models for efficient description of the CCN activity of atmospheric aerosols.

Complementary mass spectrometric techniques for the characterization of the organic fraction in atmospheric aerosols

Aerosol particles are strongly related to climate, air quality, visibility and human health issues. They contribute the largest uncertainty in the assessment of the Earth´s radiative budget, directly by scattering or absorbing solar radiation or indirectly by nucleating cloud droplets. The influence of aerosol particles on cloud related climatic effects essentially depends upon their number concentration, size and chemical composition. A major part of submicron aerosol consists of secondary organic aerosol (SOA) that is formed in the atmosphere by the oxidation of volatile organic compounds. SOA can comprise a highly diverse spectrum of compounds that undergo continuous chemical transformations in the atmosphere.rnThe aim of this work was to obtain insights into the complexity of ambient SOA by the application of advanced mass spectrometric techniques. Therefore, an atmospheric pressure chemical ionization ion trap mass spectrometer (APCI-IT-MS) was applied in the field, facilitating the measurement of ions of the intact molecular organic species. Furthermore, the high measurement frequency provided insights into SOA composition and chemical transformation processes on a high temporal resolution. Within different comprehensive field campaigns, online measurements of particular biogenic organic acids were achieved by combining an online aerosol concentrator with the APCI-IT-MS. A holistic picture of the ambient organic aerosol was obtained through the co-located application of other complementary MS techniques, such as aerosol mass spectrometry (AMS) or filter sampling for the analysis by liquid chromatography / ultrahigh resolution mass spectrometry (LC/UHRMS).rnIn particular, during a summertime field study at the pristine boreal forest station in Hyytiälä, Finland, the partitioning of organic acids between gas and particle phase was quantified, based on the online APCI-IT-MS and AMS measurements. It was found that low volatile compounds reside to a large extent in the gas phase. This observation can be interpreted as a consequence of large aerosol equilibration timescales, which build up due to the continuous production of low volatile compounds in the gas phase and/or a semi-solid phase state of the ambient aerosol. Furthermore, in-situ structural informations of particular compounds were achieved by using the MS/MS mode of the ion trap. The comparison to MS/MS spectra from laboratory generated SOA of specific monoterpene precursors indicated that laboratory SOA barely depicts the complexity of ambient SOA. Moreover, it was shown that the mass spectra of the laboratory SOA more closely resemble the ambient gas phase composition, indicating that the oxidation state of the ambient organic compounds in the particle phase is underestimated by the comparison to laboratory ozonolysis. These observations suggest that the micro-scale processes, such as the chemistry of aerosol aging or the gas-to-particle partitioning, need to be better understood in order to predict SOA concentrations more reliably.rnDuring a field study at the Mt. Kleiner Feldberg, Germany, a slightly different aerosol concentrator / APCI-IT-MS setup made the online analysis of new particle formation possible. During a particular nucleation event, the online mass spectra indicated that organic compounds of approximately 300 Da are main constituents of the bulk aerosol during ambient new particle formation. Co-located filter analysis by LC/UHRMS analysis supported these findings and furthermore allowed to determine the molecular formulas of the involved organic compounds. The unambiguous identification of several oxidized C 15 compounds indicated that oxidation products of sesquiterpenes can be important compounds for the initial formation and subsequent growth of atmospheric nanoparticles.rnThe LC/UHRMS analysis furthermore revealed that considerable amounts of organosulfates and nitrooxy organosulfates were detected on the filter samples. Indeed, it was found that several nitrooxy organosulfate related APCI-IT-MS mass traces were simultaneously enhanced. Concurrent particle phase ion chromatography and AMS measurements indicated a strong bias between inorganic sulfate and total sulfate concentrations, supporting the assumption that substantial amounts of sulfate was bonded to organic molecules.rnFinally, the comprehensive chemical analysis of the aerosol composition was compared to the hygroscopicity parameter kappa, which was derived from cloud condensation nuclei (CCN) measurements. Simultaneously, organic aerosol aging was observed by the evolution of a ratio between a second and a first generation biogenic oxidation product. It was found that this aging proxy positively correlates with increasing hygroscopicity. Moreover, it was observed that the bonding of sulfate to organic molecules leads to a significant reduction of kappa, compared to an internal mixture of the same mass fractions of purely inorganic sulfate and organic molecules. Concluding, it has been shown within this thesis that the application of modern mass spectrometric techniques allows for detailed insights into chemical and physico-chemical processes of atmospheric aerosols.rn

