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With the large-scale grid connected operation of new energy power plants, the safe operation of grid and new energy power plants has been faced with unprecedented challenges, especially the influence of meteorological conditions. Due to the inherent volatility of new energy, accurate prediction of generation power can effectively reduce the impact on the electricity grid system. In order to improve the prediction accuracy, a dynamic meteorological element correction method considering multiple meteorological elements is introduced, and a centralized power forecasting platform is established. At the same time, due to the frequent occurrence of extreme weather events in recent years, such as wind fan icing, lightning gale, hail, mountain torrents and so on, all of them have brought challenges to the safe operation of new energy power plants, so a fan icing warning model has been developed. Based on the Internet automatic published information plantform of meteorological disaster has established which greatly reduced the loss of power plants caused by the extreme weather.

Daily maximum temperatures from 753 stations across China and the heat wave indicators are used to study the temporal and spatial characteristics of heat wave intensity, frequency and heat wave days in China over the period of 1961–2010. The results show that high frequency, long duration and strong intensity of heat waves occurred in the Jianghuai area, Jiangnan area, and eastern Sichuan Basin. The highest frequency and the longest duration are located in northern Jiangxi and northern Zhejiang provinces, and the highest intensity in northern Zhejiang province is even more prominent. The frequency, heat wave days and intensity showed a general increasing trend in the past 50 years, while decadal characteristics are also observed with a decreasing trend from the 1960s to the early 1980s and increasing trend from the end of the 1980s to 2010. The regional variations demonstrate a significant increasing trend in the northern and western parts of North China, central-northern part of Northwest China, the central part of South China, the Yangtze River Delta and the southern Sichuan Basin, with an obvious decreasing trend in the southern Huanghuai area, northern Jianghuai area and Hanjiang River Basin. Citation Ye, D.-X., Yin, J.-F.,Chen, Z.-H., et al., 2014. Spatial and temporal variations of heat waves in China from 1961 to 2010. Adv. Clim. Change Res. 5(2), doi: 10.3724/SP.J.1248.2014.066.

In Central China, the obvious climate change has happened along with global warming. Based on the observational analysis, the climate change has significant effects, both positive and negative, in every field within the study area, and with the harmful effects far more prevalent. Under the scenario A1B, it is reported that temperature, precipitation, days of heat waves and extreme precipitation intensity will increase at respective rates of 0.38°C per decade, 12.6 mm per decade, 6.4 d and 47 mm per decade in the 21st century. It is widely believed that these climate changes in the future will result in some apparent impacts on agro-ecosystems, water resources, wetland ecosystem, forest ecosystem, human health, energy sectors and other sensitive fields in Central China. Due to the limited scientific knowledge and researches, there are still some shortages in the climate change assessment methodologies and many uncertainties in the climate prediction results. Therefore, it is urgent and essential to increase the studies of the regional climate change adaptation, extend the research fields, and enhance the studies in the extreme weather and climate events to reduce the uncertainties of the climate change assessments. Ren, Y.-J., J.-X. Cui, S.-Q. Wan, et al., 2013: Climate change impacts on central China and adaptation measures. Adv . Clim . Change Res ., 4 (4), doi: 10.3724/SP.J.1248.2013.215.

In order to improve the prediction accuracy of the photovoltaic power prediction model, this paper presents a novel forecasting model, which is the combination between Principal Component Analysis (PCA) and Support Vector Machine (SVM) intelligent algorithm. PCA statistical method used to extract less principal components instead of the original meteorological factor. Then that is as the input of the SVM model. The proposed prediction model verify its validity by the data of a roof grid-connected PV system located at Huazhong University of Science and Technology. The results show that the forecasting model can not only improve the prediction accuracy of the photovoltaic power output also reduce the computational model to meet real-time requirements for forecasting. The prediction of the accuracy is better than that of the currently used photovoltaic power forecast by comparing other model.

Shuangliu xuan shiwen ji 双柳轩诗文集 (Anthology of sanctum with two willows) was an anthology of some early works of Yuan Mei (1716−1798), an eminent writer of the Qing dynasty, and had been published before he got famous. Taking the enormous differences found during collation of this anthology with Yuan Mei quan ji 袁枚全集 (Corpus of Yuan Mei), a contemporary collated edition of Xiaocangshan fang quan ji 小仓山房全集 (Complete works of Xiaocangshan villa) compiled in Yuan Mei’s senectitude, as an example, this paper is trying to reveal that a work may go through continuous revisions or even recomposition before they are finalized and taken into a corpus. Like burying a body, every compilation of one’s work is a layer on the first-hand work, and the latest layer is no doubt the farthest from the body. Thus the author suggests that researches attempting to trace diachronies of a renowned writer’s works should dig deep, and if the body itself has lost into dust, at least the early compilations could bring us much closer to how the works used to be.

