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Directly assimilating microwave radiances over land, snow and sea ice remains a significant challenge for data assimilation systems. These data assimilation systems are critical to the success of global numerical weather prediction systems including the Global Earth Observing System-Atmospheric Data Assimilation System (GEOS-ADAS). Extending more surface sensitive microwave channels over land, snow and ice could provide a needed source of data for Numerical Weather Prediction particularly in the Planetary Boundary Layer (PBL). Unfortunately, the accuracy of emissivity models currently available within the GEOS-ADAS along with other data assimilation systems are insufficient to simulate and assimilate radiances. Recently, Munchak et al. (2020) published a 5-year climatological database for retrieved microwave emissivity from the GPM Microwave Imager (GMI) aboard the Global Precipitation Mission (GPM). In this work the database is utilized by modifying the GEOS-ADAS to use this emissivity database in place of the default emissivity value available in the Community Radiative Transfer Model (CRTM), which is the fast radiative transfer model used by the GEOS-ADAS. As a first step, the GEOS-ADAS is run in a so-called “stand-alone” mode to simulate radiances from GMI using the default CRTM emissivity, and replacing the default CRTM emissivity models with values from Munchak et al, 2020. The simulated observations using Munchak et al., 2020 agree more closely with observations from GMI. These results are presented along with a discussion of the implication for GMI observations within the GEOS-ADAS.

As the demand for cloud computing continues to increase, cloud service providers face the daunting challenge to meet the negotiated SLA agreement, in terms of reliability and timely performance, while achieving cost-effectiveness. This challenge is increasingly compounded by the increasing likelihood of failure in large-scale clouds and the rising impact of energy consumption and CO2 emission on the environment. This paper proposes Shadow Replication, a novel fault-tolerance model for cloud computing, which seamlessly addresses failure at scale, while minimizing energy consumption and reducing its impact on the environment. The basic tenet of the model is to associate a suite of shadow processes to execute concurrently with the main process, but initially at a much reduced execution speed, to overcome failures as they occur. Two computationally-feasible schemes are proposed to achieve Shadow Replication. A performance evaluation framework is developed to analyze these schemes and compare their performance to traditional replication-based fault tolerance methods, focusing on the inherent tradeoff between fault tolerance, the specified SLA and profit maximization. The results show that Shadow Replication leads to significant energy reduction, and is better suited for compute-intensive execution models, where up to 30% more profit increase can be achieved due to reduced energy consumption.

The academic community's interest in surf tourism continues to grow with important contributions being made to our understanding of culture, economic behavior, and impact at specific sites. However, there was little understood about the impact surfers and surf tourism have on the overall economy of the UK. Given the estimated 500,000 surfers in the UK in 2007 by the Department for the Environment, Food and Rural Affairs, and given unique access to a comprehensive database of UK surfers it has been possible to go some way toward correcting this data shortage. By analyzing 2,159 questionnaire responses, and after taking account of economic multipliers, a total contribution to the UK economy by domestic surfers of ?4.95 billion with an average direct spend of ?2,980 per year on surfing-related expenditure may be estimated making surfing an important contributor to UK tourism and the UK economy.

Whether they need to check the weather or start their favorite playlist, voice assistance lets them do it all hands-free. 4, Lighting for Every Mood Smart lighting systems can adjust to various scenarios: daylight-like lighting for meal prep or intimate lighting for dinner. Motorized Window Treatments Kitchens often feature large windows that let in plenty of beautiful natural light but may be problematic for privacy or sun glare. Motorized window treatments are a game-changer, allowing your clients to adjust shades at the touch of a button.

Smoking causes cancer, it raises the risk of heart disease, it more than doubles the risk of stroke, it increases absenteeism at work, and it cuts lives short. Research suggests that a 10 percent increase in the cost of cigarettes cuts the number of pregnant smokers 7 percent and reduces the number of kids who smoke by a similar percentage. Raising the tobacco tax is one simple action we can take that will pay impressive dividends.^ How we reduce the cost of health care in Indiana really is a simple question with a strikingly simple answer.

To enable future scientific breakthroughs and discoveries, the next generation of scientific applications will require exascale computing performance to support the execution of predictive models and analysis of massive quantities of data, with significantly higher resolution and fidelity than what is possible within existing computing infrastructure. Delivering exascale performance will require massive parallelism, which could result in a computing system with over a million sockets, each supporting many cores. Resulting in a system with millions of components, including memory modules, communication networks, and storage devices. This increase in number of components significantly increases the propensity of exascale computing systems to faults, while driving power consumption and operating costs to unforeseen heights. To achieve exascale performance two challenges must be addressed: resilience to failures and adherence to power budget constraints. These two objectives conflict insofar as performance is concerned, as achieving high performance may push system components past their thermal limit and increase the likelihood of failure. With current systems, the dominant resilience technique is checkpoint/restart. It is believed, however, that this technique alone will not scale to the level necessary to support future systems. Therefore, alternative methods have been suggested to augment checkpoint/restart – for example process replication. In this thesis, we present a new fault tolerance model called shadow replication that addresses resilience and power simultaneously. Shadow replication associates a shadow process with each main process, similar to traditional replication, however, the shadow process executes at a reduced speed. Shadow replication reduces energy consumption and produces solutions faster than checkpoint/restart and other replication methods in limited power environments. Shadow replication reduces energy consumption up to 25% depending upon the application type, system parameters, and failure rates. The major contribution of this thesis is the development of shadow replication, a power-aware fault tolerant computational model. The second contribution is an execution model applying shadow replication to future high performance exascale-class systems. Next, is a framework to analyze and simulate the power and energy consumption of fault tolerance methods in high performance computing systems. Lastly, to prove the viability of shadow replication an implementation is presented for the Message Passing Interface.

This research has involved the conduct of a series of laboratory measurements of the centimeter-wavelength opacity of water vapor along with the development of a hybrid radiative transfer ray-tracing simulator for the atmosphere of Jupiter which employs a model for water vapor opacity derived from the measurements. For this study an existing Georgia Tech high-sensitivity microwave measurement system (Hanley and Steffes, 2007) has been adapted for pressures ranging from 12–100 bars, and a corresponding temperature range of 293–525°K. Water vapor is measured in a mixture of hydrogen and helium. Using these measurements which covered a wavelength range of 6–20 cm, a new model is developed for water vapor absorption under Jovian conditions. In conjunction with our laboratory measurements, and the development of a new model for water vapor absorption, we conduct sensitivity studies of water vapor microwave emission in the Jovian atmosphere using a hybrid radiative transfer ray-tracing simulator. The approach has been used previously for Saturn (Hoffman, 2001), and Venus (Jenkins et al., 2001). This model has been adapted to include the antenna patterns typical of the NASA Juno Mission microwave radiometer (NASA/Juno-MWR) along with Jupiter’s geometric parameters (oblateness), and atmospheric conditions. Using this adapted model we perform rigorous sensitivity tests for water vapor in the Jovian atmosphere. This work will directly improve our understanding of microwave absorption by atmospheric water vapor at Jupiter, and improve retrievals from the Juno microwave radiometer. Indirectly, this work will help to refine models for the formation of Jupiter and the entire solar system through an improved understanding of the planet-wide abundance of water vapor which will result from the successful opreation of the Juno Microwave Radiometer (Juno-MWR).