Harmony: Towards automated self-adaptive consistency in cloud storage

In just a few years cloud computing has become a very popular paradigm and a business success story, with storage being one of the key features. To achieve high data availability, cloud storage services rely on replication. In this context, one major challenge is data consistency. In contrast to traditional approaches that are mostly based on strong consistency, many cloud storage services opt for weaker consistency models in order to achieve better availability and performance. This comes at the cost of a high probability of stale data being read, as the replicas involved in the reads may not always have the most recent write. In this paper, we propose a novel approach, named Harmony, which adaptively tunes the consistency level at run-time according to the application requirements. The key idea behind Harmony is an intelligent estimation model of stale reads, allowing to elastically scale up or down the number of replicas involved in read operations to maintain a low (possibly zero) tolerable fraction of stale reads. As a result, Harmony can meet the desired consistency of the applications while achieving good performance. We have implemented Harmony and performed extensive evaluations with the Cassandra cloud storage on Grid?5000 testbed and on Amazon EC2. The results show that Harmony can achieve good performance without exceeding the tolerated number of stale reads. For instance, in contrast to the static eventual consistency used in Cassandra, Harmony reduces the stale data being read by almost 80% while adding only minimal latency. Meanwhile, it improves the throughput of the system by 45% while maintaining the desired consistency requirements of the applications when compared to the strong consistency model in Cassandra.

The Evolution of Massive Young Stellar Objects in the Large Magellanic Cloud

This thesis presents an analysis of the largest catalog to date of infrared spectra of massive young stellar objects in the Large Magellanic Cloud. Evidenced by their very different spectral features, the luminous objects span a range of evolutionary states from those most embedded in their natal molecular material to those that have dissipated and ionized their surroundings to form compact HII regions and photodissociation regions. We quantify the contributions of the various spectral features using the statistical method of principal component analysis. Using this analysis, we classify the YSO spectra into several distinct groups based upon their dominant spectral features: silicate absorption (S Group), silicate absorption and fine-structure line emission (SE), polycyclic aromatic hydrocarbon (PAH) emission (P Group), PAH and fine-structure line emission (PE), and only fine-structure line emission (E). Based upon the relative numbers of sources in each category, we are able to estimate the amount of time massive YSOs spend in each evolutionary stage. We find that approximately 50% of the sources have ionic fine-structure lines, indicating that a compact HII region forms about half-way through the YSO lifetime probed in our study. Of the 277 YSOs we collected spectra for, 41 have ice absorption features, indicating they are surrounded by cold ice-bearing dust particles. We have decomposed the shape of the ice features to probe the composition and thermal history of the ice. We find that most the CO2 ice is embedded a polar ice matrix that has been thermally processed by the embedded YSO. The amount of thermal processing may be correlated with the luminosity of the YSO. Using the Australia Telescope Compact Array, we imaged the dense gas around a subsample of our sources in the HII complexes N44, N105, N113, and N159 using HCO+ and HCN as dense gas tracers. We find that the molecular material in star forming environments is highly clumpy, with clumps that range from subparsec to ~2 parsecs in size and with masses between 10^2 to 10^4 solar masses. We find that there are varying levels of star formation in the clumps, with the lower-mass clumps tending to be without massive YSOs. These YSO-less clumps could either represent an earlier stage of clump to the more massive YSO-bearing ones or clumps that will never form a massive star. Clumps with massive YSOs at their centers have masses larger than those with massive YSOs at their edges, and we suggest that the difference is evolutionary: edge YSO clumps are more advanced than those with YSOs at their centers. Clumps with YSOs at their edges may have had a significant fraction of their mass disrupted or destroyed by the forming massive star. We find that the strength of the silicate absorption seen in YSO IR spectra feature is well-correlated with the on-source HCO+ and HCN flux densities, such that the strength of the feature is indicative of the embeddedness of the YSO. We estimate that ~40% of the entire spectral sample has strong silicate absorption features, implying that the YSOs are embedded in circumstellar material for about 40% of the time probed in our study.

