The magnitude of the snow-sourced reactive nitrogen flux to the boundary layer in the Uintah Basin, Utah, USA

Reactive nitrogen (Nr=NO, NO2, HONO) and volatile organic carbon emissions from oil and gas extraction activities play a major role in wintertime ground-level ozone exceedance events of up to 140 ppb in the Uintah Basin in eastern Utah. Such events occur only when the ground is snow covered, due to the impacts of snow on the stability and depth of the boundary layer and ultraviolet actinic flux at the surface. Recycling of reactive nitrogen from the photolysis of snow nitrate has been observed in polar and mid-latitude snow, but snow-sourced reactive nitrogen fluxes in mid-latitude regions have not yet been quantified in the field. Here we present vertical profiles of snow nitrate concentration and nitrogen isotopes (δ15N) collected during the Uintah Basin Winter Ozone Study 2014 (UBWOS 2014), along with observations of insoluble light-absorbing impurities, radiation equivalent mean ice grain radii, and snow density that determine snow optical properties. We use the snow optical properties and nitrate concentrations to calculate ultraviolet actinic flux in snow and the production of Nr from the photolysis of snow nitrate. The observed δ15N(NO3-) is used to constrain modeled fractional loss of snow nitrate in a snow chemistry column model, and thus the source of Nr to the overlying boundary layer. Snow-surface δ15N(NO3-) measurements range from -5‰ to 10‰ and suggest that the local nitrate burden in the Uintah Basin is dominated by primary emissions from anthropogenic sources, except during fresh snowfall events, where remote NOx sources from beyond the basin are dominant. Modeled daily-averaged snow-sourced Nr fluxes range from 5.6-71x107 molec cm-2 s-1 over the course of the field campaign, with a maximum noon-time value of 3.1x109 molec cm-2 s-1. The top-down emission estimate of primary, anthropogenic NOx in the Uintah and Duchesne counties is at least 300 times higher than the estimated snow NOx emissions presented in this study. Our results suggest that snow-sourced reactive nitrogen fluxes are minor contributors to the Nr boundary layer budget in the highly-polluted Uintah Basin boundary layer during winter 2014.

Mt. Bachelor Observatory 2015 data

The Mt. Bachelor Observatory (MBO) is a high altitude atmospheric research station that is located at the Mt. Bachelor ski area in Central Oregon. The observatory was started by Prof. Dan Jaffe in 2004. Over this time, his time at UW has made observations of ozone, carbon monoxide, mercury, nitrogen oxides, particulate matter and other atmospheric constituents. The data for 2015 are reported in this dataset. MBO coordinates (summit building): Latitude: 43.9775 N Longitude: 121.6861 W Elevation: 2.74 km asl

Scalar turbulent behavior in the roughness sublayer of an Amazonian forest.

An important current problem in micrometeorology is the characterization of turbulence in the roughness sublayer (RSL), where most of the measurements above tall forests are made. There, scalar turbulent fluctuations display significant departures from the predictions of Monin?Obukhov similarity theory (MOST). In this work, we analyze turbulence data of virtual temperature, carbon dioxide, and water vapor in the RSL above an Amazonian forest (with a canopy height of 40 m), measured at 39.4 and 81.6 m above the ground under unstable conditions. We found that dimensionless statistics related to the rate of dissipation of turbulence kinetic energy (TKE) and the scalar variance display significant departures from MOST as expected, whereas the vertical velocity variance follows MOST much more closely. Much better agreement between the dimensionless statistics with the Obukhov similarity variable, however, was found for the subset of measurements made at a low zenith angle Z, in the range 0°  <  |Z|  <  20°. We conjecture that this improvement is due to the relationship between sunlight incidence and the ?activation?deactivation? of scalar sinks and sources vertically distributed in the forest. Finally, we evaluated the relaxation coefficient of relaxed eddy accumulation: it is also affected by zenith angle, with considerable improvement in the range 0°  <  |Z|  <  20°, and its values fall within the range reported in the literature for the unstable surface layer. In general, our results indicate the possibility of better stability-derived flux estimates for low zenith angle ranges.

Scalar turbulent behavior in the roughness sublayer of an Amazonian forest.

An important current problem in micrometeorology is the characterization of turbulence in the roughness sublayer (RSL), where most of the measurements above tall forests are made. There, scalar turbulent fluctuations display significant departures from the predictions of Monin?Obukhov similarity theory (MOST). In this work, we analyze turbulence data of virtual temperature, carbon dioxide, and water vapor in the RSL above an Amazonian forest (with a canopy height of 40?m), measured at 39.4 and 81.6?m above the ground under unstable conditions. We found that dimensionless statistics related to the rate of dissipation of turbulence kinetic energy (TKE) and the scalar variance display significant departures from MOST as expected, whereas the vertical velocity variance follows MOST much more closely. Much better agreement between the dimensionless statistics with the Obukhov similarity variable, however, was found for the subset of measurements made at a low zenith angle Z, in the range 0°??

