The China Meteorological Forcing Dataset (CMFD) is the first high spatial-temporal resolution gridded near-surface meteorological dataset developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis datasets and in-situ station data. Its record begins in January 1979 and is ongoing (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in the CMFD, including 2-meter air temperature, surface pressure, and specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate. Validations against observations measured at independent stations show that the CMFD is of superior quality than the GLDAS (Global Land Data Assimilation System); this is because a larger number of stations are used to generate the CMFD than are utilised in the GLDAS. Due to its continuous temporal coverage and consistent quality, the CMFD is one of the most widely-used climate datasets for China.

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The objective of this study is to evaluate two satellite rainfall products Global Precipitation Measurement Integrated MultisatellitE Retrievals and Tropical Rainfall Measuring Mission 3B42V7 (GPM IMERG and TRMM 3B42V7) in southern Tibetan Plateau region, with special focus on the dependence of products' performance on topography and rainfall intensity. Over 500 in situ rain gauges constitute an unprecedentedly dense rain gauge network over this region and provide an exceptional resource for ground validation of satellite rainfall estimates. Our evaluation centers on the rainy season from May to October in 2014. Results indicate that (1) GPM product outperforms TRMM at all spatial scales and elevation ranges in detecting daily rainfall accumulation; (2) rainfall accumulation over the entire rainy season is negatively correlated with mean elevation for rain gauges and the two satellite rainfall products, while the performance of TRMM also significantly correlates with topographic variations; (3) in terms of the ability of rainfall detection, false alarming ratio of TRMM (21%) is larger than that of GPM (14%), while missing ratio of GPM (13%) is larger than that of TRMM (9%). GPM tends to underestimate the amount of light rain events of 0–1 mm/d, while the opposite (overestimation) is true for TRMM. GPM shows better detecting ability for light rainfall (0–5 mm/d) events but there is no detection skill for both GPM and TRMM at high‐elevation (>4500 m) regions. Our results not only highlight the superiority of GPM to TRMM in southern Tibetan Plateau region but also recommend that further improvement on the rainfall retrieval algorithm is needed by considering topographical influences for both GPM and TRMM rainfall products.

Dimethacrylate monomers are commonly used as the organic phase of dental restorative materials but many questions remain about the underlying kinetics and network formation in these highly crosslinked photopolymer systems. Several novel experimental and modeling techniques that have been developed for other multifunctional (meth)acrylates were utilized to gain further insight into these resin systems. Specifically, this work investigates the copolymerization behavior of bis-GMA (2,2-bis[p-(2-hydroxy-3-methacryloxyprop-1oxy)-phenyl]propane) and TEGDMA (triethylene glycol dimethacrylate), two monomers typically used for dental resin formulations. Near-infrared spectroscopy, electron paramagnetic resonance spectroscopy, as well as dynamic mechanical and dielectric analysis were used to characterize the kinetics, radical populations, and structural properties of this copolymer system. In addition, a kinetic model is described that provides valuable information about the network evolution during the formation of this crosslinked polymer. The results of these numerous studies illustrate that all of the aforementioned techniques can be readily applied to dental resin systems and consequently can be used to obtain a wealth of information about these systems. The application of these techniques provides insight into the complex polymerization kinetics and corresponding network formation, and as a result, a more complete understanding of the anomolous behaviors exhibited by these systems, such as diffusion controlled kinetics and conversion dependent network formation, is attained.

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When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM) model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1) the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2) the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3) to the authors' best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection.

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