Multi-sensor Cloud and Aerosol Retrieval Simulator and Its Applications

Executing a cloud or aerosol physical properties retrieval algorithm from controlled synthetic data is an important step in retrieval algorithm development. Synthetic data can help answer questions about the sensitivity and performance of the algorithm or aid in determining how an existing retrieval algorithm may perform with a planned sensor. Synthetic data can also help in solving issues that may have surfaced in the retrieval results. Synthetic data become very important when other validation methods, such as field campaigns,are of limited scope. These tend to be of relatively short duration and often are costly. Ground stations have limited spatial coverage whilesynthetic data can cover large spatial and temporal scales and a wide variety of conditions at a low cost. In this work I develop an advanced cloud and aerosol retrieval simulator for the MODIS instrument, also known as Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS). In a close collaboration with the modeling community I have seamlessly combined the GEOS-5 global climate model with the DISORT radiative transfer code, widely used by the remote sensing community, with the observations from the MODIS instrument to create the simulator. With the MCARS simulator it was then possible to solve the long standing issue with the MODIS aerosol optical depth retrievals that had a low bias for smoke aerosols. MODIS aerosol retrieval did not account for effects of humidity on smoke aerosols. The MCARS simulator also revealed an issue that has not been recognized previously, namely,the value of fine mode fraction could create a linear dependence between retrieved aerosol optical depth and land surface reflectance. MCARS provided the ability to examine aerosol retrievals against “ground truth” for hundreds of thousands of simultaneous samples for an area covered by only three AERONET ground stations. Findings from MCARS are already being used to improve the performance of operational MODIS aerosol properties retrieval algorithms. The modeling community will use the MCARS data to create new parameterizations for aerosol properties as a function of properties of the atmospheric column and gain the ability to correct any assimilated retrieval data that may display similar dependencies in comparisons with ground measurements.

Water uptake of aerosols with a focus on seeded aerosols and instrumentation techniques

This thesis focuses on the volatile and hygroscopic properties of mixed aerosol species. In particular, the influence organic species of varying solubility have upon seed aerosols. Aerosol studies were conducted at the Paul Scherrer Institut Laboratory for Atmospheric Chemistry (PSI-LAC, Villigen, Switzerland) and at the Queensland University of Technology International Laboratory for Air Quality and Health (QUT-ILAQH, Brisbane, Australia). The primary measurement tool employed in this program was the Volatilisation and Hygroscopicity Tandem Differential Mobility Analyser (VHTDMA - Johnson et al. 2004). This system was initially developed at QUT within the ILAQH and was completely re-developed as part of this project (see Section 1.4 for a description of this process). The new VHTDMA was deployed to the PSI-LAC where an analysis of the volatile and hygroscopic properties of ammonium sulphate seeds coated with organic species formed from the photo-oxidation of á-pinene was conducted. This investigation was driven by a desire to understand the influence of atmospherically prevalent organics upon water uptake by material with cloud forming capabilities. Of particular note from this campaign were observed influences of partially soluble organic coatings upon inorganic ammonium sulphate seeds above and below their deliquescence relative humidity (DRH). Above the DRH of the seed increasing the volume fraction of the organic component was shown to reduce the water uptake of the mixed particle. Below the DRH the organic was shown to activate the water uptake of the seed. This was the first time this effect had been observed for á-pinene derived SOA. In contrast with the simulated aerosols generated at the PSI-LAC a case study of the volatile and hygroscopic properties of diesel emissions was undertaken. During this stage of the project ternary nucleation was shown, for the first time, to be one of the processes involved in formation of diesel particulate matter. Furthermore, these particles were shown to be coated with a volatile hydrophobic material which prevented the water uptake of the highly hygroscopic material below. This result was a first and indicated that previous studies into the hygroscopicity of diesel emission had erroneously reported the particles to be hydrophobic. Both of these results contradict the previously upheld Zdanovksii-Stokes-Robinson (ZSR) additive rule for water uptake by mixed species. This is an important contribution as it adds to the weight of evidence that limits the validity of this rule.

