Function spaces from coherent continuous domains to RB-domains

In this paper, continuing the work of the first and third authors, we study the function spaces from coherent continuous domains to RB-domains. Firstly, we prove that the function spaces from coherent core compact spaces with compact open sets as a basis to bifinite domains (algebraic RB-domains) are algebraic. As an application, we give an example which shows that a function space from a coherent quasi-algebraic domain to a finite domain might not be coherent. Finally, we show that the function space from a coherent continuous domain to an RB-domain is an RB-domain. Particularly, a function space from an FS-domain (introduced by Achim Jung) to an RB-domain is an RB-domain. This addresses an old open problem of whether FS-domains are RB-domains

Assimilation of Remotely Sensed Soil Moisture in the MESH Model

Soil moisture information is critically important to weather, climate, and hydrology forecasts since the wetness of the land strongly affects the partitioning of energy and water at the land surface. Spatially distributed soil moisture information, especially at regional, continental, and global scales, is difficult to obtain from ground-based (in situ) measurements, which are typically based upon sparse point sources in practice. Satellite microwave remote sensing can provide large-scale monitoring of surface soil moisture because microwave measurements respond to changes in the surface soil’s dielectric properties, which are strongly controlled by soil water content. With recent advances in satellite microwave soil moisture estimation, in particular the launch of the Soil Moisture and Ocean Salinity (SMOS) satellite and the Soil Moisture Active Passive (SMAP) mission, there is an increased demand for exploiting the potential of satellite microwave soil moisture observations to improve the predictive capability of hydrologic and land surface models. In this work, an Ensemble Kalman Filter (EnKF) scheme is designed for assimilating satellite soil moisture into a land surface-hydrological model, Environment Canada’s standalone MESH to improve simulations of soil moisture. After validating the established assimilation scheme through an observing system simulation experiment (synthetic experiment), this study explores for the first time the assimilation of soil moisture retrievals, derived from SMOS, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), in the MESH model over the Great Lakes basin. A priori rescaling on satellite retrievals (separately for each sensor) is performed by matching their cumulative distribution function (CDF) to the model surface soil moisture’s CDF, in order to reduce the satellite-model bias (systematic error) in the assimilation system that is based upon the hypothesis of unbiased errors in model and observation. The satellite retrievals, the open-loop model soil moisture (no assimilation) and the assimilation estimates are, respectively, validated against point-scale in situ soil moisture measurements in terms of the daily-spaced time series correlation coefficient (skill R). Results show that assimilating either L-band retrievals (SMOS) or X-band retrievals (AMSR-E/AMSR2) can favorably influence the model soil moisture skill for both surface and root zone soil layers except for the cases with a small observation (retrieval) skill and a large open-loop skill. The skill improvement ΔRA-M, defined as the skill for the assimilation soil moisture product minus the skill for the open-loop estimates, typically increases with the retrieval skill and decreases with increased open-loop skill, showing a strong dependence upon ΔRS-M, defined as the retrieval skill minus the model (open-loop) surface soil moisture skill. The SMOS assimilation reveals that the cropped areas typically experience large ΔRA-M, consistent with a high satellite observation skill and a low open-loop skill, while ΔRA-M is usually weak or even negative for the forest-dominated grids due to the presence of a low retrieval skill and a high open-loop skill. The assimilation of L-band retrievals (SMOS) typically results in greater ΔRA-M than the assimilation of X-band products (AMSR-E/AMSR2), although the sensitivity of the assimilation to the satellite retrieval capability may become progressively weaker as the open-loop skill increases. The joint assimilation of L-band and X-band retrievals does not necessarily yield the best skill improvement. As compared to previous studies, the primary contributions of this thesis are as follows. (i) This work examined the potential of latest satellite soil moisture products (SMOS and AMSR2), through data assimilation, to improve soil moisture model estimates. (ii) This work, by taking advantage of the ability of SMOS to estimate surface soil moisture underneath different vegetation types, revealed the vegetation cover modulation of satellite soil moisture assimilation. (iii) The assimilation of L-band retrievals (SMOS) was compared with the assimilation of X-band retrievals (AMSR-E/AMSR2), providing new insight into the dependence of the assimilation upon satellite retrieval capability. (iv) The influence of satellite-model skill difference ΔRS-M on skill improvement ΔRA-M was consistently demonstrated through assimilating soil moisture retrievals derived from radiometers operating at different microwave frequencies, different vegetation cover types, and different retrieval algorithms

QoS-guaranteed resource provisioning for cloud-based MapReduce

This PhD project has investigated how to guarantee the quality of MapReduce services in cloud computing while minimizing the operational cost of the MapReduce services through dynamic resource provisioning. In this PhD project, a framework for the dynamic resource provisioning has been developed. Meanwhile, theoretical results for the dynamic resource provisioning have been derived, and a set of efficient and effective algorithms used in the framework have been proposed

Monitoring and Source Tracing of Machining Error Based on Built-in Sensor Signal

AbstractOnline monitoring and source tracing of machining error is of great significance for ensuring machining quality and improving machining efficiency. For an open numerical controller, the built-in sensors signals can be captured through driver interface in machining process. These signals contain various information of machining conditions of machine tool. The capture and analysis of the built-in sensors signals can be used for the online monitoring and source tracing of machining error. In this paper, an novel approach is developed for machining error monitoring and source tracing based on built-in sensor signal analysis and multi-body system theory. A ball screw grinding process was monitored, and the analysis results show the validity of the approach