Synergetic use of millimeter- and centimeter-wavelength radars for retrievals of cloud and rainfall parameters

A remote sensing approach for simultaneous retrievals of cloud and rainfall parameters in the vertical column above the US Department of Energy's (DOE) Climate Research Facility at the Tropical Western Pacific (TWP) Darwin site in Australia is described. This approach uses vertically pointing measurements from a DOE <i>K</i><sub>a</sub>-band radar and scanning measurements from a nearby C-band radar pointing toward the TWP Darwin site. Rainfall retrieval constraints are provided by data from a surface impact disdrometer. The approach is applicable to stratiform precipitating cloud systems when a separation between the liquid hydrometeor layer, which contains rainfall and liquid water clouds, and the ice hydrometeor layer is provided by the radar bright band. Absolute C-band reflectivities and <i>K</i><sub>a</sub>-band vertical reflectivity gradients in the liquid layer are used for retrievals of the mean layer rain rate and cloud liquid water path (CLWP). C-band radar reflectivities are also used to estimate ice water path (IWP) in regions above the melting layer. The retrieval uncertainties of CLWP and IWP for typical stratiform precipitation systems are about 500–800 g m<sup>−2</sup> (for CLWP) and a factor of 2 (for IWP). The CLWP retrieval uncertainties increase with rain rate, so retrievals for higher rain rates may be impractical. The expected uncertainties of layer mean rain rate retrievals are around 20%, which, in part, is due to constraints available from the disdrometer data. The applicability of the suggested approach is illustrated for two characteristic events observed at the TWP Darwin site during the wet season of 2007. A future deployment of W-band radars at the DOE tropical Climate Research Facilities can improve CLWP estimation accuracies and provide retrievals for a wider range of stratiform precipitating cloud events

BootBandit: A macOS bootloader attack

Historically, the boot phase on personal computers left systems in a relatively vulnerable state. Because traditional antivirus software runs within the operating system, the boot environment is difficult to protect from malware. Examples of attacks against bootloaders include so‐called “evil maid” attacks, in which an intruder physically obtains a boot disk to install malicious software for obtaining the password used to encrypt a disk. The password then must be stored and retrieved again through physical access. In this paper, we discuss an attack that borrows concepts from the evil maid. We assume exploitation can be used to infect a bootloader on a system running macOS remotely to install code to steal the user\u27s password. We explore the ability to create a communication channel between the bootloader and the operating system to remotely steal the password for a disk protected by FileVault 2. On a macOS system, this attack has additional implications due to “password forwarding” technology, in which a user\u27s account password also serves as the FileVault password, enabling an additional attack surface through privilege escalation

A Game-Dynamic Model of Gas Transportation Routes and Its Application to the Turkish Gas Market [Updated November 2003]

The purpose of this paper is to study an optimal structure of a system of international gas pipelines competing for a gas market. We develop a game-dynamic model of the operation of several interacting gas pipeline projects with project owners acting as players in the game. The model treats the projects' commercialization times major players' controls. Current quantities of gas supply are modeled as approximations of Nash equilibrium points in instantaneous "gas supply games", in which each player maximizes his/her current net profit due to the sales of gas. We use the model to analyze the Turkish gas market, on which gas routes originating from Russia, Turkmenistan and Iran are competing. The analysis is carried out in three steps. At step 1, we model the operation of the pipelines as planned and estimate the associated profits. At step 2, we optimize individual projects, with respect to their profits, assuming that the other pipelines operate as planned. At step 3, we find numerical Nash equilibrium commercialization policies for the entire group of the pipelines. The simulations show the degrees to which the planned regimes are not optimal compared to the Nash equilibrium ones. Another observation is that in equilibrium regimes the pipelines are not always being run at their full capacities, which implies that the proposed pipeline capacities might not be optimal. The simulation results turn out to be moderately sensitive to changes in the discount rate and highly sensitive to changes in the price elasticity of gas demand