Quantifying the volume fraction and texture of cancellous bone using 3.0 Tesla magnetic resonance imaging

Introduction: 3.0 Tesla MRI offers the potential to quantify the volume fraction and structural texture of cancellous bone, along with quantification of marrow composition, in a single non-invasive examination. This study describes our preliminary investigations to identify parameters which describe cancellous bone structure including the relationships between texture and volume fraction.

Decomposition of aggregated load : finding induction motor fraction in real load

The main contribution of this paper is decomposition/separation of the compositie induction motors load from measurement at a system bus. In power system transmission buses load is represented by static and dynamic loads. The induction motor is considered as the main dynamic loads and in the practice for major transmission buses there will be many and various induction motors contributing. Particularly at an industrial bus most of the load is dynamic types. Rather than traing to extract models of many machines this paper seeks to identify three groups of induction motors to represent the dynamic loads. Three groups of induction motors used to characterize the load. These are the small groups (4kw to 11kw), the medium groups (15kw to 180kw) and the large groups (above 630kw). At first these groups with different percentage contribution of each group is composite. After that from the composite models, each motor percentage contribution is decomposed by using the least square algorithms. In power system commercial and the residential buses static loads percentage is higher than the dynamic loads percentage. To apply this theory to other types of buses such as residential and commerical it is good practice to represent the total load as a combination of composite motor loads, constant impedence loads and constant power loads. To validate the theory, the 24hrs of Sydney West data is decomposed according to the three groups of motor models.

A penalty-based genetic algorithm for the composite SaaS placement problem in the cloud

Cloud computing is a latest new computing paradigm where applications, data and IT services are provided over the Internet. Cloud computing has become a main medium for Software as a Service (SaaS) providers to host their SaaS as it can provide the scalability a SaaS requires. The challenges in the composite SaaS placement process rely on several factors including the large size of the Cloud network, SaaS competing resource requirements, SaaS interactions between its components and SaaS interactions with its data components. However, existing applications’ placement methods in data centres are not concerned with the placement of the component’s data. In addition, a Cloud network is much larger than data center networks that have been discussed in existing studies. This paper proposes a penalty-based genetic algorithm (GA) to the composite SaaS placement problem in the Cloud. We believe this is the first attempt to the SaaS placement with its data in Cloud provider’s servers. Experimental results demonstrate the feasibility and the scalability of the GA.

QoS-oriented sesource allocation and scheduling of multiple composite web services in a hybrid cloud using a random-key genetic algorithm

In cloud computing resource allocation and scheduling of multiple composite web services is an important challenge. This is especially so in a hybrid cloud where there may be some free resources available from private clouds but some fee-paying resources from public clouds. Meeting this challenge involves two classical computational problems. One is assigning resources to each of the tasks in the composite web service. The other is scheduling the allocated resources when each resource may be used by more than one task and may be needed at different points of time. In addition, we must consider Quality-of-Service issues, such as execution time and running costs. Existing approaches to resource allocation and scheduling in public clouds and grid computing are not applicable to this new problem. This paper presents a random-key genetic algorithm that solves new resource allocation and scheduling problem. Experimental results demonstrate the effectiveness and scalability of the algorithm.