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Reanalysis data play a vital role in weather and climate study as well as meteorological resource development and application. In this work, the East Asia Reanalysis System (EARS) was developed using the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolations (GSI) data assimilation system. The regional reanalysis system is forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis ERA-Interim data at 6 h intervals. Hourly surface observations are assimilated by the Four-Dimension Data Assimilation (FDDA) scheme during the WRF model integration; upper observations are assimilated in three-dimensional variational data assimilation (3D-VAR) mode at the analysis moment. It should be highlighted that many of the assimilated observations have not been used in other reanalysis systems. The reanalysis runs from 1980 to 2018, producing a regional reanalysis dataset covering East Asia and surrounding areas at 12 km horizontal resolution, 74 sigma levels, and 3 h intervals. Finally, an evaluation of EARS has been performed with respect to the root mean square error (RMSE), based on the 10-year (2008–2017) observational data. Compared to the global reanalysis data of ERA-Interim, the regional reanalysis data of EARS are closer to the observations in terms of RMSE in both surface and upper-level fields. The present study provides evidence for substantial improvements seen in EARS compared to the ERA-Interim reanalysis fields over East Asia. The study also demonstrates the potential use of the EARS data for applications over East Asia and proposes further plans to provide the latest reanalysis in real-time operation mode. Simple data and updated information are available on Zenodo at https://doi.org/10.5281/zenodo.7404918 (Yin et al., 2022), and the full datasets are publicly accessible on the Data-as-a-Service platform of the China Meteorological Administration (CMA) at http://data.cma.cn (last access: 19 May 2023).

The morphological, physicochemical, and biochemical properties of soluble and insoluble dietary fiber from seven types of algae were investigated. The soluble dietary fiber (SDF) contents (6.48 to 60.90% of the total fiber) in most of the investigated algae were significantly lower than the insoluble dietary fiber (IDF) contents (39.10 to 93.52% of the total fiber). It can be inferred from the infrared and UV-Vis spectra that the SDF and IDF of algae may contain cellulose, hemicellulose, various monosaccharides, phenolic compounds, and quinone pigments. The bound phenolic in the seven algae varied widely in contents (3.76 to 14.08 mg GAE/g in IDF and 1.94 to 8.61 mg GAE/g in SDF), whose antioxidant activities in the IDF were stronger than those in SDF because of different phenolic compositions. The HPLC-mass spectrometry (MS)/MS results showed that the IDF may contain methyl-8α-hydroxy-grindelate-7β-O-7'β-ether hydrate, hydroxydecanoic acid, and malyngic acid. PRACTICAL APPLICATION: Polysaccharides of high content in algae cannot be digested by humans, hence regarded as dietary fibers. A large amount of bound phenolic compounds in dietary fibers can add to the biological activities of dietary fibers. These topics are important to the development of seaweed-based functional foods.

Hard interaction learning between source sequences and their next targets is challenging, which exists in a myriad of sequential prediction tasks. During the training process, most existing methods focus on explicitly hard interactions caused by wrong responses. However, a model might conduct correct responses by capturing a subset of learnable patterns, which results in implicitly hard interactions with some unlearned patterns. As such, its generalization performance is weakened. The problem gets more serious in sequential prediction due to the interference of substantial similar candidate targets. To this end, we propose a Hardness Aware Interaction Learning framework (HAIL) that mainly consists of two base sequential learning networks and mutual exclusivity distillation (MED). The base networks are initialized differently to learn distinctive view patterns, thus gaining different training experiences. The experiences in the form of the unlikelihood of correct responses are drawn from each other by MED, which provides mutual exclusivity knowledge to figure out implicitly hard interactions. Moreover, we deduce that the unlikelihood essentially introduces additional gradients to push the pattern learning of correct responses. Our framework can be easily extended to more peer base networks. Evaluation is conducted on four datasets covering cyber and physical spaces. The experimental results demonstrate that our framework outperforms several state-of-the-art methods in terms of top-k based metrics., 10 pages; 4 figures; Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM '21), November 1--5, 2021, Virtual Event, QLD, Australia

The South Asian High (SAH) has an important influence on atmospheric circulation and the Asian climate in summer. However, current comparative analyses of the SAH are mostly between reanalysis datasets and there is a lack of sounding data. We therefore compared the climatology, trends and abrupt changes in the SAH in the Japanese 55-year Reanalysis (JRA-55) dataset, the National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) dataset, the European Center for Medium-Range Weather Forecasts Reanalysis Interim (ERA-interim) dataset and radiosonde data from China using linear analysis and a sliding t-test. The trends in geopotential height in the control area of the SAH were positive in the JRA-55, NCEP-CFSR and ERA-interim datasets, but negative in the radiosonde data in the time period 1979–2014. The negative trends for the SAH were significant at the 90% confidence level in the radiosonde data from May to September. The positive trends in the NCEP-CFSR dataset were significant at the 90% confidence level in May, July, August and September, but the positive trends in the JRA-55 and ERA-Interim were only significant at the 90% confidence level in September. The reasons for the differences in the trends of the SAH between the radiosonde data and the three reanalysis datasets in the time period 1979–2014 were updates to the sounding systems, changes in instrumentation and improvements in the radiation correction method for calculations around the year 2000. We therefore analyzed the trends in the two time periods of 1979–2000 and 2001–2014 separately. From 1979 to 2000, the negative SAH trends in the radiosonde data mainly agreed with the negative trends in the NCEP-CFSR dataset, but were in contrast with the positive trends in the JRA-55 and ERA-Interim datasets. In 2001–2014, however, the trends in the SAH were positive in all four datasets and most of the trends in the radiosonde and NCEP-CFSR datasets were significant. It is therefore better to use the NCEP-CFSR dataset than the JRA-55 and ERA-Interim datasets when discussing trends in the SAH.

Although previous studies show that urbanization contributes to less than 10 % of the long-term regional total warming trend of mean surface air temperature in northeast China (Li et al. 2010), the urban heat island (UHI) impact on extreme temperatures could be more significant. This paper examines the urbanization impact on extreme winter minimum temperatures from 33 stations in North China during the period of 1957–2010. We use the Generalized Extreme Value (GEV) distribution to analyze the distribution of extreme minimum temperatures and the long-term variations of the three distributional characteristics parameters. Results suggest that among the three distribution parameters, the position parameter is the most representative in terms of the long-term extreme minimum temperature change. A new classification method based on the intercommunity (factors analysis method) of the temperature change is developed to detect the urbanization effect on winter extreme minimum temperatures in different cities. During the period of rapid urbanization (after 1980), the magnitude of variations of the three distribution parameters for the urban station group is larger than that for the reference station group, indicating a higher chance of occurrence of warmer weather and a larger fluctuation of temperatures. Among different types of cities, the three parameters of extreme minimum temperature distribution of the urban station group are, without exception, higher than those of the reference station group. The urbanization of different types of cities all show a warming effect, with small-size cities have the most evident effects on extreme minimum temperatures.