Characteristics of urban heat island (UHI) in a high latitude coastal city - a case study of Turku, SW Finland

The term urban heat island (UHI) refers to the common situation in which the city is warmer than its rural surroundings. In this dissertation, the local climate, and especially the UHI, of the coastal city of Turku (182,000 inh.), SW Finland, was studied in different spatial and temporal scales. The crucial aim was to sort out the urban, topographical and water body impact on temperatures at different seasons and times of the day. In addition, the impact of weather on spatiotemporal temperature differences was studied. The relative importance of environmental factors was estimated with different modelling approaches and a large number of explanatory variables with various spatial scales. The city centre is the warmest place in the Turku area. Temperature excess relative to the coldest sites, i.e. rural areas about 10 kilometers to the NE from the centre, is on average 2 °C. Occasionally, the UHI intensity can be even 10 °C. The UHI does not prevail continuously in the Turku area, but occasionally the city centre can be colder than its surroundings. Then the term urban cool island or urban cold island (UCI) is used. The UCI is most common in daytime in spring and in summer, whereas during winter the UHI prevails throughout the day. On average, the spatial temperature differences are largest in summer, whereas the single extreme values are often observed in winter. The seasonally varying sea temperature causes the shift of relatively warm areas towards the coast in autumn and inland in spring. In the long term, urban land use was concluded to be the most important factor causing spatial temperature differences in the Turku area. The impact was mainly a warming one. The impact of water bodies was emphasised in spring and autumn, when the water temperature was relatively cold and warm, respectively. The impact of topography was on average the weakest, and was seen mainly in proneness of relatively low-lying places for cold air drainage during night-time. During inversions, however, the impact of topography was emphasised, occasionally outperforming those of urban land use and water bodies.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Study of Control Methods of a High-Frequency Cycloconverter for Domestic Induction Heating Appliances

A high-frequency cyclonverter acts as a direct ac-to-ac power converter circuit that does not require a diode bidge rectifier. Bridgeless topology makes it possible to remove forward voltage drop losses that are present in a diode bridge. In addition, the on-state losses can be reduced to 1.5 times the on-state resistance of switches in half-bridge operation of the cycloconverter. A high-frequency cycloconverter is reviewed and the charging effect of the dc-capacitors in ``back-to-back'' or synchronous mode operation operation is analyzed. In addition, a control method is introduced for regulating dc-voltage of the ac-side capacitors in synchronous operation mode. The controller regulates the dc-capacitors and prevents switches from reaching overvoltage level. This can be accomplished by variating phase-shift between the upper and the lower gate signals. By adding phase-shift between the gate signal pairs, the charge stored in the energy storage capacitors can be discharged through the resonant load and substantially, the output resonant current amplitude can be improved. The above goals are analyzed and illustrated with simulation. Theory is supported with practical measurements where the proposed control method is implemented in an FPGA device and tested with a high-frequency cycloconverter using super-junction power MOSFETs as switching devices.