Novel metallo-porphyrin based colourimetric amine sensors and their processing via plasma enhanced chemical vapour deposition at atmospheric pressure : synthesis, characterisation and mechanistic studies

Volatile amines are prominent indicators of food freshness, as they are produced during many microbiological food degradation processes. Monitoring and indicating the volatile amine concentration within the food package by intelligent packaging solutions might therefore be a simple yet powerful way to control food safety throughout the distribution chain.rnrnIn this context, this work aims to the formation of colourimetric amine sensing surfaces on different substrates, especially transparent PET packaging foil. The colour change of the deposited layers should ideally be discernible by the human eye to facilitate the determination by the end-user. rnrnDifferent tailored zinc(II) and chromium(III) metalloporphyrins have been used as chromophores for the colourimetric detection of volatile amines. A new concept to increase the porphyrins absorbance change upon exposure to amines is introduced. Moreover, the novel porphyrins’ processability during the deposition process is increased by their enhanced solubility in non-polar solvents.rnrnThe porphyrin chromophores have successfully been incorporated into polysiloxane matrices on different substrates via a dielectric barrier discharge enhanced chemical vapour deposition. This process allows the use of nitrogen as a cheap and abundant plasma gas, produces minor amounts of waste and by-products and can be easily introduced into (existing) roll-to-roll production lines. The formed hybrid sensing layers tightly incorporate the porphyrins and moreover form a porous structure to facilitate the amines diffusion to and interaction with the chromophores.rnrnThe work is completed with the thorough analysis of the porphyrins’ amine sensing performance in solution as well as in the hybrid coatings . To reveal the underlying interaction mechanisms, the experimental results are supported by DFT calculations. The deposited layers could be used for the detection of NEt3 concentrations below 10 ppm in the gas phase. Moreover, the coated foils have been tested in preliminary food storage experiments. rnrnThe mechanistic investigations on the interaction of amines with chromium(III) porphyrins revealed a novel pathway to the formation of chromium(IV) oxido porphyrins. This has been used for electrochemical epoxidation reactions with dioxygen as the formal terminal oxidant.rn

Aerosol-cloud interactions studied with a chemistry-climate model

This study aims at a comprehensive understanding of the effects of aerosol-cloud interactions and their effects on cloud properties and climate using the chemistry-climate model EMAC. In this study, CCN activation is regarded as the dominant driver in aerosol-cloud feedback loops in warm clouds. The CCN activation is calculated prognostically using two different cloud droplet nucleation parameterizations, the STN and HYB CDN schemes. Both CDN schemes account for size and chemistry effects on the droplet formation based on the same aerosol properties. The calculation of the solute effect (hygroscopicity) is the main difference between the CDN schemes. The kappa-method is for the first time incorporated into Abdul-Razzak and Ghan activation scheme (ARG) to calculate hygroscopicity and critical supersaturation of aerosols (HYB), and the performance of the modied scheme is compared with the osmotic coefficient model (STN), which is the standard in the ARG scheme. Reference simulations (REF) with the prescribed cloud droplet number concentration have also been carried out in order to understand the effects of aerosol-cloud feedbacks. In addition, since the calculated cloud coverage is an important determinant of cloud radiative effects and is influencing the nucleation process two cloud cover parameterizations (i.e., a relative humidity threshold; RH-CLC and a statistical cloud cover scheme; ST-CLC) have been examined together with the CDN schemes, and their effects on the simulated cloud properties and relevant climate parameters have been investigated. The distinct cloud droplet spectra show strong sensitivity to aerosol composition effects on cloud droplet formation in all particle sizes, especially for the Aitken mode. As Aitken particles are the major component of the total aerosol number concentration and CCN, and are most sensitive to aerosol chemical composition effect (solute effect) on droplet formation, the activation of Aitken particles strongly contribute to total cloud droplet formation and thereby providing different cloud droplet spectra. These different spectra influence cloud structure, cloud properties, and climate, and show regionally varying sensitivity to meteorological and geographical condition as well as the spatiotemporal aerosol properties (i.e., particle size, number, and composition). The changes responding to different CDN schemes are more pronounced at lower altitudes than higher altitudes. Among regions, the subarctic regions show the strongest changes, as the lower surface temperature amplifies the effects of the activated aerosols; in contrast, the Sahara desert, where is an extremely dry area, is less influenced by changes in CCN number concentration. The aerosol-cloud coupling effects have been examined by comparing the prognostic CDN simulations (STN, HYB) with the reference simulation (REF). Most pronounced effects are found in the cloud droplet number concentration, cloud water distribution, and cloud radiative effect. The aerosol-cloud coupling generally increases cloud droplet number concentration; this decreases the efficiency of the formation of weak stratiform precipitation, and increases the cloud water loading. These large-scale changes lead to larger cloud cover and longer cloud lifetime, and contribute to high optical thickness and strong cloud cooling effects. This cools the Earth's surface, increases atmospheric stability, and reduces convective activity. These changes corresponding to aerosol-cloud feedbacks are also differently simulated depending on the cloud cover scheme. The ST-CLC scheme is more sensitive to aerosol-cloud coupling, since this scheme uses a tighter linkage of local dynamics and cloud water distributions in cloud formation process than the RH-CLC scheme. For the calculated total cloud cover, the RH-CLC scheme simulates relatively similar pattern to observations than the ST-CLC scheme does, but the overall properties (e.g., total cloud cover, cloud water content) in the RH simulations are overestimated, particularly over ocean. This is mainly originated from the difference in simulated skewness in each scheme: the RH simulations calculate negatively skewed distributions of cloud cover and relevant cloud water, which is similar to that of the observations, while the ST simulations yield positively skewed distributions resulting in lower mean values than the RH-CLC scheme does. The underestimation of total cloud cover over ocean, particularly over the intertropical convergence zone (ITCZ) relates to systematic defficiency of the prognostic calculation of skewness in the current set-ups of the ST-CLC scheme.rnOverall, the current EMAC model set-ups perform better over continents for all combinations of the cloud droplet nucleation and cloud cover schemes. To consider aerosol-cloud feedbacks, the HYB scheme is a better method for predicting cloud and climate parameters for both cloud cover schemes than the STN scheme. The RH-CLC scheme offers a better simulation of total cloud cover and the relevant parameters with the HYB scheme and single-moment microphysics (REF) than the ST-CLC does, but is not very sensitive to aerosol-cloud interactions.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

