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.

Harmonization of Life Cycle Climate Performance and Its Improvements for Heat Pump Applications

Life Cycle Climate Performance (LCCP) is an evaluation method by which heating, ventilation, air conditioning and refrigeration systems can be evaluated for their global warming impact over the course of their complete life cycle. LCCP is more inclusive than previous metrics such as Total Equivalent Warming Impact. It is calculated as the sum of direct and indirect emissions generated over the lifetime of the system “from cradle to grave”. Direct emissions include all effects from the release of refrigerants into the atmosphere during the lifetime of the system. This includes annual leakage and losses during the disposal of the unit. The indirect emissions include emissions from the energy consumption during manufacturing process, lifetime operation, and disposal of the system. This thesis proposes a standardized approach to the use of LCCP and traceable data sources for all aspects of the calculation. An equation is proposed that unifies the efforts of previous researchers. Data sources are recommended for average values for all LCCP inputs. A residential heat pump sample problem is presented illustrating the methodology. The heat pump is evaluated at five U.S. locations in different climate zones. An excel tool was developed for residential heat pumps using the proposed method. The primary factor in the LCCP calculation is the energy consumption of the system. The effects of advanced vapor compression cycles are then investigated for heat pump applications. Advanced cycle options attempt to reduce the energy consumption in various ways. There are three categories of advanced cycle options: subcooling cycles, expansion loss recovery cycles and multi-stage cycles. The cycles selected for research are the suction line heat exchanger cycle, the expander cycle, the ejector cycle, and the vapor injection cycle. The cycles are modeled using Engineering Equation Solver and the results are applied to the LCCP methodology. The expander cycle, ejector cycle and vapor injection cycle are effective in reducing LCCP of a residential heat pump by 5.6%, 8.2% and 10.5%, respectively in Phoenix, AZ. The advanced cycles are evaluated with the use of low GWP refrigerants and are capable of reducing the LCCP of a residential heat by 13.7%, 16.3% and 18.6% using a refrigerant with a GWP of 10. To meet the U.S. Department of Energy’s goal of reducing residential energy use by 40% by 2025 with a proportional reduction in all other categories of residential energy consumption, a reduction in the energy consumption of a residential heat pump of 34.8% with a refrigerant GWP of 10 for Phoenix, AZ is necessary. A combination of advanced cycle, control options and low GWP refrigerants are necessary to meet this goal.

Language-based Techniques for Practical and Trustworthy Secure Multi-party Computations

Secure Multi-party Computation (MPC) enables a set of parties to collaboratively compute, using cryptographic protocols, a function over their private data in a way that the participants do not see each other's data, they only see the final output. Typical MPC examples include statistical computations over joint private data, private set intersection, and auctions. While these applications are examples of monolithic MPC, richer MPC applications move between "normal" (i.e., per-party local) and "secure" (i.e., joint, multi-party secure) modes repeatedly, resulting overall in mixed-mode computations. For example, we might use MPC to implement the role of the dealer in a game of mental poker -- the game will be divided into rounds of local decision-making (e.g. bidding) and joint interaction (e.g. dealing). Mixed-mode computations are also used to improve performance over monolithic secure computations. Starting with the Fairplay project, several MPC frameworks have been proposed in the last decade to help programmers write MPC applications in a high-level language, while the toolchain manages the low-level details. However, these frameworks are either not expressive enough to allow writing mixed-mode applications or lack formal specification, and reasoning capabilities, thereby diminishing the parties' trust in such tools, and the programs written using them. Furthermore, none of the frameworks provides a verified toolchain to run the MPC programs, leaving the potential of security holes that can compromise the privacy of parties' data. This dissertation presents language-based techniques to make MPC more practical and trustworthy. First, it presents the design and implementation of a new MPC Domain Specific Language, called Wysteria, for writing rich mixed-mode MPC applications. Wysteria provides several benefits over previous languages, including a conceptual single thread of control, generic support for more than two parties, high-level abstractions for secret shares, and a fully formalized type system and operational semantics. Using Wysteria, we have implemented several MPC applications, including, for the first time, a card dealing application. The dissertation next presents Wys*, an embedding of Wysteria in F*, a full-featured verification oriented programming language. Wys* improves on Wysteria along three lines: (a) It enables programmers to formally verify the correctness and security properties of their programs. As far as we know, Wys* is the first language to provide verification capabilities for MPC programs. (b) It provides a partially verified toolchain to run MPC programs, and finally (c) It enables the MPC programs to use, with no extra effort, standard language constructs from the host language F*, thereby making it more usable and scalable. Finally, the dissertation develops static analyses that help optimize monolithic MPC programs into mixed-mode MPC programs, while providing similar privacy guarantees as the monolithic versions.