Change in first-flowering dates and its correlation with climatic factors was mainly analyzed, and several predicting models for the first-flowering dates were established based on the flowering dates of Japanese Cherry Blossoms ( P. yedoensis Mats.) during 1947–2008 on campus of Wuhan University and the climatic data in the same period. The results show that: 1) in 1947–2000, the first-flowering dates advanced with 2.17 d per decade, with an overall trend of 11.72 d in the 54 years, the fading dates were postponed for only 1.83 d in the same period, and the florescence duration increased by 13.55 d; 2) the first-flowering dates have negative correlation with monthly average temperature from December to March. The increasing winter (from December to February) temperature is the main reason for the advancement of the first-flowering dates. Per 1°C increase in average temperature of February and wintertime makes the first-flowering dates advance by 1.66 d and 2.86 d, respectively; 3) some statistical models of the first-flowering dates were built up with average temperature of February and wintertime based on the data during 1947–2000, and they are detected independently during 2001–2008.

China Meteorological Administration (CMA) and the local Meteorological Bureaus, have set up Emergency Management Office and associated organizations for weather emergency in the Chinese inland. A suite of relatively comprehensive system for meteorological disasters and some emergency response programs have also been implemented by CMA. To illustrate how the mechanism operates, this paper presents two cases with one involving the Olympics weather emergency services in August 2008 and the other the low-temperature and snow disaster emergency response in Southern China in early 2008. I conclude that weather emergency management in China should follow the scientific emergency management principle, as well as the prevention-oriented and emergency rescue-combined principle.

With the development of cloud computing, the whole internet computing has entered an era of high-performance computing and application. Cloud computing makes it possible for resources to be transacted as goods, and for anyone at any time and any place to make use of the mighty power of cloud computing. As is known to all, the meteorological science involves large amount of data and high demand of calculation. In order to improve the accuracy and timeliness of meteorology indices forecasting computing in this paper, employing cloud computing technology in the high performance computing, a meteorological science data computing and process control framework based on hybrid clouds is put forward. And with the practical application, the usability, feasibility and expansibility of this framework has been proved. A large number of evaluation data has demonstrated that this framework can provide higher computational efficiency and prediction products with higher resolution time.

Epigenetic studies of blood lipid traits have identified genes underlying lipid metabolism in the general population. However, investigation of this association has not been conducted in survivors of childhood cancer, a population with a much higher burden of dyslipidemia and other cardiometabolic conditions due to exposures of genotoxic cancer therapies. We performed epigenome-wide association studies (EWAS) to identify blood DNA methylation (DNAm) 5’-cytosine-phosphate-guanine-3′ (CpGs) associated with lipid concentrations, including high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), and triglycerides (TG), for childhood cancer survivors from the St. Jude Lifetime (SJLIFE) cohort. DNAm was generated with blood derived DNA using Illumina MethylationEPIC BeadChip array. Adjusted M-value of DNAm for each CpG was calculated based on a linear regression of M-value against a set of covariates, including sex, age at DNA sampling, leukocyte subtype proportions, top four significant genetic principal components, and top four methylation principal components. In the exposure EWAS, a linear regression model was used for lipid levels prior to DNA sampling as an exposure variable and DNAm as an outcome variable, adjusting for cancer treatments, age at lipid measurement, BMI, cigarette smoking and lipid lowering medicine use. In the outcome EWAS, a linear regression model was used for DNAm as an exposure variable and lipid levels after DNA sampling as outcome variables, adjusting for cancer treatments, age at lipid measurement, BMI, smoking, lipid lowering medicine use, lipid levels measured at DNA sampling, age at DNA sampling, and polygenic risk score for specific lipid levels. Among survivors of European ancestry (N=2052), we identified 43 significant CpGs (P50) and lipid outcomes (106/111 with I2>50). Additionally, no overlap between CpGs associated with lipid exposures and lipid outcomes suggests that the DNAm levels of these CpGs could be either the cause or consequence of lipid levels. Examination of the EWAS catalog and recent literature, suggests that most of the lipid associated CpGs identified in our study are novel. Blood lipid associated CpGs may be epigenetic biomarkers for identification of survivors with higher risk of dyslipidemia and may inform potential drug targets for future interventions. Citation Format: Qian Dong, Nan Song, Cheng Chen, Zhenghong Li, John Easton, Heather Mulder, Jinghui Zhang, Geoffrey Neale, Emily Walker, I-Chan Huang, Kirsten K. Ness, Melissa M. Hudson, Leslie L. Robison, Zhaoming Wang. Epigenome-wide association study of blood lipids among survivors of childhood cancer in the St. Jude Lifetime Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3764.