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.

Nested Sampling and its Applications in Stable Compressive Covariance Estimation and Phase Retrieval with Near-Minimal Measurements

Compressed covariance sensing using quadratic samplers is gaining increasing interest in recent literature. Covariance matrix often plays the role of a sufficient statistic in many signal and information processing tasks. However, owing to the large dimension of the data, it may become necessary to obtain a compressed sketch of the high dimensional covariance matrix to reduce the associated storage and communication costs. Nested sampling has been proposed in the past as an efficient sub-Nyquist sampling strategy that enables perfect reconstruction of the autocorrelation sequence of Wide-Sense Stationary (WSS) signals, as though it was sampled at the Nyquist rate. The key idea behind nested sampling is to exploit properties of the difference set that naturally arises in quadratic measurement model associated with covariance compression. In this thesis, we will focus on developing novel versions of nested sampling for low rank Toeplitz covariance estimation, and phase retrieval, where the latter problem finds many applications in high resolution optical imaging, X-ray crystallography and molecular imaging. The problem of low rank compressive Toeplitz covariance estimation is first shown to be fundamentally related to that of line spectrum recovery. In absence if noise, this connection can be exploited to develop a particular kind of sampler called the Generalized Nested Sampler (GNS), that can achieve optimal compression rates. In presence of bounded noise, we develop a regularization-free algorithm that provably leads to stable recovery of the high dimensional Toeplitz matrix from its order-wise minimal sketch acquired using a GNS. Contrary to existing TV-norm and nuclear norm based reconstruction algorithms, our technique does not use any tuning parameters, which can be of great practical value. The idea of nested sampling idea also finds a surprising use in the problem of phase retrieval, which has been of great interest in recent times for its convex formulation via PhaseLift, By using another modified version of nested sampling, namely the Partial Nested Fourier Sampler (PNFS), we show that with probability one, it is possible to achieve a certain conjectured lower bound on the necessary measurement size. Moreover, for sparse data, an l1 minimization based algorithm is proposed that can lead to stable phase retrieval using order-wise minimal number of 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.