Earth’s albedo and its symmetry

Abstract The properties of Earth's albedo and its symmetries are analyzed using twenty years of space-based Energy Balanced And Filled product of Clouds and the Earth's Radiant Energy System measurements. Despite surface asymmetries, top of the atmosphere temporally & hemispherically averaged reflected solar irradiance R appears symmetric over Northern/Southern hemispheres. This is confirmed with the use of surrogate time-series, which provides margins of 0.1±0.28Wm?2 for possible hemispheric differences supported by Clouds and Earth's Radiant System data. R time-series are further analyzed by decomposition into a seasonal (yearly and half yearly) cycle and residuals. Variability in the reflected solar irradiance is almost entirely (99%) due to the seasonal variations, mostly due to seasonal variations in insolation. The residuals of hemispherically averaged R are not only small, but also indistinguishable from noise, and thus not correlated across hemispheres. This makes yearly and sub-yearly timescales unlikely as the basis for a symmetry-establishing mechanism. The residuals however contain a global trend that is large, as compared to expected albedo feedbacks. It is also hemispherically symmetric, and thus indicates the possibility of a symmetry enforcing mechanism at longer timescales. To pinpoint precisely which parts of the Earth system establish the hemispheric symmetry, we create an energetically consistent cloud-albedo field from the data. We show that the surface albedo asymmetry is compensated by asymmetries between clouds over extra-tropical oceans, with southern hemispheric storm-tracks being 11% cloudier than their northern hemisphere counterparts. This again indicates that, assuming the albedo symmetry is not a result of chance, its mechanism likely operates on large temporal and spatial scales

Machine Learning and System Identification for Estimation in Physical Systems

In this thesis, we draw inspiration from both classical system identification and modern machine learning in order to solve estimation problems for real-world, physical systems. The main approach to estimation and learning adopted is optimization based. Concepts such as regularization will be utilized for encoding of prior knowledge and basis-function expansions will be used to add nonlinear modeling power while keeping data requirements practical.The thesis covers a wide range of applications, many inspired by applications within robotics, but also extending outside this already wide field.Usage of the proposed methods and algorithms are in many cases illustrated in the real-world applications that motivated the research.Topics covered include dynamics modeling and estimation, model-based reinforcement learning, spectral estimation, friction modeling and state estimation and calibration in robotic machining.In the work on modeling and identification of dynamics, we develop regularization strategies that allow us to incorporate prior domain knowledge into flexible, overparameterized models. We make use of classical control theory to gain insight into training and regularization while using tools from modern deep learning. A particular focus of the work is to allow use of modern methods in scenarios where gathering data is associated with a high cost.In the robotics-inspired parts of the thesis, we develop methods that are practically motivated and make sure that they are implementable also outside the research setting. We demonstrate this by performing experiments in realistic settings and providing open-source implementations of all proposed methods and algorithms

Linear Parameter-Varying Spectral Decomposition

A linear parameter-varying (LPV) spectral decomposition method, based on least-squares estimation and kernel expansions, is developed. Statistical properties of the estimator are analyzed and verified in simulations. The method is linear in the parameters, applicable to both the analysis and modeling problems and is demonstrated on both simulated signals as well as measurements of the torque in an electrical motor

LPVSpectral.jl : A toolbox for least-squares spectral estimation and LPV spectral decomposition.

An implementation of the spectral estimation method presented in Bagge Carlson et al. "Linear Parameter-Varying Spectral Decomposition." 2017 American Control Conference