A transformative approach to enhance the parameter information from microwave and infrared remote sensing measurements

In observational science,data is the foundation of a scientific model;satellite-derived parameters serve as data for earth sciences models.The building of science is imprecise if data is ambiguous.Remote sensing‘big data’provides a wealth of information for unlocking the mysteries of earth sciences.The parameter estimation from remote sensing measurements is extremely ill-posed and the inverse method plays a significant role in extracting parameter information.In this paper,predominant stochastic inverse methods in satellite retrieval applications are critically investigated from different schools of thought and several basic flaws are revealed,e.g.error being treated as definite information.The major drawbacks of these methods include a high reliance on a priori information and binding the satellite retrievals to in situ measurements.A fundamentally different and transformative approach is explored as an alternative.A rational,reliable,and repeatable determination of geophysical parameter values from remote sensing measurements is possible using the total least squares based deterministic inverse method.It is a physical model-based data-driven optimization,where the error quantity is extracted from the problem itself for regularization on a case-by-case basis using singular vector decomposition of the augmented function of the Jacobian and the residual.By moving from the prevalent to the proposed inverse method,a paradigm shift in results from“information loss”to‘information gain’is achieved.

Multi-objective optimization in quantum parameter estimation

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved,it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives:(1) maximizing the Fisher information, improving the parameter estimation precision, and(2)minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ε-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.