Use of Land Surface Temperature to Estimate Surface Energy Fluxes: Contributions of Wilfried Brutsaert and Collaborators

Land surface temperature (LST) plays a key role in governing the land surface energy budget, and measurements or estimates of LST are an integral part of many land surface models and methods to estimate land surface sensible heat (H) and latent heat fluxes. In particular, the LST anchors the potential temperature profile in Monin-Obukhov similarity theory, from which H can be derived. Brutsaert has made important contributions to our understanding the nature of surface temperature measurements as well as the practical but theoretically sound use of LST in this framework. His work has coincided with the wide-spread availability of remotely sensed LST measurements. Use of remotely sensed LST estimates inevitably involves complicating factors, such as: varying spatial and temporal scales in measurements, theory, and models; spatial variability of LST and H; the relationship between measurements of LST and the temperature felt by the atmosphere; and the need to correct satellite-based radiometric LST measurements for the radiative effects of the atmosphere. This paper reviews the progress made in research in these areas by tracing and commenting on Brutsaert's contributions.

Development of Dynamic Constraint Models for a Model Based Transient Calibration Process

Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control. Parameters for this second order system have been extracted from real transient data. The model has been shown to be the best choice relative to a list of appropriate candidates such as neural networks and first order models. The selected second order model was used in conjunction with transient emission models to predict emissions over the FTP cycle. It has been shown that emission predictions based on air-handing parameters predicted by the dynamic constraint model do not differ significantly from corresponding emissions based on measured air-handling parameters.