Planning water resource systems under uncertainty

Stationarity assumptions of linked human-water systems are frequently invalid given the difficult-to-predict changes affecting such systems. Population growth and development is fuelling rising water demand whilst in some parts of the world water supply is likely to decrease as a result of a changing climate. A combination of infrastructure expansion and demand management will be necessary to maintain the water supply/demand balance. The inherent uncertainty of future conditions is problematic when choosing a strategy to upgrade system capacity. Additionally, changing stakeholder priorities mean multi-criteria planning methods are increasingly relevant. Various modelling-assisted approaches are available to help the water supply planning process. This thesis investigates three state-of-the-art multi-criteria water source systems planning approaches. The first two approaches seek robust rather than optimal solutions; they both use scenario simulation to test the system plans under different plausible versions of the future. Under Robust Decision Making (RDM) alternative strategies are simulated under a wide range of plausible future scenarios and regret analysis is used to select an initial preferred strategy. Statistical cluster analysis identifies causes of system failure enabling further plan improvement. Info-Gap Decision Theory tests the proposed strategies under plausible conditions that progressively deviate from the expected future scenario. Decision makers then use robustness plots to determine how much uncertain parameters can deviate from their expected value before the strategies fail. The third approach links a water resource management simulator and a many-objective evolutionary search algorithm to reveal key trade-offs between performance objectives. The analysis shows that many-objective evolutionary optimisation coupled with state-of-the art visual analytics helps planners assess the best (approximately Pareto-optimal) plans and their inherent trade-offs. The alternative plans are evaluated using performance measures that minimise costs and energy use whilst maximising engineering and environmental performance criteria subject to basic supply reliability constraints set by regulators. The analyses show that RDM and Info-Gap are computationally burdensome but are able to consider a small number of candidate solutions in detail uncovering the solutions’ vulnerabilities in the face of uncertainty in future conditions while the multi-objective optimisation approach is able to consider many more possible portfolios and allow decision makers to visualize the trade-offs between performance metrics

Screening robust water infrastructure investments and their trade-offs under global change: A London example

We propose an approach for screening future infrastructure and demand management investments for large water supply systems subject to uncertain future conditions. The approach is demonstrated using the London water supply system. Promising portfolios of interventions (e.g., new supplies, water conservation schemes, etc.) that meet London’s estimated water supply demands in 2035 are shown to face significant trade-offs between financial, engineering and environmental measures of performance. Robust portfolios are identified by contrasting the multi-objective results attained for (1) historically observed baseline conditions versus (2) future global change scenarios. An ensemble of global change scenarios is computed using climate change impacted hydrological flows, plausible water demands, environmentally motivated abstraction reductions, and future energy prices. The proposed multi-scenario trade-off analysis screens for robust investments that provide benefits over a wide range of futures, including those with little change. Our results suggest that 60 percent of intervention portfolios identified as Pareto optimal under historical conditions would fail under future scenarios considered relevant by stakeholders. Those that are able to maintain good performance under historical conditions can no longer be considered to perform optimally under future scenarios. The individual investment options differ significantly in their ability to cope with varying conditions. Visualizing the individual infrastructure and demand management interventions implemented in the Pareto optimal portfolios in multi-dimensional space aids the exploration of how the interventions affect the robustness and performance of the system

Trade-off informed adaptive and robust real options water resources planning

Planning water resource systems is challenged primarily by two realities. First, uncertainty is inherent in the predictions of future supplies and demands due for example to hydrological variability and climate change. To build societal resilience water planners should seek to enhance the adaptability and robustness of water resource system interventions. Second, water resource developments typically involve competing interests which implies considering the trade-offs and synergies implied by the highest performing combinations of development options is useful. This work describes a real options based planning framework that generates adaptive and robust water system design alternatives able to consider and trade-off different goals. The framework can address different types of uncertainties and suggests the highest performing designs across multiple evaluation criteria, such as financial costs and water supply service performance metrics. Using a global city's water resource and supply system as a demonstration of the approach, we explore the trade-offs between a long-term water management plan's infrastructure services (service resilience, reliability, vulnerability) and its financial costs under supply and demand uncertainty. The set of trade-off solutions consist of different investment plans which are adaptive and robust to future changing conditions. Results show that the highest performing plans lower net present value (NPV) of needed investments by up to 18%, while maintaining similar performance across the other objectives. The real option value of delaying investments as much as possible approaches up to 14% of total NPV