Establishing fluxes of carbon dioxide and a transect of pCO2 during the late fall of precipitation driven Northern fjord

Senior thesis written for Oceanography 445

Assessment of relationships between precipitation and satellite derived vegetation condition in South Australia

The normalised difference vegetation index (NDVI) has evolved as a primary tool for monitoring continental-scale vegetation changes and interpreting the impact of short to long-term climatic events on the biosphere. The objective of this research was to assess the nature of relationships between precipitation and vegetation condition, as measured by the satellite-derived NDVI within South Australia. The correlation, timing and magnitude of the NDVI response to precipitation were examined for different vegetation formations within the State (forest, scrubland, shrubland, woodland and grassland). Results from this study indicate that there are strong relationships between precipitation and NDVI both spatially and temporally within South Australia. Differences in the timing of the NDVI response to precipitation were evident among the five vegetation formations. The most significant relationship between rainfall and NDVI was within the forest formation. Negative correlations between NDVI and precipitation events indicated that vegetation green-up is a result of seasonal patterns in precipitation. Spatial patterns in the average NDVI over the study period closely resembled the boundaries of the five classified vegetation formations within South Australia. Spatial variability within the NDVI data set over the study period differed greatly between and within the vegetation formations examined depending on the location within the state. ACRONYMS AVHRR Advanced Very High Resolution Radiometer ENVSAEnvironments of South Australia EOS Terra-Earth Observing System EVIEnhanced Vegetation Index MODIS Moderate Resolution Imaging Spectro-radiometer MVC Maximum Value Composite NDVINormalised Difference Vegetation Index NIRNear Infra-Red NOAANational Oceanic and Atmospheric Administration SPOT Systeme Pour l’Observation de la Terre. [ABSTRACT FROM AUTHOR]

Hydrothermal control on organic matter alteration and illite precipitation, Mt Isa Basin, Australia

Abundant illite precipitation, in Proterozoic rocks from Northern Lawn Hill Platform, Mt Isa Basin, Australia, occurred in organic matter-rich black shales rather than in sandstones, siltstones and organic matter-poor shales. Sandstones and siltstones acted as impermeable rocks, as early diagenetic quartz and carbonate minerals reduced the porosity-permeability. Scanning and transmission electron microscopy (SEM and TEM) studies indicate a relation between creation of microporosity-permeability and organic matter alteration, suitable for subsequent mineral precipitation. K-Ar data indicate that organic matter alteration and the subsequent illite precipitation within the organic matter occurred during the regional hydrothermal event at 1172 +/- 150 (2sigma) Ma. Hot circulating fluids are considered to be responsible for organic matter alteration, migration and removal of volatile hydrocarbon, and consequently porosity-permeability creation. Those rocks lacking sufficient porosity-permeability, such as sandstones, siltstones and organic matter poor shales, may not have been affected by fluid movement. In hydrothermal systems, shales and mudstones may not be impermeable as usually assumed because of hydrocarbons being rapidly removed by fluid, even with relatively low total organic carbon.

Precipitation induced densification in a sintered Al-Zn-Mg-Cu alloy

It is demonstrated that slow cooling to 200 degrees C from a high sintering temperature (620 degrees C) reduces porosity in an Al-8Zn-2.5Mg-1Cu powder compact when compared to isothermal sintering at the higher temperature for a longer time. The reduction in porosity is attributed to shrinkage associated with removal of solute from the aluminium solid solution and heterogeneous precipitation of the eta phase (MgZn2), particularly onto pore surfaces. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Precipitation and Fire Effects on Flowering of a Rare Prairie Orchid

A small, isolated population of the threatened western prairie fringed orchid (Platanthera praeclara Sheviak & Bowles) occurs at Pipestone National Monument, Minnesota, in a mesic prairie that is periodically burned to control invasive cool-season grasses. During 1995-2004, monitoring counts of flowering orchids in the monument varied considerably for different years. Similar precipitation amounts in the spring and histories of burning suggest that fire and precipitation in the spring were not the causes of the variation. For the eight non-burn years in the monitoring record, we compared the number of flowering plants and the precipitation amounts during six growth stages of the orchid and found a 2-variab1e model (precipitation during senescence/bud development and precipitation in the dormant period) explained 77% of the annual variation in number of flowering plants. We also conducted a fire experiment in early May 2002, the typical prescribed burn period for the monument, and found that the frequency of flowering, vegetative, and absent plants observed in July did not differ between burned and protected locations of orchids. We used the model and forecasts of precipitation in the spring to develop provisional burn decision scenarios. We discussed management implications of the scenarios.

Particle formation during soy protein precipitation

The current success of soy foods is driving soy ingredient manufacturers to develop innovative products for food manufacturers. One such innovation is separating the soy proteins glycinin and beta-conglycinin to take advantage of their individual functional and nutritional properties. Precipitation by acidification is a low-cost method for separating these two proteins. Separation is achieved by preferentially precipitating glycinin at pH ~ 6 while beta-conglycinin remains in solution. Understanding the particle formation during protein precipitation is important as it can influence the efficiency of the precipitation process as well as subsequent downstream processes such as the particle-liquid separation step, usually achieved by centrifugation. Most of the previous soy protein precipitation studies are limited to precipitation at pH 4 as this is the pH range most commonly used in the commercial manufacturing of soy protein isolates. To date, there have been no studies on the particle formation during precipitation at pH > 5.Precipitation of soy protein is generally thought to occur by the rapid formation of primary particles in the size range of 0.1 - 0.3 microns followed by aggregation of these particles via collision to aggregates of size about 1 - 50 microns. The formation of the primary particles occurs on a time scale much shorter than that of the overall precipitation process (Nelson and Glatz, 1985). This study shows that precipitation of soy protein is indeed rapid. At high pH levels, binary liquid-liquid separation occurs forming a protein-rich heavy phase. The protein-rich phase appears as droplets which can be coalesced to form a uniform bulk layer under centrifugation forces. Upon lowering the pH level by the addition of acid, further protein is precipitated as amorphous material which binds the droplets together to form aggregates of amorphous precipitates. Liquid-liquid separation has been observed in many protein solutions but this phenomenon has only scarcely been reported in the literature for soy proteins. It presents an exciting opportunity for an innovative product. Features of the liquid-phase protein such as protein yield and purity will be characterized.

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed

A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting

The generation of very short range forecasts of precipitation in the 0-6 h time window is traditionally referred to as nowcasting. Most existing nowcasting systems essentially extrapolate radar observations in some manner, however, very few systems account for the uncertainties involved. Thus deterministic forecast are produced, which have a limited use when decisions must be made, since they have no measure of confidence or spread of the forecast. This paper develops a Bayesian state space modelling framework for quantitative precipitation nowcasting which is probabilistic from conception. The model treats the observations (radar) as noisy realisations of the underlying true precipitation process, recognising that this process can never be completely known, and thus must be represented probabilistically. In the model presented here the dynamics of the precipitation are dominated by advection, so this is a probabilistic extrapolation forecast. The model is designed in such a way as to minimise the computational burden, while maintaining a full, joint representation of the probability density function of the precipitation process. The update and evolution equations avoid the need to sample, thus only one model needs be run as opposed to the more traditional ensemble route. It is shown that the model works well on both simulated and real data, but that further work is required before the model can be used operationally. © 2004 Elsevier B.V. All rights reserved.