Parallelization of web processing services on cloud computing: A case study of Geostatistical Methods

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Exploration of alpine orographic precipitation patterns with radar image processing and clustering techniques

A methodology of exploratory data analysis investigating the phenomenon of orographic precipitation enhancement is proposed. The precipitation observations obtained from three Swiss Doppler weather radars are analysed for the major precipitation event of August 2005 in the Alps. Image processing techniques are used to detect significant precipitation cells/pixels from radar images while filtering out spurious effects due to ground clutter. The contribution of topography to precipitation patterns is described by an extensive set of topographical descriptors computed from the digital elevation model at multiple spatial scales. Additionally, the motion vector field is derived from subsequent radar images and integrated into a set of topographic features to highlight the slopes exposed to main flows. Following the exploratory data analysis with a recent algorithm of spectral clustering, it is shown that orographic precipitation cells are generated under specific flow and topographic conditions. Repeatability of precipitation patterns in particular spatial locations is found to be linked to specific local terrain shapes, e.g. at the top of hills and on the upwind side of the mountains. This methodology and our empirical findings for the Alpine region provide a basis for building computational data-driven models of orographic enhancement and triggering of precipitation. Copyright (C) 2011 Royal Meteorological Society .

A distributed, cloud-ready, digital content processing and transformation platform and a specific use case

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Big Data Processing in the Cloud: a Hydra/Sufia Experience

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Oxidation of oleic acid at the air-water interface and its potential effects on cloud critical supersaturations

The oxidation of organic films on cloud condensation nuclei has the potential to affect climate and precipitation events. In this work we present a study of the oxidation of a monolayer of deuterated oleic acid (cis-9-octadecenoic acid) at the air-water interface by ozone to determine if oxidation removes the organic film or replaces it with a product film. A range of different aqueous sub-phases were studied. The surface excess of deuterated material was followed by neutron reflection whilst the surface pressure was followed using a Wilhelmy plate. The neutron reflection data reveal that approximately half the organic material remains at the air-water interface following the oxidation of oleic acid by ozone, thus cleavage of the double bond by ozone creates one surface active species and one species that partitions to the bulk (or gas) phase. The most probable products, produced with a yield of similar to(87 +/- 14)%, are nonanoic acid, which remains at the interface, and azelaic acid (nonanedioic acid), which dissolves into the bulk solution. We also report a surface bimolecular rate constant for the reaction between ozone and oleic acid of (7.3 +/- 0.9) x 10(-11) cm(2) molecule s(-1). The rate constant and product yield are not affected by the solution sub-phase. An uptake coefficient of ozone on the oleic acid monolayer of similar to 4 x 10(-6) is estimated from our results. A simple Kohler analysis demonstrates that the oxidation of oleic acid by ozone on an atmospheric aerosol will lower the critical supersaturation needed for cloud droplet formation. We calculate an atmospheric chemical lifetime of oleic acid of 1.3 hours, significantly longer than laboratory studies on pure oleic acid particles suggest, but more consistent with field studies reporting oleic acid present in aged atmospheric aerosol.

The cloud chamber and CTR Wilson’s legacy to atmospheric science

2011 is the centenary year of the short paper (Wilson,1911) first describing the cloud chamber, the device for visualising high-energy charged particles which earned the Scottish physicist Charles Thomas Rees (‘CTR’) Wilson the 1927 Nobel Prize for physics. His many achievements in atmospheric science, some of which have current relevance, are briefly reviewed here. CTR Wilson’s lifetime of scientific research work was principally in atmospheric electricity at the Cavendish Laboratory, Cambridge; he was Reader in Electrical Meteorology from 1918 and Jacksonian Professor from 1925 to 1935. However, he is immortalised in physics for his invention of the cloud chamber, because of its great significance as an early visualisation tool for particles such as cosmic rays1 (Galison, 1997). Sir Lawrence Bragg summarised its importance:

Advances in sequential data assimilation and numerical weather forecasting: an Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic Ensemble Square Root Filters, and a test of a new time stepping scheme in an atmospheric model

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn’t represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.